## PAMPh2.0 — A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (wpiea2024148-print-pdf) ## Source details **Canonical URL:** [PAMPh2.0 — A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (wpiea2024148-print-pdf)](https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024148-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2024/english/wpiea2024148-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2024/english/wpiea2024148-print-pdf.pdf.json) --- ### Introduction: motivation, purpose, and operational milestones - Motivations for FPAS modernization: - Increase in complexity of operating environment for a small open economy vulnerable to sudden capital flow swings, exchange rate volatility, and global supply shocks. - Model purpose and design: - Core: move from a multi-equation econometric model (MEM) to a semi-structural Quarterly Projection Model (QPM) — Policy Analysis Model for the Philippines, version 2.0 (PAMPh2.0). - Features: forward-looking projections with endogenous monetary policy; inclusion of fiscal policy, macrofinancial linkages, labor dynamics, and FX interventions; modular structure for operational efficiency. - Inspiration and methods: extended QPM FINEX (Berg et al., 2023), Integrated Policy Framework (IPF), microfounded portfolio balance approach (Gabaix and Maggiori, 2015). - Operational milestones and dissemination: - IMF–BSP TA collaboration led by ICD began Spring 2022. - PAMPh2.0 features presented at the 61st Philippine Economic Society Annual Meeting and Conference on 7 November 2023. - TA delivered “conditional forecasting,” shadow (mock) forecasts, and scenario analysis. - Institutional aims: - Support interdepartmental coordination, assist policy deliberations via BSP Advisory Committee (AC) and Monetary Board, foster ownership through internal and external dissemination. ### Box 1 — Technical Assistance (multi‑phase modernization) - TA Phases (ICD–BSP DER collaboration): - Phase 1: April 2022 — review initial PAMPh and forecasting performance. - Phase 2: October 2022 — decomposition of aggregate demand, external, and fiscal sectors; meeting with Governor and Monetary Board. - Phase 3: March 2023 — calibration and storytelling capacity; systematic forecasting assessment; initial institutional change discussions. - Phase 4: July 2023 — credit cycle dynamics, macroprudential policy, capital flow management measures (CFMs). - Phase 5: February–March 2024 — labor block extension; addressed senior management concerns (model structure, market interest rate, policy rule calibration, supply shock treatment, inflation expectations); presentation to Monetary Board and AC covering shadow forecasts and alternative scenarios. ### PAMPh2.0 structure, conventions, and key modeling components - Core modeling conventions: - Variables in log form; decomposed into gap (hat) and trend (bar) components. - Gaps are percentage deviations; trends follow autoregressive processes. - Internal balance (real economy and inflation): - GDP decomposed by expenditure: Y = C + I + G + X − M with component price deflators P. - Consumption modeled via an Euler-type equation with external habit formation; channels include real interest rate gap r̂, real loan rate gap r̂L, risk premium gap p̂rdm, output gap ŷ, relative price gaps (exports/imports), remittances, real wage gap w r̂, taxes, credit shocks εIGR̄_h. - Labor and wage block: business wage Phillips curve, minimum wage dynamics, total wage index w = σ6 wM + (1−σ6) wB, real wage decomposition, Okun’s law linking unemployment gap û to lagged output gap. - Investment via Tobin‑Q: q̂ depends on expected output gap, expected portfolio investment ẑh, monetary conditions (r̂, r̂L), and risk premium p̂rdm. - CPI split into food, energy, and core components with New Keynesian open‑economy hybrid Phillips curves; real marginal costs rmc include output gap ŷ, REER gap, pass‑through from non‑core relative prices, and real wage gap w r̂. - Banking sector and credit channel: - Two credit categories: households (h) and firms (f); stock and flow distinction. - Outstanding credit evolves by survival, scaling with nominal potential GDP growth, new credit issuance, and shocks (Eq.26). - Newly issued credit gaps: households respond to consumption gap and real lending rate gap; firms respond to house price gap ẑh, lending rate gap, and loan‑to‑value constraints (Eqs.27–28). - Real lending rate built from nominal lending rate (one‑year bond rate plus credit premium pprdm_G) and expected inflation. - Credit premium pprdm_G = risk component + regulatory component; financial accelerator operates via expected output gap affecting risk premium. - Banks’ capital-to-assets ratio (~leverage) adjusts via retained earnings and dividend policy with a nonlinear rule preventing dividends if capital ratio falls below target (Eq.37). - External balance and financial flows: - BoP identity equates current account and financial account (Eq.38). - Current account components include net exports, remittances, interest on FCY public debt, and other private foreign income (Eq.39). - Financial inflows decomposition and renewal of expired FCY debt factor in exponential rescaling by nominal GDP growth and depreciation (Eq.40). - Trend and gap decompositions for private financial inflows and country risk premium feed the UIP and external financing dynamics (Eqs.41–43). - UIP condition: domestic market rate = foreign rate + country risk premium + ξ·FXI + 4·(expected nominal depreciation) + ε_rs (Eq.44), with expected nominal exchange rate forward/backward‑looking (Eq.45). - Policy blocks: - Monetary policy: Taylor‑rule with inflation reaction coefficient 1.5 and output gap reaction 0.3 in calibrated rule; interest rate smoothing included; policy rate is overnight RRP rate operationally. - FX intervention (FXI) and reserve accumulation rules (fxi_rrr rule, fxa_rrr accumulation toward target, ARA reserve ratio historically > 150 percent since 2008). - Capital Flow Management measures (CFMs): administrative restrictions (τr CCFM,T fmiI) and capital inflow taxes (τCCFM,focw) modify sensitivity of private inflows to UIP premium and effective cost of inflows (Eq.51–52). - Macroprudential policy: leverage ratio target (capital/total assets) can be passive (ρbG_TTTTTT = 1) or active (ρbG_TTTTTT < 1 with φ > 0) to smooth credit cycle (Eq.53). - Fiscal policy: reaction function targeting public debt and stabilizing growth; primary deficit cyclically adjusted; government instruments include revenues rddI, current and capital spending, transfers; debt target follows AR(1) (eq.54), deficit identities and debt service specified (eqs.55–61). ### Impulse responses and scenario analysis (Section 4.1) — key simulations and results - Simulation conventions: - Impulse responses start from steady state; plotted as deviations from initial BGP steady state (zero = no deviation). - Scenarios include US Fed tightening, international food price shock (El Niño), private consumption shock, public debt consolidation, asset price increase/credit boom, risk appetite shock, minimum wage increase. - US Fed monetary tightening: - Shock: additional 100 basis point increase in US Fed funds rate in Q1 2024. - Without FXI: BSP raises policy rate; domestic demand contracts over four years; inflation returns to target by early 2026. - With FXI: milder depreciation, lower policy rate increases, smaller economic contraction; FXI reduces reserves and requires sterilization to avoid monetary control loss. - International food price shock (El Niño): - Shock: world food prices surge by 10 percent vs baseline; real food price gap +10 percentage points in first two quarters of 2024. - Outcome: immediate non‑core food inflation spike; BSP tightens to prevent second‑round effects; tightening costs include negative output gap, reduced consumption, worse terms of trade, and higher risk premiums. - Private consumption shock: - Shock: households increase consumption by 1 percentage point in Q1 2024. - Outcome: higher output gap, core inflation pressure; BSP raises policy rate; nominal appreciation curbs non‑core inflation; external position worsens, raising risk premiums and constraining future monetary easing. - Public debt consolidation: - Scenario: reduce debt target by 10 percentage points of nominal GDP during 2024, reaching new target by end‑2025 (adjustment over eight quarters). - Measures: revenue mobilization and tax increases to deliver primary surplus. - Short run: disposable income falls, consumption and output gap contract. - Medium run: improved external position, lower risk premiums, peso appreciation, temporary positive output gap and higher inflation prompting policy tightening. - Historical context: public debt at threshold of 60 percent of GDP in 2023 vs pre‑pandemic average 40 percent. - Asset price increase and credit boom: - Shock: permanent 10 percent asset price increase. - Without macroprudential measures: capital adequacy falls; banks raise lending spreads; two years needed to restore capital adequacy; monetary policy responds only to price stability. - With macroprudential response: countercyclical capital buffer raised; banks accumulate capital via higher lending rates and lower dividends; tighter lending conditions neutralize long‑run asset price effects without monetary tightening. Conclusion: macroprudential policies more effective than monetary tightening for asset price bubbles. - Risk appetite shocks: - Shock: one‑time negative 10 percentage point shock to risk appetite → 8 percent nominal depreciation in no‑FXI case. - FXI can reduce required policy rate increase and economic cost; CFMs can complement FXI and reduce immediate FXI needs but may lead to more depreciation in medium term under administrative CFMs. - Minimum wage increase shocks: - Shock: minimum wage increase ≈ 4 percent year‑on‑year in Q4 2024. - Two scenarios: (1) no pass‑through beyond minimum wage recipients; (2) partial pass‑through to business wages (wider coverage). - Effects: higher private consumption and marginal costs, inflationary pressures; BSP tightens gradually. Larger macroeconomic effects in partial pass‑through scenario. ### Calibration and empirical validation (Section 4.2) - Calibration approach and objectives: - Iterative process: model structure → data collection → parameter selection → iterative modification. - Parameter categories: - steady‑state parameters (potential growth, nominal GDP ratios), - gap/trend decomposition parameters (relative volatilities and persistence), - transmission and policy response parameters (interest elasticities, fiscal multipliers, Taylor rule coefficients). - Calibration distinct from Bayesian estimation but uses multivariate filter (Kalman) and iterative in‑sample fitting via MATLAB IRIS Toolbox. - Calibrated features and notable parameter choices: - Lower than 0.5 backward‑looking coefficients suggest low persistence in many series. - Stronger income channel to reflect higher share of Keynesian/low‑income households. - Calibrated Taylor rule coefficients: inflation reaction = 1.5; output gap reaction = 0.3. - Interest rate smoothing higher than benchmark reflecting BSP practice. - In‑sample performance: - PAMPh2.0 forecasts align closely with observed data and capture main turning points with minimal bias. - RMSE relative to Random Walk: PAMPh2.0 RMSE generally < 1 (outperforms RW) for most variables and horizons; exception: current account deficit RMSE ratio slightly > 1 but close to 1. - Conditionality: in‑sample forecasts conditional on assumed policy responses; deviations occur if actual policy differs. - Historical interpretation and shock decomposition: - Kalman filter used to estimate gaps/trends and structural shocks. - Inflation drivers: core inflation driven by real marginal cost; non‑core (food, energy) driven by international commodity prices and exchange rate. - Real activity: Philippines’ potential growth high and stable except major shocks (GFC, COVID‑19); output gap central to inflationary episodes. - Fiscal history: debt consolidated from 60 to 40 percent pre‑COVID‑19; rose to 60 percent during COVID‑19; consolidation resumed from 2022. - Monetary narrative: real interest rate gap and country risk premium explain shifts in policy stance; pre‑2015 contractionary, 2015 onwards more expansionary, COVID‑19 easing, post‑2022 tightening. ### Appendix highlights: model equations and parameters - Appendix B: full system of equations across blocks: - Real aggregate demand, labor market, banking/credit, aggregate supply (core/food/energy Phillips curves), GDP deflators, real interest rate and exchange rate, balance of payments, monetary policy (Taylor rule, FXI/FXA rules), fiscal block (expenditures, revenues, debt dynamics), foreign variables. - Appendix C: parameter listings and document identifiers (Working Paper No. WP/2024/148); page references included. ### Policy implications and institutional outlook (Conclusion) - PAMPh2.0 strengthens FPAS by providing: - forward‑looking, policy‑endogenous projections, - integrated macrofinancial and labor channels, - tools to analyze non‑traditional instruments (FXI, CFMs, MPMs). - Institutional implications: - Facilitates coordination across monetary policy, financial supervision, and macroprudential sectors. - Requires refinement of forecasting and policy calendars, improved interdepartmental communication, and ownership via dissemination and iterative feedback. - Operational outlook: - Remaining gaps are manageable; formal adoption underway with a clear plan for activities and future model refinements as theoretical and empirical understanding evolves. *Source: wpiea2024148-print-pdf — A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (PAMPh2.0) — IMF Working Paper No. WP/2024/148 — https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024148-print-pdf.pdf* ### 1. Introduction ........................................................................................................ ### 1. Introduction ### BSP modernization motivations and context - The Bangko Sentral ng Pilipinas (BSP) is upgrading its Forecasting and Policy Analysis System (FPAS), moving from a multi-equation econometric model (MEM) toward a semi-structural Quarterly Projection Model (QPM) as the FPAS core. - Motivations include: increasing complexity of the operating environment; a small open economy vulnerable to sudden and large swings in capital flows and exchange rate volatility; and global supply shocks. - The extended QPM, Policy Analysis Model for the Philippines, version 2.0 (PAMPh2.0), provides forward-looking projections with endogenous monetary policy and extends to include fiscal policy, macrofinancial linkages, labor dynamics, and additional tools like FX interventions. - Collaborative IMF–BSP technical assistance (TA) led by ICD began in Spring 2022 to develop and modernize PAMPh2.0. ### PAMPh2.0 purpose, design, and features - PAMPh2.0 is an extended QPM intended to enhance real-time evaluation of decisions, analyze trade-offs across policy tools, and support interdepartmental coordination within the BSP. - Key model features: - Forward-looking projections with endogenous monetary policy. - Inclusion of fiscal policy, macrofinancial linkages, labor dynamics, and FX interventions. - Inspired by the extended QPM FINEX (Berg et al., 2023) and the Integrated Policy Framework (IPF). - Incorporates macro-financial channels via the microfounded portfolio balance approach by Gabaix and Maggiori (2015). - Capable of analyzing policy tools beyond standard monetary and fiscal policies: foreign exchange intervention (FXI), capital flow management measures (CFMs), and macroprudential measures (MPMs). - Designed as a state-of-the-art, open-economy New Keynesian general equilibrium model for baseline forecasts, risk analysis, policy scenarios, and simulations. - Structured into modular components for operational efficiency and interpretability. ### Operational advances and milestones achieved - TA project milestones and extensions incorporated gradually include: - “Conditional forecasting.” - Preparation and presentation of shadow (mock) forecasts in parallel or ahead of real-time forecasts. - Conducting scenario analyses. - Dissemination and early adoption steps: - PAMPh2.0 features and structure were presented during the 61st Philippine Economic Society Annual Meeting and Conference on 7 November 2023. - Internal and external dissemination has clarified calibration methods, labor and wage dynamics under persistent inflation, and model validation priorities. ### Validation approach and forecast evaluation - Three forecast evaluation methods considered: - Decomposition of changes in consecutive forecasts (applied to shadow forecasts) to explain forecast variations to policymakers. - Comparing historical forecasts with actual data—requires at least a year’s worth of historical quarterly forecasts and more elaborate databases; preparations underway. - Statistical evaluation (bias and RMSE) during early forecasting stages—acknowledges conditionality of PAMPh forecasts on policy interest rate and potential deviations driven by actual policy responses. - Emphasis on creating a robust model that fits Philippines empirical evidence while maintaining theoretical and macroeconomic consistency within its monetary regime. ### Challenges, policy communication, and institutional implications - Key challenges in adopting PAMPh2.0: - Managing communication associated with multiple channels and trade-offs when constructing narratives for complex policy mixes. - Integrating policies that operate on different time frames and frequencies and affect various parts of economic cycles. - Coordination across BSP departments that operate independently; need for integrated views guided by the BSP's Advisory Committee (AC) on Monetary Policy and Monetary Board. - Assessing the size and nature of shocks in real time; preventive policies (e.g., MPMs) may be necessary to maintain macro-financial stability. - PAMPh2.0 aims to assist in: - Fostering ownership and drawing early-stage feedback through internal and external dissemination. - Institutionalizing coordination among monetary policy, financial supervision, and macroprudential sectors. - Providing technical staff and policymakers tools to assess initial conditions and assumptions, quantify policy effects and alternative scenarios, and understand trade-offs to align policies with the BSP's long-term objectives. ### Roadmap and outlook for PAMPh2.0 - The BSP plans additional reforms alongside the core model: - Refinements to the forecast and monetary policy meeting calendar. - Establishing effective interdepartmental and vertical communication with senior management to facilitate policy deliberations. - PAMPh2.0 is positioned as a dynamic model expected to undergo future refinements as theoretical and empirical understanding evolves. - Remaining gaps are not expected to significantly impede operationalization and adoption; formal adoption is underway with a clear plan of activities. *Source: wpiea2024148-print-pdf — 1. Introduction — https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024148-print-pdf.pdf* ### Box 1. Multi-Phase Tec ### Box 1. Multi-Phase Tec ### Technical Assistance Proceedings: multi-year modernization of PAMPh - Collaboration between the IMF Institute for Capacity Development (ICD) and the BSP Department of Economic Research (DER). - Phase 1: - First on-site mission in April 2022. - Aim: build upon the initial version of PAMPh, focusing on reviewing its features and forecasting performance. - Phase 2: - Mission in October 2022. - Introduced detailed decomposition of aggregate demand, external, and fiscal sectors to quantify transmission channels and interlinkages of different policy instruments. - Concluded with a meeting discussing challenges, risks, and opportunities with the Governor and members of the Monetary Board. - Phase 3: - TA mission in March 2023. - Focused on further enhancing PAMPh’s calibration and storytelling capacity. - Introduced a systematic assessment of forecasting performance. - Initial discussions on institutional changes needed for PAMPh’s planned adoption as the BSP’s workhorse model for monetary policy analysis and forecasting. - Phase 4: - July 2023. - Incorporated credit cycle dynamics, focusing on macrofinancial linkages and the role of macroprudential policy. - Added capital flow management measures to analyze impacts on exchange rate dynamics and monetary conditions, and interactions with other policies. - Concluded with discussions on future steps with senior management. - Phase 5: - February and March 2024. - Extended PAMPh to incorporate the labor block. - DER-EFFG and ICD TA team addressed senior management concerns regarding: - model structure, - market interest rate, - policy rule calibration, - supply shock treatment, - inflation expectations. - Concluded with a presentation to the BSP Monetary Board and Advisory Committee on Monetary Policy covering model issues, PAMPh-based shadow forecasts, and assessment of alternative scenarios. ### Stylized facts integrated into PAMPh2.0: motivation and model adaptations - Purpose: - Ensure PAMPh2.0 relevance in macroeconomic policy analysis and monetary policy formulation by incorporating crucial stylized facts of the Philippine economy. - Align closely with the BSP’s policy framework and country-specific features that deviate from a standard QPM. - Intended outcome: - Adaptations to enhance model structure and properties; detailed explanations in Sections 3 and 4 (of the source). ### GDP and expenditure-side decomposition (key findings) - Long-run growth and major episodes: - Average growth of 5.0 percent from 2001 to 2023. - GDP growth slowed to 1.4 percent in 2009 (GFC). - GDP grew at 6.4 percent from 2010 to 2019, with over 6.0 percent growth sustained for eight consecutive years starting in 2012. - Significant contraction in 2020 due to COVID-19 pandemic; recovery in 2021–2022. - Sectoral contributions: - Private consumption, investment, and public consumption all made steady contributions to growth pre-pandemic. - Services and industry accounted for approximately 90 percent of the economy’s performance. - Output gap: - Generally positive during 2010–2019, particularly in 2018–2019. - Post-pandemic dynamics: - 2021–2022 recovery driven by services on supply side and household, government expenditures, and investments on demand side. ### Labor market and wage developments (key findings) - Employment and job quality: - Longest economic and job growth period before COVID-19. - Wage and salary workers grew at an annual rate of 4.6 percent from 2015 to 2019. - Pandemic led to loss of 1.7 million wage and salary jobs by January 2021. - Labor market gradually improved in 2022; sustained growth in wage and salary workers only evident beginning in 2023. - Wages: - Average daily pay and daily minimum wage grew at an average of 4.5 percent from 2005 to 2012. - Since 2013: - Average daily pay growth accelerated to 5.4 percent. - Minimum wage growth slowed to 3.0 percent. - Real average daily pay grew by 0.2 percent in 2023, compared with its 10-year average of 1.9 percent. - Real minimum wage increased by 1.3 percent in 2023, compared with its 10-year average growth of -0.2 percent. - Labor Utilization Composite Index (LUCI): - LUCI combines nine labor market variables using principal components method. - Indicates significant loosening of the labor market in 2020–2021 and gradual improvement since Q1 2022. - Estimates for Q4 2023 show sustained enhancement in LUCI, potentially leading to inflationary pressures in upcoming quarters. - Definitions/notes: - Average basic daily pay excludes allowances, bonuses, commissions, overtime pay, and benefits in kind (footnote definition preserved). ### Prices: core and non-core subcomponents (key findings) - Inflation history and drivers: - From 2002 to 2009 under IT framework: headline inflation was above target in four years, below in three years, and within target once. - Above-target episodes driven by supply-side shocks (global oil, weather, VAT/excise taxes). - Post-GFC period (2010–2017): low and stable inflation; GDP growth averaged 6.5 percent from 2010 to 2017; inflation slightly below 3.0 percent. - 2018 spike attributed to supply-side factors (surges in global crude oil prices, increased domestic excise taxes, elevated domestic rice prices), and demand-side pressures in services inflation. - 2019–2020: headline and core inflation decelerated back within target range due to lower food and energy prices and structural reforms (e.g., Rice Tariffication Law). - 2021–2023 surge: - 2021: headline inflation began to rise driven by food and energy inflation; core inflation slowed earlier due to output gap effects. - 2022 and 2023: inflation surged, averaging 5.8 percent and 6.0 percent, respectively, driven by consecutive supply-side shocks and second-round effects. - Specific drivers: escalated oil and fertilizer prices (Russia–Ukraine conflict), weather disruptions to food supplies, transport fare hikes, electricity adjustments, and higher minimum wages. - Relative price dynamics: - Energy price surge in 2021 and early-2022 war shock contributed to inflationary pressures. - Positive second-round effects evident in H2 2022 with elevated relative price of core. ### Banking sector and credit cycle (key findings and indicators) - Banking sector size and performance: - Banks accounted for approximately 78 percent of total financial assets from 2010 to 2023. - 2023: strong performance with robust capital and liquidity buffers, continuous growth in assets, loans, deposits, and profits. - Capital adequacy: - As of end-June 2023: - Solo CAR for Universal and Commercial Banks (U/KBs): 16.3 percent. - Consolidated CAR for U/KBs: 16.9 percent. - BSP minimum requirement: 10 percent. - BIS standard: 8.0 percent. - Credit and intermediation: - Outstanding credit relative to nominal GDP averaged 44 percent from 2010 to 2023. - Credit-to-GDP ratio rose to 54 percent by 2023. - Production loans comprised over 90 percent of total credit. - Real estate credit share rose from 13 percent in 2010 to 20 percent in 2023. - Credit gap and financial conditions: - Positive credit gap started to open in 2018 due to rising credit growth; tighter financial conditions and BSP monetary tightening helped reduce the credit gap. - Credit gap at end-2023 turned slightly negative. - Financial cycle indicator constructed from: credit gap, interest rate spread between lending and policy rates, and property price growth; each variable carries equal weight. - Interest rates and spreads: - Average bank lending rate generally tracked the BSP policy rate (RRP). - Positive spread reflects term premium and credit risk; spread widened considerably during the pandemic. - BSP Senior Bank Loan Officers' Survey indicated net tightening of overall credit standards for enterprises and households during COVID-19. - Average bank lending rate definition preserved (footnote): average of U/KBs’ reported quoted or indicative high/low lending rates as reported in the IRLD survey; prior to 2020 based on interest income and outstanding peso-denominated loans. - Banks’ balance-sheet composition and performance: - Loans dominated U/KBs’ balance sheets: increased from 63 percent in 2010 to around 73 percent in 2023. - Capital-to-asset ratio (leverage ratio) stable at approximately 12 percent, except for a temporary surge in Q1 2013. - As of Q4 2023: - ROA of U/KBs: 1.5 percent (from 0.8 percent in Q1 2021). - Share of non-performing loans to total assets fell to 1.6 percent from 2.1 percent at pandemic peak. - Macroprudential framework: - Countercyclical capital buffer (CCyB) implementation mechanism announced December 2018 (BSP Circular No. 1024) but not yet active operationally. - Current CCyB: 0.0 percent; any increase effective 12 months after announcement; decreases implemented immediately. - Pre-deployed macroprudential measures available for countercyclical adjustment include caps on loan-to-value ratios, general loan loss provisioning, single borrower limits, concentration limits, limits on open FX positions, asset cover for banks’ foreign currency deposit unit (FCDU) liabilities, and liquidity measures. *Italic: IMF WORKING PAPERS — A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (PAMPh2.0) — Source content: wpiea2024148-print-pdf - Box 1. Multi-Phase Tec* ### 2.5 Monetary Policy and Transmission Mechanism ### 2.5 Monetary Policy and Transmission Mechanism ### BSP framework and policy instruments - The BSP has adopted a flexible inflation targeting (FIT) framework in conducting monetary policy. - The BSP's primary instrument is the overnight reverse repurchase (RRP) rate, which the BSP adjusts based on the emerging outlook for inflation, GDP growth, and other macroeconomic variables (e.g., interest rates, exchange rate, domestic credit and equity prices, indicators of demand and supply, and external economic conditions). - The BSP has been operating a symmetric interest rate corridor (IRC) since June 2016, with the deposit and lending facility rates set 50 basis points (bps) below and above the target RRP rate. - The BSP operates active term liquidity facilities to help absorb structural excess liquidity, including auctions for 7- and 14-day term deposit facilities, and the 28- and 56-day BSP Securities. - The BSP can adjust reserve requirements (currently stands at 9.5 percent for U/KBs), and the rediscount rate on loans extended by the BSP to banking institutions. - The BSP can undertake outright sales or purchases of government securities to adjust liquidity in the financial system. ### Pass-through of policy rate adjustments to market rates - The passthrough of policy rate adjustments to overnight market rates is generally high. - Both the IBCL and overnight PHIREF rates broadly track movements of the RRP rate. - Post adoption of the IRC system in 2016, a correlation coefficient of 0.99 between the RRP and IBCL rates was observed. - The correlation for PHIREF was 0.92. - On average, both IBCL and PHIREF rates fall below the RRP rate but the IBCL rate has a smaller spread against the RRP rate. - Traditionally, the BSP utilized the unsecured IBCL rate, along with other market interest rates, as its primary indicator to guide open market operations; following the implementation of variable-rate RRP auctions in September 2023, the BSP looks at the overnight RRP rate as its principal market rate. ### Real interest rates and policy tightening - The real interest rate remained negative for much of 2020-2022 due to policy rate cuts amid the COVID-19 pandemic. - The real interest rate trended higher, reaching 3.7 percent in Q1 2024, following the 450-bp increase in the RRP rate by the BSP to address significant inflation pressure. ### Transmission to longer-term rates and bank funding - While passthrough to short-term market rates appears high, passthrough for interest rates at the longer horizon could be moderated. - Long-term bank loan rates may be influenced by perceptions of risks on economic prospects and inflation as well as credit worthiness of borrower. - The funding costs of banks are also affected by reserve requirements and other government regulations. - Yields on long-term government securities are influenced by the level of indebtedness and country risk premium. *IMF Working Paper — A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (PAMPh2.0), Section 2.5* ### 2.8 Foreign Trade ### 2.8 Foreign Trade ### Trade profile and external sector contributions - The Philippines has experienced a trade deficit for most years, as imports of capital goods, consumer goods, and raw materials surpassed the value of key exports such as electronic products, machinery, textiles, and agricultural products. - Major trading partners included the United States, China, Japan, South Korea, and ASEAN member countries. - Key exports included electronics, semiconductors, clothing, machinery and equipment, coconut oil, and tropical fruits. - Imports consisted primarily of intermediate goods and capital equipment essential for domestic production and infrastructure development. - The external sector benefited from remittances from the OFWs and receipts from tourism and BPO industry. ### External assumptions in PAMPh - The external block of the PAMPh is determined by forecasts from the Global Projections Model Network (GPMN), which uses the GPM++ Model. - The GPM++ Model: - covers approximately 30 countries, representing around 80 percent of the world GDP. - encompasses commodities and financial linkages to the real economy. - Exogenously adopted inputs by PAMPh include output gap and CPI changes in key regions: the US, Euro Zone, China, Japan, and others. - Price changes of oil and food in the international market that impact domestic food and energy inflation are sourced from GPM++ projections. - Estimated spillovers and exchange rate changes from these regions, along with the impact of interest rate movements from major central banks like the US Federal Reserve, are incorporated into PAMPh. ### Role of external balance and financial flows in PAMPh2.0 - The exchange rate reconciles the balance of payments (BoP) and influences net exports and other BoP components. - Financial flows depend on the difference between domestic and foreign interest rates, adjusted for anticipated depreciation, deviating from Uncovered Interest Parity (UIP). - FX interventions (FXI) significantly affect the necessity for portfolio capital flows to maintain BoP equilibrium. - Capital Flow Management measures (CFMs) influence the effective interest rate for foreign investors, shape the stock of Net Foreign Assets (NFA), and affect the economy’s response to external shocks. - The net export position anchors current account dynamics, further influenced by additional foreign-related income, interest income, and foreign direct investment returns. - The financial account mirrors the current account and dictates the external financing position. - The country risk premium, contingent on external financing, shapes nominal exchange rate dynamics and thereby influences inflation pressures. - PAMPh2.0 can examine scenarios where policymakers implement CFMs to restrict the private sector’s access to foreign financing, addressing adverse effects of capital outflows or disorderly market conditions (DMCs). ### Integration of external considerations into PAMPh2.0 policy framework - PAMPh2.0 integrates external and internal balances with policy levers, including: - Monetary policy (policy interest rate, FXI, macroprudential measures). - Fiscal policy (government absorption via consumption and investment, tax considerations). - Non-traditional tools: FXI, CFMs, and MPMs. - The model allows assessment of interactions among these instruments in response to economic shocks and considers initial debt levels and the sensitivity of inflation to exchange rate depreciation. ### Monetary policy in PAMPh2.0 - The BSP employs three tools to maintain price stability and support real activity: 1. Operating within an adopted Flexible Inflation Targeting (FIT) regime, the BSP determines the policy rate based on a Taylor rule. Monetary policy typically responds to anticipated inflation deviations from the targeted (midpoint) 3 percent level and the cyclical position of GDP. 2. FX intervention is used in cases of disorderly market conditions (DMC) to stabilize the peso and alleviate sharp depreciation pressure from capital outflows, helping prevent de-anchoring inflation expectations. 3. As financial regulator, the central bank can adjust reserve requirements or implement other macroprudential policies to influence private sector access to credit. ### Fiscal policy in PAMPh2.0 - Fiscal authorities use a reaction function aiming to: - Maintain public debt at a targeted level. - Stabilize economic activity by keeping growth close to potential. - The government decides on the cyclically-adjusted primary balance based on deviations of public debt from the target and the cyclical position of the economy. - Government instruments include multiple revenue sources and current and capital spending (including social transfers), adjusted to achieve the fiscal balance target. - The fiscal impulse (change in fiscal policy stance) impacts economic activity and can influence inflation through a limited second-round effect. - The framework highlights trade-offs between tax-based vs. expenditure-based fiscal consolidation and the need for coordination between monetary and fiscal policy. ### Banking sector, credit channels, and model nonlinearity - The model depicts a banking sector where: - Commercial banks extend consumer credit to households and collateralized credit to firms for housing. - Lending rates are tied to compounded effective market rates closely connected to the monetary policy rate. - Asset prices influence collateral credit positions, introducing endogenous financial cycles. - Most model equations are linear, with nonlinearity incorporated in specific blocks: - Macroprudential block: when U/KBs first reduce dividend payments to build-up capital; if insufficient, banks raise spreads. - Nonlinear effects also used for public debt limits or limits on FXI when reserves get too low. ### Modeling conventions and notation - Variables are expressed in log variables and decomposed into gap and trend components. - Gaps are percentage deviations from potential or trend and denoted with a hat (e.g., output gap 푦푦�). - Trends are described with autoregressive processes and denoted with a bar (e.g., potential GDP 푦푦̄). - Fiscal and external balance structural equations use nominal GDP ratios, denoted with a superscript (rcrr rcrr) for ratios (e.g., public debt-to-GDP ratio 푑푑푑푑푑푑 푡푡 푟푟푟푟푟푟). *IMF Working Paper — A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (PAMPh2.0), section 2.8* ### 3.1 Internal Balance ### 3.1 Internal Balance ### 3.1.1 Real Economy and Aggregate Demand - GDP identity (expenditure side): Y decomposed into C, I, G, X, M with component price deflators P (Eq.1 structure retained in model). - Linearized representation: output gap (ŷ) computed as weighted average of expenditure-side component gaps with time‑variant weights from nominal GDP ratios to potential (Eq.2). - Trend growth identity: GDP trend as weighted average of growth rates of expenditure components with nominal-GDP-ratio weights (Eq.3). - Consumption gap equation (Euler-type with external habit formation; hybrid expectations, monetary conditions, and income channels) (Eq.4): - Consumption gap: Ĝ - Monetary conditions: real interest rate gap r̂, real loan rate gap r̂L, risk premium gap p̂rdm - Income channel: output gap ŷ, export relative price gap rpp̂X, import relative price gap rpp̂M, remittances rrdm̄t, real wage gap w r̂, taxes r dÎ - Credit/financial channel: εIGR̄ h,rrr (shock to household credit) - Preference shock: εĜ - Labor and wages: - Business wage Phillips curve for nominal business wage q-o-q growth ΔwB (Eq.5): backward and forward-looking wage expectations, consumption gap Ĝ, real wage gap w r̂, minimum‑wage spillover ΔwM shock. - Minimum wage dynamics: wM = wM_{t-1} + εΔwM (Eq.6). - Total wage index: w = σ6 wM + (1−σ6) wB (Eq.7). - Real wage identity: wr = w − Gp i rG c r c (Eq.8). - Real wage decomposition: wr = w r̂ + w r̄trend (Eq.9). - Trend real wage autoregressive growth with potential-growth gap influence (Eq.10). - Unemployment (Okun’s law): unemployment gap ûId linked to lagged ûId and lagged output gap ŷ_{t-1} (Eq.11). - Unemployment rate: u = ūNAIRU + û (Eq.12). - NAIRU dynamics driven by structural level ū_{ss} and deviations of potential GDP growth from long run (Eq.13). - Investment: - Investment gap (Ĩ) guided by Tobin‑Q theory (Eq.14). - Tobin‑Q definition includes expected output gap, expected portfolio investment ẑh, monetary conditions (r̂, r̂L) and risk premium p̂rdm, and expected q̂ (Eq.15). - Public capital expenditures (dx̂ i,rrr) enter gross capital accumulation. - Government consumption: exogenous autoregressive gap process gĜ (Eq.16). - Exports and imports: - Export gap x̂ autoregressive process with foreign demand ŷw and real effective exchange rate gap rddr̂ relative to real export price rpp̂X (Eq.17). Note: export elasticity to REER is relatively low due to semiconductor sector. - Import gap m̂ depends on consumption gap Ĝ, investment gap Ĩ, government absorption gĜ, export gap x̂, and real exchange rate gap rddr̂ with calibrated shares from I/O tables (Eq.18). - Trend equations for expenditure components: - Trend dynamics for j ∈ {G, I, gG, X}: autoregressive with steady‑state real growth, error‑correction to steady-state nominal GDP ratios, and shocks εΔ ̅j (Eq.19). - Potential GDP growth follows AR process toward Δŷ_{SSA} with shock εΔŷ (Eq.20). - Model structure follows FINEX / Berg et al. (2023) development and preserves time‑varying trend ratios to maintain steady‑state nominal GDP shares. ### 3.1.2 Inflation and Aggregate Supply - CPI decomposition: headline CPI (Gp i) split into food, energy, and core components Gp i_{fcf}, Gp i_{ceg y}, Gp i_{Gc rc} with COICOP weights w_{Gc i,·} and an error term for unmodeled weight changes (Eq.21). - Real normalization: all price levels normalized by headline CPI, split into gap and trend components; trends follow exogenous AR processes and gaps feed Phillips curves. - Core inflation: New Keynesian open‑economy hybrid Phillips curve with backward and forward expectations, real marginal costs rmc_Gc, and cost‑push shock (Eq.22). - Real marginal cost function rmc_Gc includes: - Output gap ŷ (domestic demand pressure) - International spillovers via REER gap rddr̂ − rpp̂_{Gc} - Pass‑through from non‑core relative prices (rpp̂_{C I c r g y} − rpp̂_{Gc} and rpp̂_{fcf} − rpp̂_{Gc}) - Real wage gap w r̂ (Eq.23) - Food and energy Phillips curves: hybrid expectations plus cost terms driven by international commodity prices deflated by domestic commodity price levels and their own shocks (Eqs.24–25). - Energy: largely determined by international Brent oil price; idiosyncratic domestic shock absorbs electricity/water/gas components. - Food: international commodity food price basket with country‑specific shocks absorbed. - Phillips curves for GDP expenditure deflators: incorporated and interconnected with expenditure gaps and imported inflation/pass‑through (details in Appendix B reference noted in source). - Relative price and pass‑through framework follows Al‑Sharkas et al. (2023) approach for deriving real marginal costs from nominal price differentials. ### 3.1.3 Banking Sector - Credit types: two credit categories—households (h) and firms (f); distinction between stock (outstanding credit) and flow (newly issued loans). - Outstanding credit ratio G r_{j,rrr} evolves from surviving previous outstanding credit, growth of nominal potential GDP scaling, newly issued credit ratio I G r_{j,rrr}, and shock εG r (Eq.26). δ_j denotes maturing share. - Newly issued credit ratios decomposed into trend and gap; gap dynamics differ for households and firms. - Household new credit gap I G r̂_{h,rrr}: - AR component, positive response to private consumption gap Ĝ, negative response to real lending rate gap r̂L, and credit demand shock εI Gr̂_h (Eq.27). - Firm new credit gap I G r̂_{f,rrr}: - AR component, dependence on relative house price gap ẑh, suppressing effect of real lending rate gap r̂L, lower bound constraint (max(..., −I G r̄_{f,ss,rrr})), steady‑state share I G r̄_{f,ss,rrr}, loan‑to‑value lt_I, and credit demand shock εI Gr̂_f (Eq.28). - Real lending rate: - Nominal lending rate rrr_L tied to one‑year government bond rate and includes credit premium pprdm_G and shock εr sL (Eq.29). - Real lending rate computed by subtracting expected inflation from nominal lending rate. - Credit premium pprdm_G composed of risk component pprdm_GGG and regulatory component drpprdad (Eq.30). - Risk component pprdm_GGG: AR dynamics, negatively linked to expected output gap E_r ŷ_{t+1} (financial accelerator), positively linked to country risk premium p̂rdm, and shock εcrcm_cc (Eq.31). - Regulatory component represents bank margin added to meet capital requirements. - House prices: - Relative house price gap ẑh AR with expected future relative house price, positive effect from output gap ŷ, negative effect from real lending rate gap r̂L, and shock εẑh (Eq.32). - House price decomposed into trend z̄h and gap ẑh. - Banks and capital: - Bank assets tta assumed stable relative to nominal potential GDP: tta = Gr_{rrr} + tta_wcfgc + εtta (Eq.33). Outstanding credit is explicitly modeled as main asset component. - Capital‑to‑assets ratio dG r rrr evolves by carrying forward prior ratio, scaled by asset growth, plus return on assets rra and dividend share diI_r, and shock εbG (Eq.34). - Return on assets rra approximated from lending rate minus policy rate and other asset returns (Eq.35). - Banks can adjust lending‑rate spread drpprdad to accumulate capital: drpprdad = −f_c (dG_r − dG_r,TTT) + εbbsscrcrf (Eq.36), where dG_r,TTT is the capital target. - Dividend policy nonlinear rule: diI_r = max((1−rrr) diI_ss + dG_r − dG_r,TTT, 0) + εfiI ensures no dividends if capital ratio falls below target (Eq.37). - Model captures financial accelerator: output gap reduces credit premium and thus eases lending conditions; housing collateral and loan‑to‑value influence firm credit demand; bank capital dynamics interact with lending spreads and dividend policy to fulfill regulatory capital objectives. *Source: wpiea2024148-print-pdf - 3.1 Internal Balance* ### 3.2 External Balance ### 3.2 External Balance ### BoP constraint and model role - In the extended QPM, the BoP constraint is modeled explicitly, represented by the identity eq.38. - The current account (net cross-border flows of goods and services) matches the financial account (net flows of financial claims)—generically, exports less imports together with private financial flows less FX purchases must equal zero. - The PAMPh2.0 country application of FINEX embeds the structure of endogenous private financial flows (i.e., portfolio flows and cross border bank lending) responding to the uncovered interest parity (UIP) premium. - Balance identity (as presented): - 0 = 퐺퐺푎푎 푟푟 푟푟푟푟푟푟 +푓푓푎푎 푟푟 푟푟푟푟푟푟 (38) ### Current account components (eq.39) - The current account is derived from: - the net export position (퐼퐼푥푥 푟푟 푟푟푟푟푟푟), - remittance inflows (푟푟푑푑푚푚 푖푖푡푡 푟푟 푟푟푟푟푟푟) referring to income flow from Filipinos working abroad, - interest payments on foreign currency (FCY) denominated public debt position (푖푖퐼퐼푡푡퐺퐺푟푟푟푟푡푡 푟푟 푓푓,푟푟푟푟푟푟), - other private foreign income (푟푟푡푡ℎ 푟푟 푟푟푟푟푟푟) which includes foreign interest payments on private net foreign asset and foreign direct investments. - Current account identity (as presented): - 퐺퐺푎푎 푟푟 푟푟푟푟푟푟 =퐼퐼푥푥 푟푟 푟푟푟푟푟푟 +푟푟푑푑푚푚 푖푖푡푡 푟푟 푟푟푟푟푟푟 −푖푖퐼퐼푡푡퐺퐺푟푟푟푟푡푡 푟푟 푓푓,푟푟푟푟푟푟 +푟푟푡푡ℎ 푟푟 푟푟푟푟푟푟 (39) - Note: ‘rat’ superscript refers to variables expressed as a ratio of nominal GDP. ### Financial account inflows and external financing needs (eq.40) - Financial account inflows (푓푓푎푎 푟푟 푟푟푟푟푟푟) are decomposed to determine the economy’s external financing needs commensurately with macroeconomic conditions. - Financial inflows decomposition (as presented): - 푓푓푎푎 푟푟 푟푟푟푟푟푟 =퐼퐼푓푓푝푝 푟푟 푟푟푟푟푟푟 −푓푓푥푥푖푖 푟푟 푟푟푟푟푟푟 −푓푓푥푥푎푎 푟푟 푟푟푟푟푟푟 + �푑푑푑푑푑푑푡푡1푌푌 푟푟 푓푓,푟푟푟푟푟푟 −푑푑푑푑푑푑푡푡1푌푌 푟푟−4 푓푓.푟푟푟푟푟푟 ⋅ exp �Δ 4 푑푑 푟푟 100 � exp �Δ 4 푔푔푑푑푝푝 푟푟 퐼퐼푐푐푚푚 100 �� (40) - Private sector financial inflows (퐼퐼푓푓푝푝 푟푟 푟푟푟푟푟푟) are arrived at residually under derived 푓푓푥푥푖푖 푟푟 푟푟푟푟푟푟, 푓푓푥푥푎푎 푟푟 푟푟푟푟푟푟 and the renewal of expired 푑푑푑푑푑푑 푡푡1푌푌 푓푓,푟푟푟푟푟푟. - The term exp() represents the exponential function. - Since terms are expressed in period-t nominal GDP, the four-period lag term of foreign currency-denominated public debt is rescaled by the year-on-year nominal GDP growth and nominal depreciation. ### Trend and cyclical decomposition (eq.41–43) - Trend versions of eq.39 (equilibrium current account balance) and eq.40, and components therein (all denoted by bars), track the trend and gap parts of the current account and financial account balances. - Trend and cyclical position of private financial inflows (as presented): - 퐼퐼푓푓푝푝 ����� 푟푟 푟푟푟푟푟푟 =퐼퐼푓푓푝푝 푆푆푆푆 + ℎ 2 1−ℎ 3 ∙� 푝푝푟푟푑푑푚푚������� 푟푟 −ℎ 3 푝푝푟푟푑푑푚푚������� 푟푟−1 − ( 1−ℎ 3 ) 푝푝 푟푟 푑푑 푚푚 푆푆푆푆 −ε 푟푟 푐푐푟푟푐푐푚푚 ����������� � (41) - 퐼퐼푓푓푝푝 � 푟푟 푟푟푟푟푟푟 =ℎ 1 ∙�푝푝푟푟푑푑푚푚� 푟푟 −ε 푟푟 푐푐푟푟푐푐푚푚 � (42) - The terms 푝푝푟푟푑푑푚푚������� and 푝푝푟푟 푑푑푚푚� denote country risk premium trend and gap positions respectively, and the ε 푐푐푟푟푐푐푚푚 ����������� and ε 푐푐푟푟푐푐푚푚 � are the corresponding shocks: - 푝푝푟푟푑푑푚푚 푟푟 =푝푝푟푟 푑푑푚푚������� 푟푟 +푝푝푟푟푑푑푚푚� 푟푟 (43) - The total country risk premium feeds into the UIP condition. ### UIP condition and exchange rate determination (eq.44–45) - The UIP condition is the financial market equilibrium condition: the difference between the domestic (푖푖 푟푟 푀푀) market interest rate and the foreign interest rate (푖푖 푈푈 푆푆) equals the expected (nominal) exchange rate depreciation (푟푟 푟푟 푐푐 −푟푟 푟푟) adjusted by the country risk premium (푝푝 푟푟 푑푑푚푚 푟푟). - The 푓푓푥푥푖푖 푟푟 푟푟 푟푟푟푟 term captures the impact of central bank FX intervention policies: as the BSP increases foreign reserves, it generates higher demand for foreign assets, leading to a rise in domestic interest rates. - An exogenous (ε 푟푟 푠푠) risk-on/off term captures the willingness of foreign investors to supply financing which falls with higher reserves (a state-contingent component). - UIP condition (as presented): - 푖푖 푟푟 푀푀 =푖푖 푟푟 푈푈 푆푆 +푝푝푟푟푑푑푚푚 푟푟 +휉휉∙푓푓푥푥푖푖 푟푟 푟푟 푟푟푟푟 + 4∙ ( 푟푟 푟푟 푐푐 −푟푟 푟푟 ) +ε 푟푟 푠푠 (44) - The expected nominal exchange rate is expressed as the weighted average of past and future nominal exchange rates: - 푟푟 푟푟 푐푐 =휇휇E 푟푟 푟푟 푟푟+1 + ( 1−휇휇 ) � 푟푟 푟푟−1 + Δ푟푟̅ 푟푟 2 � (45) - Interpretation: The UIP condition determines the short-run position of the nominal exchange rate, where the expected nominal exchange rate is a combination of forward and backward-looking terms. *IMF WORKING PAPERS A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (PAMPh2.0) — 3.2 External Balance* ### 3.3 Macroeconomic Policies ### 3.3 Macroeconomic Policies ### Monetary Policy - Extended QPMs motivated by the FINEX can accommodate a wide range of traditional and non-traditional policy instruments: a policy interest rate, FXI—to accumulate reserves and stabilize the exchange rate, MPMs, and CFMs (price-based and regulatory capital controls). - The BSP's primary instrument is the overnight reverse repurchase (RRP) rate, with the ability to deploy FXI, CFMs, and MPMs; these instruments are typically utilized when conventional interest rate policy might be constrained, taking a pragmatic approach rather than following an explicit framework. - The BSP’s flexible exchange rate regime is the first line of defense against financial market volatility and global shocks; the BSP may transact in the FX market to ensure orderly market conditions and to reduce excessive short-term volatility that could impact inflation and inflation expectations. - Interest rate reaction function: - The BSP operates under a flexible inflation-targeting regime and the policy rate follows a standard Taylor rule (eq.46); it reacts to expected deviations of annual CPI (Δ4 GGppi r+2) from the target (ΔGGppi TTTTTT) and to contemporaneous deviations of output from potential (y� r). - Policy rule (eq.46) includes an autoregressive coefficient to prevent an immediate response of the policy rate, aligning with historical volatility of the policy rate; beyond the short-term horizon the central bank sets the interest rate based on the neutral real interest rate and the inflation target. - A shock to the policy rate (εipcp) allows for discretionary steps in monetary policy, deviating from systematic behavior. - Transmission to market rates: - The policy rate is translated to the market rates affecting credit activity via eq.47: iM = γ4 iM−1 + (1−γ4) icco + εM, implying the money market rate (iM) is determined based on the policy rate with some delay. - Historically the BSP used the unsecured IBCL rate as primary indicator; with variable-rate RRP auctions in September 2023, the model adopts the overnight RRP rate as its principal market rate. ### Foreign reserves management - The central bank in the model can manage FX reserves through FX interventions aimed at influencing conjunctural outcomes (including responses to disorderly market conditions or excessive exchange rate volatility) and/or by conducting systematic reserve accumulation to build stocks. - Sterilized intervention variable fxi_rrr is modeled as a rule-based intervention (eq.48) with persistence and responses to: - exchange rate misalignments arising from current account deficits or real exchange rate overvaluation (second and fourth terms), - an interest rate differential (third term), - money market disruptions reflected in the country risk premium, - and a discretionary FXI shock (εrfxi_rrr) allowing ad-hoc interventions. - Reserve accumulation toward a target: - The central bank uses FX accumulation (fxa_rrr) to steer foreign reserves toward an exogenous target relative to monthly imports; accumulation is influenced by the disparity between actual FX reserves and the targeted threshold (eq.49). - Drawing from historical ARA-metric data, since 2008 the ARA reserve ratio has consistently exceeded 150 percent, indicating a substantial FX reserve position enabling effective response to disorderly market conditions and external imbalances. - Total change in FX reserves is a function of the sum of intervention and accumulation, currency depreciation, and US Fed policy rate. - Targeted FX accumulation and trend are determined by eq.50, which includes terms with the US Fed rate and factors expressed as 1 + ius−1 / 400 and similar 1 + Δd / 400 and 1 + Δgdp / 400 multiplicative adjustments. ### Capital flow management (CFMs) - PAMPh framework includes CFMs to isolate domestic financial markets from global influences; CFMs can stem capital outflows and shield or alleviate real economic repercussions of external shocks. - Two primary types of CFMs (building on Berg et al. (2023)): 1. Administrative restrictions (and in severe instances, outright bans) on cross-border transactions, denoted by (τr CCFM,T fmiI), which diminish sensitivity of endogenous capital flows or private foreign financing requirements to the UIP premium (prdm�). Implicitly includes limits on banks’ unhedged FX positions or foreign borrowing through administrative CFMs. 2. Capital inflow taxes (τCCFM,focw) that elevate the cost of capital inflows (e.g., requirements to hold a portion of capital inflows as unremunerated reserves), effectively increasing the excess return demanded by investors. - Formal adjustment in the model: - With CFMs, eq.42 is adjusted to eq.51 for Ifp� r: Ifp = h1 · (1 − τr CCFM,T fmiI) · (prdm� r) − (τr CCFM,focw − τCCFM,focw,SSS) − εrcrcm� (eq.51 structure preserved in text). - Corresponding dynamics for IfpSSS and prdm������� are given in eq.52, where CFMs reduce the sensitivity of inflows to the UIP premium and introduce persistence terms (h2, h3). - CFMs reduce market depth and elasticity between risk premium and supply of private foreign financing, and can complement FXI in mitigating nominal exchange rate pressures. ### Macroprudential Policy - Regulatory macroprudential requirements are modeled as a leverage ratio: capital to total assets (not risk-weighted). - Target for leverage ratio (dG_TTTTTT) can be constant or set countercyclically to smooth the credit cycle: - Target rule (eq.53): dG_r rrr,TTTT = ρbG TTTTTT dG r−1,r + (1−ρbG TTTTTT) · (dG_ss,r rrr · rrr) + φ (Gr rrr − Gr ss,rrr) + εbbG r,TTTTT (structure and parameters preserved as in source). - dG_SSS_TTTTTT is the steady state level; ρbG_TTTTTT indicates persistence in setting the instrument; εbbG_TTTTTT is a shock to the target; φ indexes policy countercyclicality. - Two modes: - Passive: set ρbG_TTTTTT = 1 so the target ratio remains constant (no response to credit cycle). - Active: set ρbG_TTTTTT < 1 with nonzero φ to smooth the credit cycle. ### Fiscal Policy - Fiscal policy affects short- and medium-term real economy trajectory, inflationary pressures, and influences monetary policy through multiple channels; PAMPh2.0 distinguishes expenditure categories (current and capital expenditures, financial transfers to households) and assumes a single revenue source. - Expansionary fiscal expenditures typically spur higher economic growth with inflationary pressures, prompting central bank responses; the inflationary impact depends on the instrument (revenue mobilization or tax increases can damp demand and aid debt reduction, reducing country risk premiums and easing medium-term monetary conditions). - Fiscal block objectives and structure: - The fiscal block provides a parsimonious account of government spending, revenue streams, and government spending multipliers; the government's main objective is to achieve and maintain a targeted public debt level. - Total debt target is exogenously given and follows an autoregressive target rule (eq.54): ddebttarget = ρfcb ddebttarget−1 + (1−ρfcb) ddebttarget_SSS + εrfcb (exact notation preserved). - Public debt is decomposed into LCY and FCY parts; a one-year maturity is assumed for both FCY and LCY debt. - Total deficit (dddf) equals the primary deficit (primddf, expenditures minus revenues) plus interest rate cost (iIt Grrt rrr, debt service of FCY and LCY debts) as in eq.55. - Structural and cyclical components: - Primary deficit decomposed into structural (cyclically adjusted) and cyclical parts (eq.56). - Cyclical part of the primary deficit follows a rule linking the budget position to the output gap: ddfGyGl d = −f1 · y� r + εrfcfgypoc (eq.57 structure preserved). - Fiscal rule objectives (eq.58): - Primary objective: stabilize the debt level by responding to deviation from target. - Secondary objective: support real economic activity by smoothing the cyclical position of the economy, allowing higher deficits when output gap is negative. - Fiscal rule includes persistence parameter ρfc, cyclical response f2, and an adjustment for deviation from debt target f3, plus shocks εrfcfsruG. - Fiscal instruments and calibration: - Instruments include revenues (rddI), financial transfers (trr), government current expenditures (gG), and government capital expenditures (gi) with the identity (eq.59): primddf = gG + gi + tr − rddI. - In the current calibration revenues automatically adjust to changes in the primary deficit while other components follow exogenous autoregressive processes; alternative consolidation strategies and fiscal multipliers for different fiscal variables can be explored. - Debt service and term structure: - Debt service distinguishes LCY and FCY interest expenditures; 1-year maturity assumed. - Maturity transformation uses a term structure (eq.60): i4r f = (1 − wG) · (i r + i r+1 + i r+2 + i r+3)/4 + tp rdm f + wG ( r̄ + tp rdm f,ss ) with tp rdm f as the domestic term premium and r̄ the neutral interest rate. - Term premium dynamics (eq.61): tp rdm r f = ρrcrcm tp rdm r−1 f + (1 − ρrcrcm) tp rdm f,ss + g2 · (ddet_r f,rrr − ddet_r f,rrr,ss) + εr rcrcm fcd (structure and parameters preserved). *Source: wpiea2024148-print-pdf - 3.3 Macroeconomic Policies* ### Section 4.1 the impulse response analysis describes the agent’s reaction to shocks and policy measures. As ### wpiea2024148-print-pdf - Section 4.1 the impulse response analysis describes the agent’s reaction to shocks and policy measures. As ### Overview: impulse response and scenario analysis - Impulse response functions describe agents’ reactions to shocks and provide insight into the dynamic properties of the model. - Simulations start from the steady state; figures plot variables as deviations from the initial steady-state equilibrium (a value of zero implies equality with the initial steady state along the Balanced Growth Path (BGP)). - Scenarios illustrated: US Fed tightening (with and without FX intervention and CFMs), international food price shock linked to El Niño, private consumption shock, public debt consolidation, asset price increase and credit boom, risk appetite shock, and minimum wage increase shock. ### 4.1.1 US Fed Monetary Tightening - Shock: additional 100 basis point increase in the US Fed funds rate in the first quarter of 2024. - Effects: - Narrower foreign output gap and slowdown in foreign inflation. - Lower foreign demand triggering capital outflows from emerging markets and depreciation pressure on other currencies. - Peso depreciation contributes to imported inflationary pressures. - Policy responses and trade-offs: - Without FXI: BSP raises policy interest rate, causing a contraction in domestic demand over the next four years and returning inflation to target by early 2026. - With FXI: FX intervention can alleviate depreciation pressure, require milder depreciation and lower policy rate increases, and cause a more limited economic contraction. - Risk of FXI: diminishes BSP’s FX reserves and requires adequate sterilization; otherwise decreasing liquidity could compromise BSP control over domestic money market conditions. ### 4.1.2 International Food Price Shock Associated with El Niño - Shock assumption: world food prices surge by 10 percent compared to baseline and remain elevated until 2025; in simulation, the real price of food gap increases by 10 percentage points in the first two quarters of 2024 compared to baseline. - Effects: - Immediate rise in domestic non-core food prices and higher headline inflation. - BSP initiates a tightening cycle to mitigate second-round effects on other inflation components. - Costs of tightening: economic deceleration, negative output gap, reduced consumption, deteriorating terms of trade, disinvestment due to higher funding costs. - Escalating import prices worsen the current account deficit and heighten external financing pressures on risk premiums and the currency. ### 4.1.3 Private Consumption Shock - Shock: domestic households temporarily increase consumption by 1 percentage point in the first quarter of 2024 (modeled via shock to the residual term of the consumption gap equation). - Effects: - Elevated output gap and intensified inflationary pressures, particularly core inflation. - BSP raises policy rate to temper domestic demand and keep inflation near target. - Assuming unchanged foreign interest rates: higher domestic interest rate prompts nominal appreciation of the peso, immediately curbing non-core inflation. - As demand recedes and appreciation persists: non-core inflation remains below target, core and headline inflation fall below target. - Temporary increase in nominal growth and government revenues helps reduce public debt initially. - Import surge worsens external position, heightens risk premiums, and constrains room for monetary policy easing back to original nominal policy rate. ### 4.1.4 Public Debt Consolidation - Context and government target: aim to reduce deficit-to-GDP ratio to below 4 percent and lower public debt to about 56 percent of GDP by 2028. - Scenario: more ambitious consolidation reducing the debt target by 10 percentage points of nominal GDP during 2024, reaching lower debt level by end-2025; debt reduction takes eight quarters to reach the new target. - Policy measures: raising taxes and enhancing revenue mobilization to achieve a primary surplus. - Effects: - Short run: reduction in household disposable income, lower consumption, contraction in output gap. - Medium run: as fiscal balance nears neutrality and monetary policy remains accommodative, output gap temporarily turns positive and inflation rises; central bank raises policy rates to return inflation to target. - External effects: proportional reduction in government foreign liabilities improves external financing position and diminishes risk premiums, generating appreciation pressures on the peso. - Historical note: in 2023 public debt stood at the threshold of 60 percent of GDP, compared to pre-pandemic average of 40 percent of GDP. ### 4.1.5 Asset Price Increase and Credit Boom - Shock: asset prices experience a permanent 10 percent increase (exogenous shock producing a 10 percent permanent deviation from baseline). - Effects: - Credit boom via higher collateral values; outstanding credit to nominal GDP expands; capital adequacy ratio declines, increasing systemic risk exposure. - Two policy scenarios: - Without Macroprudential: - Capital adequacy target unchanged; banks must generate additional capital via higher lending rate margins. - Higher lending rates gradually curb credit boom; two years required to restore capital adequacy ratio to required level. - Monetary policy responds only to threats to price stability. - With Macroprudential: - Increase in target for countercyclical capital buffer; banks meet higher requirements by accumulating capital via higher lending rates and reduced dividend payments. - Tighter lending conditions neutralize asset price shock’s impact on long-term asset prices and mitigate spillovers to real activity. - Monetary policy tightening is unnecessary. - Conclusion: macroprudential policy is more effective than monetary policy for addressing asset price bubbles. ### 4.1.6 Risk Appetite Shocks - Shock: one-time negative 10 percentage point shock to risk appetite (country risk premium), producing an 8 percent nominal exchange rate depreciation in the initial no-FXI scenario. - Effects: - Imported inflationary pressure; BSP tightens policy rate leading to contraction in real activity. - Alternative measures and interactions: - FX intervention (selling FX reserves) can alleviate immediate exchange rate pressure and reduce required policy rate increase, achieving price stability with lower economic cost. - CFMs on cross-border transactions can insulate the economy, complementing FX intervention; in short run CFMs reduce needed FXI, but in medium term administrative CFMs lead to more depreciated nominal exchange rate as prices cannot be fully offset by contraction. - FXI operational note: under FXI simulation, once shock dissipates BSP replenishes FX reserves to restore adequate levels. ### 4.1.7 Minimum Wage Increase Shocks - Shock: minimum wage increase equivalent to year-on-year increase of about 4 percent implemented in Q4 2024 (consistent with historical average minimum wage increase). - Two scenarios: - Scenario 1 (no pass-through): no pass-through of minimum wage increase to rest of labor sector. - Scenario 2 (partial pass-through): partial pass-through to business wages, covering wider labor sector. - Effects common to both: - Increased private consumption boosts domestic demand and generates inflationary pressures. - Higher wages raise marginal costs for firms, possibly inducing additional price pressures if passed to consumers. - Monetary policy responds by gradually tightening to bring inflation back to target. - Comparative impact: - Larger inflationary and macro effects in scenario 2 because wage adjustments cover the entire labor sector rather than only minimum wage recipients. - Model alignment: results broadly align with the Philippines version of the QIPF DSGE-model (IMF 2023c) in magnitudes and signs of impulse responses; standard DSGE results often show consumption and GDP effects close to neutral or negative due to reduced labor demand and intertemporal household consumption adjustments driven by monetary response. *IMF Working Paper — Section 4.1 (PAMPh2.0) — Impulse Response and Scenario Analysis* ### 4.2 Calibration and Empirical Validation of the Model ### 4.2 Calibration and Empirical Validation of the Model ### Calibration: Parameter Categorization and Iterative Process - Calibration objectives: - Produce accurate fit with historical data, accurate forecasting, economic coherence, ability to explain historical events, and consistency of parameter values with econometric estimates. - Ensure model-based assessments and forecasts align with economic intuition and institutional wisdom (trends, cyclical components, economic shocks). - Iterative calibration stages (as implemented for PAMPh2.0): - (i) defining the structure of PAMPh2.0; - (ii) collecting and analyzing data; - (iii) determining parameters to be calibrated; - (iv) iteratively modifying parameter values to meet criteria. - Model development sequence (gradual extensions and periodic re-calibration using Philippine data and BSP experts): - Introduced GDP expenditure-side decomposition → fiscal block → external balance block, endogenous country risk premia, FX intervention, CFM policies → credit channel with macroprudential policies → labor block. - Parameter categorization: - a. Parameters Determining the Steady-State: - Reflect medium- and long-term historical averages (potential growth, nominal ratios in terms of GDP, import shares from Input-Output tables). - Consistent with medium-term policy objectives (inflation targets, reserve requirement ratios, government debt targets) and assessments/expectations of foreign variables (e.g., medium-term Fed interest rate, trading partners’ inflation targets). - b. Parameters Determining the Decomposition Between Gaps and Trends: - Include parameters and standard deviations for filtration and gap-trend decomposition. - Cycles assumed more volatile than trends: lower standard deviation for trends vs cycles; trends more persistent → higher autoregressive coefficients. - Need careful selection of relative sizes for standard deviations and autoregressive coefficients (example: investment volatility may exceed consumption and output gap volatility). - c. Parameters Governing Transmission Mechanisms and Policy Responses: - Structural linkages: strength of interest rate channel, size of fiscal multipliers, responsiveness of monetary policy to inflation. - Values chosen to capture transmission mechanisms and policy responses accurately. ### Calibration as Distinct from Estimation - Calibration process described as distinct from standard Bayesian estimation; implemented via a three-step iterative process (MATLAB with IRIS Toolbox): - i. Examine dynamic properties (impulse responses), set parameter values using institutional knowledge/policymakers’ preferences (e.g., Taylor rule parameters). - ii. Integrate model with multivariate filter to estimate unobserved variables (output gap, GDP component gaps, potential GDP, natural interest rate) and structural shocks from observed time series; reevaluate model behavior and interpretations. - iii. Generate in-sample forecasts for historically observed variables to validate fit and projection of tendencies and turning points. This procedure implicitly maximizes model likelihood similar to Bayesian estimation. - Advantages of calibration: - Incorporates expert views and allows control of coefficient values. - Facilitates flexible treatment of noisy and short incoming data compared to estimation methods. - Model-specific calibrated features and implications: - Coefficients mostly lower than 0.5 for backward-looking terms → suggests low persistence of inflation and real economic data; macro variables more susceptible to short-lived price and commodity shocks. - High volatility leads to relatively low elasticities (e.g., interest rate elasticity in consumption gap; output impact on core inflation) compared to DSGE models. - Stronger income channel reflecting higher share of low-income and Keynesian households. - Calibrated Taylor rule coefficients: inflation deviation reaction = 1.5; output gap reaction = 0.3. - Interest rate smoothing higher than benchmark values → reflects BSP’s gradual changes in monetary policy settings. - Data and evaluation notes: - COVID-19 shock interpreted as a negative level shift increasing GDP trend volatility temporarily; trends returned to pre-pandemic smoothed dynamic after 2020. - Unable to isolate direct effects of CPI-related tax changes in estimation. - Ex-post historical forecast evaluation deferred until at least a year’s worth of PAMPh2.0-based historical quarterly forecasts are collected and more elaborate databases developed. ### Empirical Validation: In-sample Simulations - Empirical setup: - In-sample simulations assume foreign variables are observed; assess model fit to domestic variables. - Simulations use data up to the beginning of each simulation, excluding external outlooks. - Key empirical findings: - Forecasted variables closely align with observed data and capture main turning points. - Model predictions exhibit minimal bias; deviations from actual data remain relatively small and change sign over the forecast horizon (see referenced Table 4.1). - Conditionality caveat: in-sample forecasts are conditional on assumed policy responses; divergence occurs if actual policy differs from assumed policy. - Forecast comparison with Random Walk (RW) benchmark: - RMSE ratio used: PAMPh2.0 RMSE relative to RW RMSE; ratio < 1 indicates PAMPh2.0 outperforms RW. - For most variables and forecasting horizons (including volatile ones like inflation and GDP), the RMSE ratio is consistently better than RW. - Exception: current account deficit — RMSE ratio higher but very close to one, indicating RW may offer slightly better forecasts but cannot significantly outperform PAMPh2.0. Possible explanation: current account contains unmodeled components outside PAMPh2.0 scope. ### Empirical Validation: Historical Interpretation - Methodology: - Use Kalman filter to estimate unobserved variables (gaps and trends) and structural shocks from observed time-series. - Cross-check gap-trend decompositions to ensure consistency with historical interpretation (e.g., output gap’s role in inflation pressures). - Test structural shocks for zero means and absence of autocorrelation (only structural shocks subject to zero means and no autocorrelation requirements). - Uses of model outputs: - Cross-plots to examine co-movements between observed and unobserved variables (e.g., core inflation driven by real marginal cost). - Structural equation analysis to apportion contributions of determinants to outcomes. - Shock decompositions to describe how structural shocks explain historical fluctuations. - Narrative construction: alignment of economic interpretation with filtration outcomes to verify accuracy. #### Inflation (4.2.3.1) - Observed facts: - With three exceptions, Philippine inflation fluctuated in the upper band of the inflation target during the observation period. - Core inflation relatively stable and smooth; food and energy price volatility contributed to headline inflation spikes. - Commodity price shocks pushed headline inflation above the high-end of the target band before the GFC, before COVID-19, and due to the Russia-Ukraine war. - Drivers and decompositions: - Core inflation influenced by real marginal cost (domestic demand pressure, imported inflation, second-round effects of commodity-driven domestic energy and food prices). Major trends in core inflation correspond to changes in real marginal cost. - Real marginal cost for core inflation primarily influenced by output gap fluctuations and spillovers from non-core inflation. - Import prices (weighted average of trading partners' inflation in USD) generally either negatively impacted or remained neutral for core marginal cost, except pre- and post-COVID-19. - Real marginal costs for non-core inflation exhibit much higher volatility driven by international commodity prices and the nominal exchange rate; spikes in cost function predict non-core inflationary pressure. - Shock decomposition insights: - Headline and core inflation deviations from targets are driven by a combination of foreign-originated shocks and financial shocks (e.g., exchange rate depreciation), especially before the GFC, in 2012, and during post-COVID-19 recovery. - Supply shocks (domestic cost-push) contribute but are less predominant. - Demand and fiscal shocks have limited impact on core inflation but align directionally with periods of overheating (pre-GFC) or slowdown (during/after COVID-19). #### Real Economic Activity (4.2.3.2) - GDP growth and potential: - Besides GFC and COVID-19 periods, Philippines’ GDP growth rate fluctuated around 6 percent; model estimates stable and high potential growth. - During COVID-19, growth slowed markedly and potential growth decreased in 2020; economy recovered and continued robust growth thereafter. - Output gap and expenditure-side decomposition: - Output gap is a key determinant of inflationary pressures. - Before GFC: positive output gap after neutral period. - From 2016 until 2020: output gap and inflationary pressures intensified due to permanently loose global and domestic monetary conditions. - Expenditure-side contributions: - Household consumption and net exports play major roles in underlying dynamics. - Investment and government consumption gaps are highly volatile and generate temporary output gap shifts. - Consumption gap: - Captures domestic demand-related inflationary pressures; relatively large weight in GDP. - Historical behavior: - Pre-GFC: consumption exceeded potential, driven by favorable income position, improving net exports, loose foreign monetary conditions. - Post-GFC: consumption near neutral zero level. - 2016–2020: gap increased due to loose monetary conditions and improving income position. - COVID-19: components (except shock) contributed negatively to the gap. - After 2022: loose monetary policy and recovery increased consumption gap to positive values, explaining inflationary pressure in 2023. - Shock decomposition: fluctuations mostly explained by foreign, financial, and monetary policy shocks; many different shocks affected consumption gap and often offset each other. - Investment gap: - Highly volatile; significant portion of cyclical changes explained by own shock. - Limited contribution from portfolio channel or relative prices; timing of government capital expenditures does not correspond with increases in investment gap. - Export and import gaps: - Export gap highly volatile and primarily determined by foreign shocks/foreign demand components; semiconductors assumed to drive exports with resilience to market share fluctuations. - Import gap computed as sum of weighted average of GDP demand-side components and its own shock; export gap plays significant role in imports; large portion of import gap determined by foreign shocks (export/import shocks), with domestic demand components contributing to a lesser degree. #### Fiscal Policy (4.2.3.3) - Debt dynamics: - Before COVID-19: successful debt consolidation reduced national debt from 60 to 40 percent of nominal GDP. - During COVID-19: government financed increased expenditures with debt issuance, raising public debt to 60 percent of GDP (partly due to significant drop in nominal GDP). - Beginning in 2022: fiscal authority consolidated debt level and reduced total deficit. - Deficit decomposition and structural deficit: - Total deficit components align with debt accumulation. - Successful pre-COVID-19 debt consolidation coincided with decreasing debt service and a negative primary deficit (surplus). - Post-COVID-19: primary deficit increased significantly, then gradually decreased from 2022. - Structural deficit (cyclically-adjusted primary deficit) closely tracked primary deficit due to relatively closed output gap; disparity widened during COVID-19 but converged by end of period. - Primary deficit breakdown: - Government steadily augmented revenue via improved revenue mobilization. - Prior to COVID-19: current expenditure and transfer expenditures relatively stable; capital expenditures gradually increased. - COVID-19 response: escalated health-related and additional capital expenditures; phased out gradually from 2022 onward. #### Monetary Policy and Credit Channel (4.2.3.4) - Real exchange rate, country risk premium, and real interest rate trend: - Real Effective Exchange Rate: - Trends followed typical emerging-market patterns pre-GFC; appreciation during great moderation due to strong capital inflows and traded sector expansion. - Trend flattened after 2010 with short-term peso pressures; 2016–2019 nominal depreciation translated into real depreciation. - Estimated country risk premium (from UIP equation) linked to external financing position and current account deficit: - Notable uptick in country risk premium after period of gradual decline following GFC. - Taper tantrum (2016–2019): nominal depreciation driven by capital outflows and elevated country risk premium. - Post-COVID-19 recovery and global monetary tightening: increased pressures on external balances and higher country risk premium. - Real interest rate and real interest rate gap: - Deviation of real interest rate from trend indicates monetary policy tightness/contractionary stance when positive. - Historical stance narrative: - Pre-financial crisis: contractionary policy to stabilize inflation. - Later: neutral and slightly expansionary stance in response to financial turmoil. - From 2015 onward: expansionary stance with negative real interest rate gap (may not have responded sufficiently to increasing country risk premiums), aligned with intensifying inflationary pressures. - COVID-19 onset: BSP reduced interest rates to support activity. - Post-2022 recovery: BSP raised interest rates to align real interest rate with trend and mitigate positive output gap. - Decomposition of real interest rate trend: variability primarily driven by country risk premium and real exchange rate trend. - Monetary policy rule and shocks: - Taylor rule calibrated to reflect historical monetary policy decisions. - Model identifies negative monetary policy shocks during periods when policy deviated from systematic rule (between 2016 and 2018 and in 2022) where accommodative conditions were maintained. - Credit channel and lending: - Private sector credit stock (% of nominal GDP) expanded following GFC, supporting growth; expansion facilitated by accommodative monetary conditions and declining lending rates. - Lending rate decomposition: - Lending rates decreased primarily due to lower government bond rates (related to monetary policy rate) and narrowing credit risk premiums. - Crisis periods (GFC, COVID-19) temporarily increased credit risk premiums. - Beginning in 2022: credit growth slowed and began to decrease due to tightening monetary conditions and rising lending rates, reflecting contractionary policy stance. *Italic: Source — wpiea2024148-print-pdf, section 4.2 "Calibration and Empirical Validation of the Model" from the PAMPh2.0 IMF working paper.* ### 5. Conclusion ### 5. Conclusion ### Modernization of FPAS and institutional commitment - The modernization initiative spearheaded by the BSP through the development of the Forecasting and Policy Analysis System (FPAS), featuring the semi-structural Quarterly Projection Model known as PAMPh2.0, represents a pivotal step towards equipping policymakers with a robust analytical tool for real-time decision-making amidst the intricacies of the macro-financial landscape. - The endeavor is bolstered by collaborative efforts with the IMF and guided by ICD-led technical assistance, signifying the BSP’s commitment to leveraging advanced modeling techniques. - A clear plan guiding activities towards the formal adoption of PAMPh2.0 underscores the BSP’s unwavering commitment to model-based, data-driven decision-making. ### Model features, extensions, and applications - PAMPh2.0 delivers forward-looking projections incorporating: - endogenous monetary and fiscal policies, - macrofinancial linkages, - labor market dynamics, - additional tools such as FX intervention. - Iterative enhancements and extensions foster integrated thinking and coordination among various policy tools within the BSP. - The model has been applied to simulate policy-relevant scenarios; for example, the recent application to simulate the minimum wage increase scenario that could assist policymakers in understanding the various consequences of a wage hike and enriching discussions about risks to the inflation outlook. ### Calibration, historical interpretation, and testing - The calibration process ensured accuracy and reliability by aligning with historical data, maintaining economic coherence, and leveraging institutional insights. - The systematic approach involved incorporating various model extensions, rigorous testing, and discussions among staff, allowing for integration of expert views and flexibility in managing the volatile nature of Philippine data. - Historical interpretation, supported by the Kalman filter, played a pivotal role in: - constructing a coherent economic narrative, - examining variable co-movement, - verifying alignment of economic interpretations with model outcomes. - Impulse response analysis highlighted the structural interpretation of historical data within PAMPh2.0, enabling policy-contingent forecasts and risk assessments and demonstrating adaptability to a range of both fundamental and non-fundamental shocks while accounting for the unique characteristics of the Philippines’ macroeconomics and finance. ### In-sample performance and empirical validation - In-sample simulations offered empirical evidence of the model’s performance, showing effectiveness in capturing key turning points of domestic variables with minimal bias. - The model effectively captured the BSP tightening cycle before and after the COVID-19 periods, aligning consistently with the inflationary pressure. - Deviations from actual data are relatively small and tend to change signs over the forecast horizon, enhancing confidence in the model’s predictive capabilities. - While no formal statistical test was conducted to determine superiority of one calibration over another, empirical exercises were vital for constructing a coherent narrative around the model and validating its accuracy by aligning economic interpretations with model outcomes. ### Policy implications, coordination, and future refinements - Formal adoption of PAMPh2.0: - provides staff and policymakers with a robust tool, - fosters development and institutionalization of coordination among monetary, financial supervision, and macroprudential sectors within the BSP. - The model facilitates: - assessment of business cycles and monetary policy stances, - quantification of policy effects, - understanding of trade-offs, - alignment with long-term objectives. - PAMPh2.0 has proven to be a valuable forecasting and policy analysis tool, especially given the recent surge in inflation. - As PAMPh2.0 becomes the preferred core model for policy deliberation, its dynamic nature enables future refinements to align with evolving theoretical thinking and empirical findings. *IMF Working Paper — A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (PAMPh2.0), 5. Conclusion.* ### Appendix B: Model Equations ### Appendix B: Model Equations ### B.1 Real Aggregate Demand - Consumption gap equation: 퐺퐺̂ 푟푟 =푎푎 1 퐺퐺̂ 푟푟−1 +푎푎 2 퐸퐸 푟푟 퐺퐺̂ 푟푟+1 −푎푎 3 ( 푎푎 4 푟푟̂ 푟푟 퐿퐿 +(1−푎푎 4 )푟푟̂ 푟푟 +푎푎 5 푝푝푟푟푑푑푚푚� 푟푟 ) +푎푎 6 �푦푦� 푟푟 +푎푎 7 � 푥푥 ̅ 푟푟 푟푟푟푟푟푟 100 푟푟푝푝� 푟푟 푋푋 − 푚푚 � 푟푟 푟푟푟푟푟푟 100 푟푟푝푝� 푟푟 푀푀 ��+푎푎 8 푟푟푑푑푚푚횤횤푡푡 � 푟푟 푟푟푟푟푟푟 − 푎푎 9 푟푟푑푑퐼퐼� 푟푟 +푎푎 10 ( 푤푤푟푟� 푟푟 + 푟푟푝푝� 푟푟 퐺퐺푐푐 푟푟푐푐 − 푟푟푝푝� 푟푟 퐶퐶 ) +푎푎 11 휀휀 푟푟 퐼퐼퐺퐺푟푟 � ℎ,푟푟푟푟푟푟 +휀휀 푟푟 퐺퐺 ̂ - Investment gap equation: 횤횤퐼퐼퐼퐼� 푟푟 =푎푎 12 횤횤퐼퐼퐼퐼� 푟푟−1 +푎푎 13 퐸퐸 푟푟 횤횤퐼퐼퐼퐼� 푟푟+1 +푎푎 14 푞푞� 푟푟 −푎푎 15 푟푟푝푝� 푟푟 푖푖퐼퐼퐼퐼 +푎푎 16 푔푔횤횤� 푟푟 푟푟푟푟푟푟 +푎푎 17 휀휀 푟푟 퐼퐼퐺퐺푟푟 � 푓푓,푟푟푟푟푟푟 +휀휀 푟푟 횤횤퐼퐼퐼퐼 � - Tobin-Q equation: 푞푞� 푟푟 =푎푎 18 (퐸퐸 푟푟 푦푦� 푟푟+1 +푎푎 19 퐸퐸 푟푟 푧푧ℎ � 푟푟+1 )−푎푎 20 ( 푎푎 21 푟푟̂ 푟푟 +푎푎 22 푟푟̂ 푟푟 퐿퐿 + ( 1−푎푎 21 −푎푎 22 ) 푝푝푟푟푑푑푚푚� 푟푟 ) +푎푎 23 퐸퐸 푟푟 푞푞� 푟푟+1 - Government consumption gap equation: 푔푔퐺퐺� 푟푟 =푎푎 24 푔푔퐺퐺� 푟푟−1 +휀휀 푟푟 푔푔퐺퐺 � - Export gap equation: 푥푥� 푟푟 =푎푎 25 푥푥� 푟푟−1 +푎푎 26 ( 푟푟푑푑푑푑푟푟� 푟푟 −푎푎 27 푟푟푝푝� 푟푟 푋푋 ) +푎푎 28 푦푦� 푟푟 푤푤 +휀휀 푟푟 푥푥 � - Import gap equation: 푚푚� 푟푟 =푎푎 29 푚푚� 푟푟−1 + (1−푎푎 29 ) ( 푎푎 30 ( 퐺퐺̂ 푟푟 −푎푎 31 푟푟푑푑푑푑푟푟� 푟푟 ) +푎푎 32 푔푔퐺퐺� 푟푟 +푎푎 33 횤횤퐼퐼퐼퐼� 푟푟 + ( 1−푎푎 30 −푎푎 32 −푎푎 33 ) 푥푥� 푟푟 ) +휀휀 푟푟 푚푚 � - Potential GDP growth: 훥훥푦푦� 푟푟 =휌휌 훥훥푦푦 � 훥훥푦푦� 푟푟−1 + ( 1−휌휌 훥훥푦푦 � ) 훥훥푦푦 푆푆푆푆 +휀휀 푟푟 훥훥푦푦 � - Gap and trend identities (selected): - 퐺퐺 푟푟 =퐺퐺̅ 푟푟 +퐺퐺̂ 푟푟 - 푖푖퐼퐼퐼퐼 푟푟 =횤횤퐼퐼퐼퐼����� 푟푟 +횤횤퐼퐼퐼퐼� 푟푟 - Annualized q-o-q growth rates and Y-o-Y definitions for y, G, I, gG, x, m and trend variables as specified (e.g., 훥훥푦푦 푟푟 =(푦푦 푟푟 −푦푦 푟푟−1) x 4; 훥훥4푦푦 푟푟 =푦푦 푟푟 − 푦푦 푟푟−4). - Great ratios (in nominal GDP) and in trends (in nominal trend GDP) identities, e.g.: 100=퐺퐺 푟푟 푟푟푟푟푟푟 +푖푖퐼퐼퐼퐼 푟푟 푟푟푟푟푟푟 +푔푔퐺퐺 푟푟 푟푟푟푟푟푟 +푥푥 푟푟 푟푟푟푟푟푟 −푚푚 푟푟 푟푟푟푟푟푟 - Output gap identity (with components weighted by trend shares and an error term) as specified. ### B.2 Labor Market Block - Nominal Business Wage Phillips Curve: 훥훥푤푤 푟푟 퐵퐵 =휎휎 1 훥훥푤푤 푟푟−1 퐵퐵 + ( 1−휎휎 1 ) 퐸퐸 푟푟 훥훥푤푤 푟푟+1 퐵퐵 +휎휎 2 ( 휎휎 3 퐺퐺̂ 푟푟 −휎휎 4 푤푤푟푟� 푟푟 ) +휎휎 5 휀휀 푟푟 훥훥푤푤 푀푀 +휀휀 푟푟 훥훥푤푤 퐵퐵 - Minimum wage and Average nominal wage: - 푤푤 푟푟 푀푀 =푤푤 푟푟−1 푀푀 +휀휀 푟푟 훥훥푤푤 푀푀 - 푤푤 푟푟 =휎휎 6 푤푤 푟푟 푀푀 +(1−휎휎 6 )푤푤 푟푟 퐵퐵 - Real wage: w푟푟 푟푟 =푤푤 푟푟 −퐺퐺푝푝푖푖 푟푟 퐺퐺푐푐 푟푟푐푐 - Growth of trend real wage: 훥훥푤푤푟푟���� 푟푟 =휎휎 7 훥훥푤푤푟푟���� 푟푟−1 + ( 1−휎휎 7 )( Δ푤푤푟푟 푆푆푆푆 +휎휎 8 (훥훥푦푦� 푟푟 −Δ푦푦 푆푆푆푆 ) ) +휀휀 푟푟 훥훥푤푤푟푟 ──── - Okun’s law (unemployment rate gap): 푢푢퐼퐼푑푑� 푟푟 =휎휎 9 푢푢퐼퐼푑푑� 푟푟−1 +휎휎 10 푦푦� 푟푟−1 +휀휀 푟푟 푢푢퐼퐼𝑐𝑐 � - NAIRU (non-accelerating rate of unemployment): 푢푢퐼퐼푑푑����� 푟푟 =휎휎 11 푢푢퐼퐼푑푑����� 푟푟−1 + ( 1−휎휎 11 ) �푢푢퐼퐼푑푑 ����� 푠푠푠푠 −휎휎 12 ( Δ푦푦� 푟푟 −Δ푦푦 푆푆푆푆 ) �+휀휀 푟푟 푢푢퐼퐼𝑐𝑐 ������ - Gap and trend identities for wages and unemployment; annualized q-o-q and Y-o-Y growth rate definitions for wages as specified. ### B.3 Banking Sector and Credit Block - Outstanding credit decomposition: 퐺퐺푟푟 푟푟 푟푟푟푟푟푟 =퐺퐺푟푟 푟푟 ℎ,푟푟푟푟푟푟 +퐺퐺푟푟 푟푟 푓푓,푟푟푟푟푟푟 - Households’ and Firms’ outstanding credit dynamics: - Households: 퐺퐺푟푟 푟푟 ℎ,푟푟푟푟푟푟 = ( 1−훿훿 ℎ ) 퐺퐺푟푟 푟푟−1 ℎ,푟푟푟푟푟푟 / exp�훥훥푔푔푑푑푝푝 ����� 푟푟 퐼퐼𝑐𝑐푚푚 /400�+퐼퐼퐺퐺푟푟 푟푟 ℎ,푟푟푟푟푟푟 +휀휀 푟푟 퐺퐺푟푟 ℎ,푟푟푟푟푟푟 - Firms: 퐺퐺푟푟 푟푟 푓푓, 푟푟푟푟푟푟 = ( 1−훿훿 푓푓 ) 퐺퐺푟푟 푟푟−1 푓푓,푟푟푟푟푟푟 / exp�훥훥푔푔푑푑푝푝 ����� 푟푟 퐼퐼𝑐𝑐푚푚 /400�+퐼퐼퐺퐺푟푟 푟푟 푓푓,푟푟푟푟푟푟 +휀휀 푟푟 퐺퐺푟푟 푓푓,푟푟푟푟푟푟 - Total new credit: 퐼퐼퐺퐺푟푟 푟푟 푟푟푟푟푟푟 =퐼퐼퐺퐺푟푟 푟푟 ℎ,푟푟푟푟푟푟 +퐼퐼퐺퐺푟푟 푟푟 푓푓,푟푟푟푟푟푟 - Households’ and Firms’ new credit gap and trend decompositions, and demand equations, e.g.: - Households’ new credit gap (demand): 퐼퐼퐺퐺푟푟� 푟푟 ℎ,푟푟푟푟푟푟 =휌휌 퐼퐼퐺퐺푟푟 � ℎ,푟푟푟푟푟푟 퐼퐼퐺퐺푟푟� 푟푟−1 ℎ,푟푟푟푟푟푟 +훼훼 퐼퐼퐺퐺푟푟 � ℎ,푟푟푟푟푟푟 퐺퐺̂ 푟푟 −훽훽 퐼퐼퐺퐺푟푟 � ℎ,푟푟푟푟푟푟 푟푟̂ 푟푟 퐿퐿 +휀휀 푟푟 퐼퐼퐺퐺푟푟 � ℎ,푟푟푟푟푟푟 - Credit interest rate (demand) and real interest rate decomposition: - 푟푟푟푟 푟푟 퐿퐿 =휌휌 푟푟푠푠 퐿퐿 푟푟푟푟 푟푟−1 퐿퐿 +�1−휌휌 푟푟푠푠 퐿퐿 �( 푟푟푟푟 푟푟 1푌푌 +푝푝푟푟푑푑 푚푚 푟푟 퐺퐺 ) +휀휀 푟푟 푟푟푠푠 퐿퐿 - Credit real interest rate: 푟푟 푟푟 퐿퐿 =푟푟푟푟 푟푟 퐿퐿 −훥훥 4 퐺퐺푝푝푖푖 푟푟+4 - Market Risk Premium of Credit, borrower default-risk, asset prices, total assets of commercial banks, capital accumulation, return on assets, lending rate spread, dividend to total assets, total asset target, credit costs, total assets in terms of nominal GDP, and q-o-q growth rates as specified with exact functional forms and parameters. ### B.4 Aggregate Supply - Headline CPI aggregation and component shocks: - 퐺퐺푝푝푖푖 푟푟 =푤푤 퐺퐺푐푐 푖푖,푓푓푐푐푐푐푓푓 퐺퐺푝푝푖푖 푟푟 푓푓푐푐푐푐푓푓 +푤푤 퐺퐺푐푐 푖푖,푐푐퐼퐼𝑐𝑐푟푟푔푔푦푦 퐺퐺푝푝푖푖 푟푟 푐푐퐼퐼𝑐𝑐푟푟푔푔푦푦 + ( 1−푤푤 퐺퐺푐푐 푖푖,푓푓푐푐푐푐푓푓 −푤푤 퐺퐺푐푐 푖푖,푐푐퐼퐼𝑐𝑐푟푟푔푔푦푦 ) 퐺퐺푝푝푖푖 푟푟 퐺퐺푐푐 푟푟푐푐 +휖휖 푟푟 퐺퐺𝑐𝑐 푖푖 - Core inflation Phillips-curve and real marginal cost of core inflation: - 훥훥퐺퐺푝푝푖푖 푟푟 퐺퐺𝑐𝑐 푟푟푐푐 =푑푑 1 훥훥퐺퐺푝푝 푖푖 푟푟−1 퐺퐺𝑐𝑐푟푟푐푐 + ( 1−푑푑 1 ) 퐸퐸 푟푟 훥훥퐺퐺푝푝 푖푖 푟푟+1 퐺퐺𝑐𝑐 푟푟푐푐 +푟푟푚푚 퐺퐺 푟푟 퐺퐺𝑐𝑐 푟푟푐푐 +휀휀 푟푟 훥훥퐺퐺𝑐𝑐𝑖𝑖 푐푐𝑐푐 푟푟푐푐 - Real marginal cost: 푟푟푚푚 퐺퐺 푟푟 퐺퐺𝑐𝑐 푟푟푐푐 =푑푑 2 푦푦� 푟푟 +푑푑 3 ( 푟푟푑푑푑푑푟푟� 푟푟 −푟푟푝푝� 푟푟 퐺퐺𝑐𝑐 푟푟푐푐 ) +푑푑 4 ( 푟푟푝푝� 푟푟 푐푐퐼퐼𝑐𝑐푟푟푔푔푦푦 −푟푟푝푝� 푟푟 퐺퐺𝑐𝑐 푟푟푐푐 ) +푑푑 5 �푟푟푝푝� 푟푟 푓푓푐푐푐푐푓푓 −푟푟푝푝� 푟푟 퐺퐺𝑐𝑐 푟푟푐푐 �+푑푑 6 푤푤푟푟� 푟푟 - Food and Energy inflation Phillips-curves, relative price definitions, trend-gap decompositions, implicit inflation targets for core, food, energy CPI, and q-o-q and Y-o-Y change definitions for CPIs and relative prices as specified. ### B.5 GDP Deflators - Consumption deflator measurement: 훥훥푝푝 푟푟 퐶퐶 =훥훥퐺퐺푝푝 푖푖 푟푟 +훥훥푟푟푝푝��� 푟푟 퐶퐶 −푑푑 1 푟푟푝푝� 푟푟 퐶퐶 +휀휀 푟푟 훥훥푐푐 퐶퐶 - Investment, Government consumption, Export, Import deflator Phillips-curves and their real marginal cost specifications (d2–d18 coefficients), with error terms and exact functional forms as provided. - Relative price gap and trend of GDP deflator: - 푟푟푝푝� 푟푟 퐺퐺퐺퐺푅푅 = 퐺퐺̅ 푟푟 푟푟 푟푟푟푟 100 푟푟푝푝� 푟푟 퐶퐶 +... +휀휀 푟푟 푟푟푐푐 � 퐺퐺퐺퐺퐺퐺 - Nominal GDP identity and trend definitions: - 푔푔푑푑 푝푝 푟푟 퐼퐼𝑐𝑐푚푚 =푦푦 푟푟 +푝푝 푟푟 퐺퐺퐺퐺푅푅 - 훥훥푔푔푑푑 푝푝 푟푟 퐼퐼𝑐𝑐푚푚 =(푔푔푑푑 푝푝 푟푟 퐼퐼𝑐𝑐푚푚 −푔푔푑푑 푝푝 푟푟−1 퐼퐼𝑐𝑐푚푚 ) x 4 - Relative price trends of component deflators (consumption, investment, government, export, import) follow AR(1) trend specifications with shock terms as provided. ### B.6 Real Interest Rate and Exchange Rate - Real interest rate identity: 푟푟 푟푟 =푖푖 푟푟 − [ 휃휃훥훥 4 퐺퐺푝푝푖푖 푟푟 + ( 1−휃휃 ) 퐸퐸 푟푟 훥훥 4 퐺퐺푝푝푖푖 푟푟+4 ] - Real exchange rate (PPP) identity: 푟푟푑푑푑푑푟푟 푟푟 =푟푟 푟푟 + 퐺퐺푝푝푖푖 푟푟 ∗ −퐺퐺푝푝푖푖 푟푟 - Real exchange rate trend, gap, Q-o-Q change of real exchange rate trend, RUIP condition, real interest rate gap, bilateral real exchange rate gap and trend vis-à-vis USD, effective foreign price in USD, implicit depreciation trend (PPP with trends), annual growth of nominal exchange rate trend, and conversion formulas exactly as specified. ### B.7 Balance of Payments - Balance of payments identity (level and trend): 0 =퐺퐺푎푎 푟푟 푟푟푟푟푟푟 +푓푓푎푎 푟푟 푟푟푟푟푟푟 - Current account and its decomposition: - 퐺퐺푎푎 푟푟 푟푟 푟푟푟푟 =퐼퐼푥푥 푟푟 푟푟 푟푟푟푟 +푟푟푑푑 푚푚푖푖푡푡 푟푟 푟푟 푟푟푟푟 −푖푖퐼퐼𝑡𝑡 퐺퐺푟푟푟푟푡푡 푟푟 푓푓,푟푟 푟푟푟푟 +푟푟푡푡ℎ 푟푟 푟푟 푟푟푟푟 - Net-export, remittances (total, trend, gap), other foreign net income (level, trend, gap), financial account gap, private sector net foreign financing position and trend, foreign credit supply (gap and trend) with structural parameters and shocks as specified. ### B.8 Monetary Policy - Taylor-rule (policy rate setting): 푖푖 푟푟 푐푐푐푐표표 =훾훾 1 푖푖 푟푟−1 푐푐푐푐표표 + ( 1−훾훾 1 )( 푟푟̅ 푟푟 +퐸퐸 푟푟 Δ퐺퐺푝푝푖푖 푟푟+1 푇푇푇푇푇푇 +훾훾 2 ( 퐸퐸 푟푟 Δ 4 퐺퐺푝푝푖푖 푟푟+2 −퐸퐸 푟푟 Δ퐺퐺푝푝푖푖 푟푟+2 푇푇푇푇푇푇 ) +훾훾 3 푦푦� 푟푟 ) +휀휀 푟푟 푖푖 푝푝푐푐 푝푝 - Market rate, UIP condition, nominal exchange rate expectation, country risk premium decomposition, FXI-rule and FXA-rule for FX interventions, FX-reserve target and accumulation, FX-reserve gap and adequacy ratio definitions, administrative and price-based CFM (capital flow measures) AR(1) specifications, and Q-o-Q and Y-o-Y changes of nominal exchange rate as specified. ### B.9 Fiscal Policy - B.9.1 Government Expenditures - Total expenditures without debt service: 푑푑푥푥 푟푟 푟푟푟푟푟푟 =푔푔 푟푟 푟푟푟푟푟푟 +푔푔푖푖 푟푟 푟푟푟푟푟푟 +푡푡푟푟 푟푟 푟푟푟푟푟푟 - Central/local decomposition, local government current expenditures AR(1), government capital expenditures AR(1), government financial transfers AR(1), trend constructions (4-quarter averages) and expenditure gaps definitions. - Total debt service and LCY/FCY debt service detailed exponential formulas for rollover and coupon accruals, 1Y LCY and FCY interest rates and term-premia definitions, new debt issuance rules with share θr and issuance smoothing, targeted issuance and targeted debt service specifications. - B.9.2 Government Revenues - Total revenues from primary and structural deficits, total revenue gap and trend (4-quarter averages) as specified. - B.9.3 Debt Accumulation - Total public debt decomposition (LCY and FCY), accumulation formulas for 1Y LCY and FCY debt including inflation/linkers/nominal exchange rate effects, and new issuance rules. - B.9.4 Fiscal Anchor, Deficits and Reaction Functions - Government deficit identity, primary deficit composition, cyclical primary deficit, cyclically adjusted primary deficit AR(1) with adjustments for debt deviation, share of LCY financing AR(1), debt deviation definition. - B.9.5 Fiscal Targets - Public debt target (AR(1) around a steady-state path), LCY and FCY public debt target buildup formulas, LCY targeted new issuance and FCY targeted new issuance rules, targeted total debt service and LCY/FCY targeted debt service exponential constructions, LCY and FCY long-term interest rate definitions, and primary deficit target equality with targeted debt service. ### B.10 Foreign Variables - Relative prices (oil, food) definitions (foreign price minus US CPI), trend-gap decompositions and AR(1) trend dynamics for relative price components and their q-o-q/Y-o-Y computations. - US CPI Phillips curve and q-o-q/Y-o-Y definitions: - 훥훥퐺퐺푝푝푖푖 푟푟 푈푈푆푆 =푘푘 1 훥훥퐺퐺푝푝 푖푖 푟푟−1 푈푈푆푆 + ( 1−푘푘 1 ) 훥훥퐺퐺푝푝푖푖 푈푈푆푆,푠푠푠푠 +휀휀 푟푟 훥훥퐺퐺𝑐𝑐푖푖 푈푈푈푈 - US FED policy rate rule (AR(1) with target real rate and implicit US CPI path), US real interest rate identity, US GDP gap AR(1), US real interest rate gap and trend AR(1). - Other trading partners (set j in {EEE, CCC, JPR, RC}) dynamics for output gap, inflation, real exchange rate and effective foreign demand gap aggregation with weights for external demand. Italic: Source: Appendix B, "Model Equations" (PAMPh2.0) ### Appendix C: Parameters ### Appendix C: Parameters ### Document identification and headings - "A Monetary and Financial Policy Analysis and Forecasting Model for the Philippines (PAMPh2.0)" - "IMF WORKING PAPERS" - "INTERNATIONAL MONETARY FUND" - "Working Paper No. WP/2024/148" ### Page references appearing in the source content - Page numbers: 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113 *Source: wpiea2024148-print-pdf - Appendix C: Parameters* --- _Source: https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024148-print-pdf.pdf_