## Section 1 — Introduction and Abstract (WP/20/201)

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### Abstract and paper identification
- WP/20/201; © 2020 International Monetary Fund.
- Title: The Monetary Policy Credibility Channel and the Amplification Effects in a Semi-structural Model.
- Authors: Thitipat Chansriniyom, Natan Epstein, and Valeriu Nalban.
- Authorized for distribution by Andrew Berg and Stephan Danninger; September 2020.
- JEL Classification Numbers: C53, E47, E52, E58.
- Keywords: Monetary policy credibility; Inflation expectations; Inflation targeting; Indonesia; Philippines.
- Authors’ E-Mail Address: tchansriniyom@imf.org; nepstein@imf.org; vnalban@imf.org.

- Abstract — core points:
  - Extends a standard semi-structural model to account for nonlinear and asymmetric effects of monetary policy (MP) credibility.
  - Central bank credibility is modeled as proportional to the deviation of inflation expectations from the announced inflation target, with positive deviations being more costly compared to negative ones.
  - A loss in policy credibility (from shocks) leads to a more persistent, backward-looking inflation process and is associated with lower output.
  - The extended model with credibility effects matches key macroeconomic data for Indonesia and Philippines over specific past episodes.
  - Suggests further exploration of adapting the model to integrated policy frameworks.

### Motivation, conceptual framing, and literature positioning
- Policy credibility is central to the efficacy of monetary policy and the transmission mechanism, especially during significant shocks.
- Standard linear semi-structural new-Keynesian models used by inflation-targeting central banks may not capture effects of large shocks or policy decisions that affect MP credibility.
- MP credibility proxied by deviations of inflation expectations from an announced numerical inflation target (following Svensson 1997).
- Two literature strands:
  - Theoretical: imperfect information and learning frameworks where credibility affects speed of learning and transmission (examples cited in source).
  - Empirical: measures of MP credibility via observable data and survey-based inflation expectations; asymmetric effects of above-target versus below-target expectations considered.
- Broader interpretation: credibility linked with institutional features — independence, communications, transparency, accountability — that determine anchoring of inflation expectations.

### Key model innovations and mechanisms
- Implements a nonlinear MP credibility channel within a standard gap model:
  - Inflation expectations channel: augmented Phillips curve with time-varying weights on backward- and forward-looking components proportional to public expectations of CB’s ability to meet its inflation objective.
  - Credibility modeled as deviation of inflation expectations from target; low credibility → more persistent, backward-looking inflation dynamics → requires aggressive and timely CB action.
- Models direct effects of MP credibility on aggregate demand:
  - Loss of credibility erodes confidence and raises uncertainty → negatively impacts output via lower private spending.
  - Gains in credibility raise confidence and positively impact output.
- Captures asymmetric effects of shocks:
  - Credibility falls more and faster for positive deviations of inflation expectations from the target than for negative deviations.
  - Timing matters: delayed policy responses can erode credibility and de-anchor expectations, raising stabilization costs.

### Empirical approach and case studies
- Case studies: Indonesia and Philippines.
- Shock scenarios replicate historical episodes from policymaker perspective:
  - Indonesia: “taper tantrum” episode in mid-2013.
  - Philippines: inflation-acceleration episode in 2018.
- Illustrative simulation findings:
  - Extended model reproduces Indonesia’s macro and policy developments after mid-2013, matching the 2013 aggressive interest rate tightening cycle where inflation expectations overshot the upper limit of the inflation target range.
  - For the Philippines, extended model reproduces observed macro developments during the 2018 inflation acceleration episode more accurately than the standard linear model.

### Policy relevance and implications
- Modeling credibility via inflation expectations deviations is especially relevant for inflation-targeting frameworks because targets are numerical and observable.
- Emerging markets typically have weaker anchoring of inflation expectations compared to advanced economies, increasing importance of credibility considerations.
- Integrated policy frameworks (IPFs) and hybrid regimes with multiple objectives and instruments add complexity and opacity that can amplify credibility effects and complicate management.
- Emphasis on:
  - Importance of timely and credible CB actions to avoid second-round inflationary spirals and costly stabilization.
  - Credibility affecting both expectations channel (anchoring expectations) and interest rate channel (magnitude of interest rate changes needed to restore equilibrium).

*Source: IMF Working Paper WP/20/201 (Section 1 of wpiea2020201-print-pdf).*

### Section 1

### Section 1 — Introduction and Abstract (WP/20/201)

### Abstract and paper identification
- WP/20/201; © 2020 International Monetary Fund.
- Title: The Monetary Policy Credibility Channel and the Amplification Effects in a Semi-structural Model.
- Authors: Thitipat Chansriniyom, Natan Epstein, and Valeriu Nalban.
- Authorized for distribution by Andrew Berg and Stephan Danninger; September 2020.
- JEL Classification Numbers: C53, E47, E52, E58.
- Keywords: Monetary policy credibility; Inflation expectations; Inflation targeting; Indonesia; Philippines.
- Authors’ E-Mail Address: tchansriniyom@imf.org; nepstein@imf.org; vnalban@imf.org.

- Abstract — core points:
  - Extends a standard semi-structural model to account for nonlinear and asymmetric effects of monetary policy (MP) credibility.
  - Central bank credibility is modeled as proportional to the deviation of inflation expectations from the announced inflation target, with positive deviations being more costly compared to negative ones.
  - A loss in policy credibility (from shocks) leads to a more persistent, backward-looking inflation process and is associated with lower output.
  - The extended model with credibility effects matches key macroeconomic data for Indonesia and Philippines over specific past episodes.
  - Suggests further exploration of adapting the model to integrated policy frameworks.

### Motivation, conceptual framing, and literature positioning
- Policy credibility is central to the efficacy of monetary policy and the transmission mechanism, especially during significant shocks.
- Standard linear semi-structural new-Keynesian models are widely used by inflation-targeting central banks but may not capture effects of large shocks or policy decisions that affect MP credibility.
- MP credibility is often proxied by deviations of inflation expectations from an announced numerical inflation target (following Svensson 1997).
- Two main strands in the literature:
  - Theoretical: imperfect information and learning frameworks (examples cited: Cukierman and Meltzer 1986; Bomfim and Rudebusch 2000; Erceg and Levin 2003; Adler et al. 2019) where agents may learn about CB rules or observe multiple instruments; credibility affects speed of learning and transmission.
  - Empirical: attempts to measure MP credibility via observable data (examples cited: Laxton and N’Diaye 2002; Levieuge et al. 2018; de Mendonça and de Guimarães e Souza 2009; Carriere-Swallow et al. 2016; Dincer and Eichengreen 2014). Survey-based inflation expectations are frequently used to compute credibility indices; asymmetric effects of above-target versus below-target expectations are considered.

- Broader interpretation: MP credibility is linked with institutional features — independence, communications, transparency, accountability — that determine the degree to which inflation expectations are anchored (see Adrian et al. 2018; Unsal 2020).

### Key model innovations and mechanisms
- Implements a nonlinear MP credibility channel within a standard gap model:
  - Inflation expectations channel: an augmented Phillips curve where weights on backward- and forward-looking components are time-varying and proportional to public expectations of the CB’s ability to meet its inflation objective over the policy horizon (building on Argov et al. 2007).
  - Credibility modeled as deviation of inflation expectations from target; when credibility is low, inflation dynamics are more persistent and backward-looking, requiring aggressive and timely CB action.
- Novel contribution: models the effects of MP credibility directly on aggregate demand:
  - Loss of credibility erodes confidence and raises uncertainty → negatively impacts output via lower private spending.
  - Gains in credibility raise confidence and positively impact output.
- Captures asymmetric effects of shocks:
  - Credibility falls more and faster for positive deviations of inflation expectations from the target than for negative deviations (positive surprises are costlier than negative ones).
  - Timing matters: delayed policy responses can erode credibility and de-anchor expectations, raising costs of subsequent stabilization.

### Empirical approach and case studies
- Case-study applications to two inflation-targeting countries in Asia: Indonesia and Philippines.
- Shock scenarios replicate historical episodes from the policymaker’s perspective:
  - Indonesia: “taper tantrum” episode in mid-2013.
  - Philippines: inflation-acceleration episode in 2018.
- Main empirical findings (illustrative evidence from simulations):
  - The extended model with the nonlinear credibility channel provides an amplification effect and reproduces Indonesia’s macro and policy developments after the mid-2013 episode; simulations match the 2013 aggressive interest rate tightening cycle where inflation expectations overshot the upper limit of the inflation target range.
  - For the Philippines, simulations suggest the extended model with credibility reproduces observed macroeconomic developments during the 2018 inflation acceleration episode more accurately than the standard linear model.

### Policy relevance and implications
- For inflation-targeting frameworks, modeling credibility via inflation expectations deviations is especially relevant because targets are numerical and observable.
- Emerging markets typically have weaker anchoring of inflation expectations compared to advanced economies (reference: IMF 2018a), increasing the importance of credibility considerations.
- Integrated policy frameworks (IPFs) and hybrid monetary policy regimes, with multiple objectives and instruments, add complexity and opacity that can make credibility effects more important and harder to manage; the paper highlights the need to consider credibility spillovers on monetary transmission in such frameworks.
- The analysis emphasizes:
  - The importance of timely and credible CB actions to avoid second-round inflationary spirals and costly stabilization.
  - That credibility affects both the expectations channel (anchoring expectations) and the interest rate channel (degree of interest rate changes needed to restore equilibrium).

*Source: IMF Working Paper WP/20/201 (Section 1 of wpiea2020201-print-pdf).*

### Section 2

### Section 2 — THE MODEL

### Overview
- The extended semi-structural model builds on the standard linear quarterly projection models (QPMs, “gap models”) used in inflation-targeting central banks.
- The extension introduces an endogenous monetary policy (MP) credibility channel, affecting formation of inflation expectations, exchange rate dynamics, and aggregate demand.
- The model comprises a domestic block (four main equations) and an exogenous foreign economy block.
- Four main domestic equations: aggregate supply (Phillips curve), aggregate demand, uncovered interest parity (UIP), and a monetary policy rule.

### Aggregate Supply (Phillips curve)
- Quarterly annualized CPI inflation rate (휋휋t) is given by (equation (1)):
  - 휋휋t = 훼훼1 휋휋4 t e + (1−훼훼1) 휋휋4 t−1 + 훼훼2 [0.5 yt + 0.5 yt−1 ] + 훼훼3 [zt − zt∗] + 훼훼4 휋휋휋휋휋휋휋휋 t + εtπ
  - εtπ is a cost-push / aggregate supply shock.
- Expected four-quarter inflation (휋휋4 t e) formation (equation (2)):
  - 휋휋4 t e = [γt / 2] Et[휋휋4 t+4 ] + [1 − γt / 2] 휋휋4 t−1 + μb bb t + εtπe
  - Standard linear QPM: replace γt / 2 by 0.5 and set μb = 0.
- The credibility stock γt ∈ [0,1] adjusts the weight between forward- and backward-looking components; γt = 1 restores standard linear model with equal weights (0.5).
- Credibility stock dynamics (equation (3)):
  - γt = ργ γt−1 + [1 − ργ] λt−1 + εtγ
  - εtγ is an exogenous component.
- Credibility build-up λt (nonlinear, asymmetric; equations (4)–(6)):
  - λt = (휋휋4 t H − 휋휋4 t)2 / [(휋휋4 t H − 휋휋4 t)2 + (휋휋4 t L − 휋휋4 t)2]
  - Regime-specific inflation processes:
    - 휋휋4 t L = ρL 휋휋4 t−1 L + (1 − ρL) 휋휋̄ L
    - 휋휋4 t H = ρH 휋휋4 t−1 H + (1 − ρH) 휋휋̄ H
  - Interpretation:
    - “L” regime converges to announced target 휋̄L = 휋∗; when met, λt → 1 and γt → 1 (credibility builds).
    - “H” regime converges to higher level 휋̄H > 휋̄L = 휋∗; when realized, λt → 0 and γt declines (credibility loss).
  - Credibility decline is faster for positive deviations above target than for symmetric negative deviations (agents penalize overshooting more than undershooting).
- Inflation expectations bias bt (equation (7)):
  - bt = γt 휋휋4 t e,L + (1 − γt) 휋휋4 t e,H − 휋∗
  - Regime-specific four-quarter ahead expectations:
    - 휋휋4 t e,L = ρL4 휋휋4 t−1 + (1 − ρL) ∑(ρL i) 휋̄L (i=0..3)
    - 휋휋4 t e,H = ρH4 휋휋4 t−1 + (1 − ρH) ∑(ρH i) 휋̄H (i=0..3)
  - Implications:
    - Full credibility (γt = 1) ⇒ bt → 0; expectations anchored at target.
    - Zero credibility (γt = 0) ⇒ bt equals difference between H-regime expectations and the announced target (positive).

### Aggregate Demand
- Output gap equation (equation (10)):
  - yt = β1 Et[yt+1] + β2 yt−1 − β3 (RRt−1 − RRt−1∗) + β4 (zt−1 − zt−1∗) + β5 y t u s − β6 Δbt + εt y
  - RR denotes real interest rate; z is log real exchange rate; y t u s is foreign (US) demand.
- Novel channel: inflation expectations bias bt enters directly (via −β6 Δbt), linking MP credibility to the business cycle:
  - Deterioration of CB credibility increases weight on high-inflation regime → upward bias in expectations → greater uncertainty → depressed economic sentiment → lower spending and reduced output.
- The β6 coefficient captures the confidence/uncertainty channel; robustness checks compare simulations with β6 = 0.

### Uncovered Interest Parity (UIP)
- UIP condition (equation (11)):
  - zt = zt e − [ (RRt − RRt u s − ρt∗) / 4 ] + εt z
  - zt e is expected RER; RRt and RRt u s are real domestic and foreign (US) interest rates; ρt∗ is sovereign risk premium; εt z captures UIP deviations.
- RER expectations specification (equation (12)):
  - zt e = δ1 Et[zt+1] + (1 − δ1) zt−1 + δ2 bt + δ3 Δbt
  - Motivation: strengthen feedbacks among inflation, expectations, and RER dynamics; capture empirical association in emerging markets between loss of MP credibility, elevated uncertainty, and currency depreciation.

### Monetary Policy Rule
- Standard Taylor rule (equation (13)):
  - RRt = γ1 RRt−1 + (1 − γ1)∗ [ RRt∗ + 휋4 t ] + γπ Et[휋4 t+4 − 휋t+4∗] + γy y t + εt RR
  - Nominal interest rate set as function of lagged rate, neutral rate (RRt∗ + 휋4 t), expected four-quarter ahead inflation deviation from target, output gap, and idiosyncratic shock εtRR.

### Model Calibration (to an emerging market economy)
- General:
  - Calibrated to an emerging market; parameters set to fit Indonesia and Philippines characteristics for application in Section III.
  - Parameter choices follow relevant semi-structural and DSGE literature; marginal changes do not alter main conclusions.
- Phillips curve:
  - 훼1 = 0.5 (equal weights on future and past inflation)
  - 훼2 = 0.3 (aggregate demand effect)
  - 훼4 = 0.05 (oil price effect)
  - 훼3 (exchange rate passthrough) is set two times lower in Philippines relative to Indonesia.
- Inflation expectations formation:
  - μb = 0.15 (multiplier on inflation expectations bias; same as Argov et al. (2007) for Israel)
  - Credibility persistence ργ set such that half-life ≈ 2.5 quarters.
- Regime steady-states:
  - Low-inflation stationary levels set to announced inflation targets:
    - Indonesia: 4.5 percent (in 2013)
    - Philippines: 3 percent (in 2018)
  - High-inflation regime steady-state: 10 percent
    - Rationale: historical spikes and sub-sample averages (e.g., Indonesia average annual CPI inflation 9.5 percent in 2001-2008; maximum 17.8 percent in 2005Q4).
  - Persistence: ρH > ρL (H-regime has more inertia).
- RER expectations:
  - δ1 = 0.5 (equal weights on backward- and forward-looking components)
  - δ2 and δ3 (bias coefficients) are 50 percent higher in Philippines relative to Indonesia.
- Aggregate demand parameters:
  - β2 > β1 (more weight on past than expected output gap)
  - β3, β4, β5 set at moderate magnitudes; β4 set relatively low due to partial dollarization and counteracting effects of RER depreciation.
  - β6 (inflation bias impact on output) set to:
    - Indonesia: 0.25
    - Philippines: 0.5
  - Simulations compare extended model responses to counterfactuals with β6 = 0 to assess confidence-channel contribution.
- Monetary policy rule:
  - Interest rate smoothing γ1 = 0.5 (lower smoothing than advanced economies)
  - Conventional coefficients for expected inflation and output gap terms.

*Source: wpiea2020201-print-pdf — Section 2*

### Section 3

### III. MODEL APPLICATIONS TO INDONESIA AND PHILIPPINES

### Model calibration (Table 1)
- Phillips curve “L” and “H” regimes, Aggregate demand, Taylor rule parameters:
  - 훼1 = 0.5
  - ρL = 0.4
  - 훽1 = 0.2
  - 훾1 = 0.5
  - 훼2 = 0.3
  - π̂L = 4.5 / 3
  - 훽2 = 0.8
  - 훾π = 1.5
  - 훼3 = 0.25 / 0.125
  - ρH = 0.8
  - 훽3 = 0.15
  - γy = 0.2
  - 훼4 = 0.05
  - π̂H = 10
  - 훽4 = 0.05
  - 훽5 = 0.1
  - μbb = 0.15
  - δ1 = 0.5
  - 훽6 = 0.25 / 0.5
  - Credibility:
    - δ2 = 0.1 / 0.15
    - ργ = 0.75
    - δ3 = 0.5 / 0.75
- Note: cells with two values indicate calibrated values for Indonesia and for Philippines, respectively.

### General experimental design and data
- Approach:
  - Case study experiments tailored to two episodes: the “taper tantrum” in mid-2013 for Indonesia, and inflation-acceleration during 2018 for Philippines.
  - Simulate counterfactual dynamic economic responses (impulse response functions, IRFs) to calibrated shocks.
  - Shocks calibrated using higher frequency data (e.g. monthly government bond yield spreads).
  - Model steady state normalized to actual data observations in the quarter prior to shocks’ occurrence.
- Observed variables used for comparison:
  - Policy interest rate (end-of-period)
  - Quarterly annualized CPI inflation rate (seasonally adjusted)
  - Quarterly annualized nominal exchange rate dynamic (NER; an increase indicates depreciation)
  - Output gap obtained as the band-pass filtered real GDP (seasonally adjusted)
  - Model-implied inflation expectations plotted (not directly comparable to survey measures due to differing information sets and horizons).
- Model limitations noted:
  - Only interest rate modeled as policy instrument (real-world CBs also use foreign exchange interventions, macroprudential measures, capital flow measures).
  - Use of revised data and band-pass output gap; real-time data and alternative output gap methods not modeled.

### A. Indonesia — “taper tantrum” (May–September 2013)
- Context and observed facts:
  - May 2013 US Federal Reserve announcement signaled slowdown in future bond purchases.
  - Over May-September 2013 Indonesia experienced:
    - Increase of about 300 basis points in its 1-year bond yield spread
    - Rapid depreciation of the rupiah (between 5 percent and 15 percent annually)
    - Acceleration of inflation to 8 percent annually in 2013Q3
  - Inflation target upper band: 4.5 percent +/–1 percentage point
  - Consensus Forecasts: analysts revised inflation expectations for 2013 and 2014 from 5.6 percent and 5 percent in May to 7.3 percent and 6.4 percent in September, respectively.
  - Three-year ahead inflation expectations did not breach 5.5 percent upper limit, but one- and two-year expectations increased markedly from mid-2013.
- Simulation scenario:
  - Impose a UIP shock (εtz) of 300 basis points in 2013Q3.
  - Normalization: model steady state equals data quarter prior to shock.
- Key model findings:
  - Both standard (linear) and extended models: higher sovereign risk perception ⇒ significant depreciation ⇒ higher inflation.
  - Standard model: positive output gap increases (via real exchange rate depreciation → net exports).
  - Extended model: output gap falls (matches observed dynamics) due to deterioration in monetary policy (MP) credibility, which reduces consumer spending.
  - Extended model matches Bank Indonesia’s ex-post policy: one-quarter ahead policy response of 125 basis points hike matched perfectly; further increases projected in 2013Q4 and slightly in 2014Q1.
  - Credibility channel amplifies UIP shock effects:
    - Temporarily reduces MP credibility stock,
    - Allows higher exchange rate depreciation and more persistent inflation,
    - Inflation expectations shift toward backward-looking component as agents lose confidence in CB’s ability to return inflation to target within 12-18 months.
  - Inflation expectations bias moves toward high-inflation regime, accelerating inflation expectations build-up and currency depreciation.
  - Output gap worsens as loss of MP credibility increases uncertainty and lowers confidence, prompting stronger CB tightening.
- Role of direct inflation expectations bias in aggregate demand:
  - Turning parameter 훽6 to 0 (no direct output gap effect) yields a strong positive output gap inconsistent with data.
  - Including direct inflation expectations bias in aggregate demand improves fit for output gap dynamics with no cost to matching nominal variables.
- Interpretation and policy implication:
  - Extended model (with MP credibility channel and direct output-gap effect) provides structural interpretation for contractionary depreciations via credibility loss rather than balance-sheet/dollarization channels.
  - During large shocks, policymakers could benefit from model extensions incorporating the credibility channel in policy deliberations and decision-making.

### B. Philippines — early-2018 inflation acceleration
- Context and observed facts:
  - Annual inflation increased from average 3.2 percent in 2017 to 4.3 percent year-on-year in March 2018, breaching BSP target 3 percent +/–1 percentage point.
  - Drivers included supply-side shocks (higher international fuel and food prices, bad weather and low agricultural output, taxation changes) and demand-side factors (core inflation upward trajectory).
  - BSP stance in early-2018: did not immediately tighten due to supply-driven nature of shocks and limited capacity of monetary policy to combat cost-push forces; intended to “look through” initial transitory supply shocks.
  - By mid-2018 headline inflation reached 4.6 percent in May 2018, and BSP began tightening: policy interest rate rose by cumulative 175 basis points between May and November 2018.
  - Inflation expectations in 2018:
    - One-year ahead expectations breached 4 percent upper limit of target band in 2018Q2
    - Two-year ahead expectations breached 4 percent in 2018Q4
    - Three-year ahead expectations revised upward within the 3-to-4 percent upper half of target range
- Simulation scenario:
  - Impose simultaneously a 0.25 percent supply (inflation) shock and a 200 basis points UIP shock in 2018Q1.
  - Rationale: matches very-early-2018 data releases — annual inflation rose from 3.3 percent in December 2017 to 4.0 percent in January 2018 and 4.5 percent in February 2018; sovereign yield spreads widened moderately end-2017 to early-2018; Consensus Forecasts showed analysts revising inflation forecasts upward for 2018 and 2019.
- Key model findings and interpretation:
  - Multiple-shock scenario captures the mix of supply and external-financing pressures faced by policymakers in early-2018.
  - Upward revisions in inflation expectations and early signs of second-round effects created risk of partial de-anchoring of expectations, motivating BSP’s subsequent tightening cycle.
  - Modeling underscores the importance of monitoring inflation expectations across horizons and of distinguishing supply-driven inflation from demand-driven or credibility-driven dynamics when forming policy.

*Source: Section 3 of the provided IMF working paper content.*

### Section 4

### IV. MODELING CREDIBILITY IN HYBRID MP REGIMES: PRELIMINARY CONSIDERATION

### Evidence from Philippines 2018 simulations
- Extended and standard models deliver similar directions of effects in the shock scenario: inflation rises and the nominal exchange rate depreciates, calling for an interest rate hike.
- Magnitudes differ markedly:
  - Credibility channel amplifies shock effects.
  - Suggested interest rate hike: about 75 basis points in the extended model, against 25 basis points in the standard model; in practice the BSP held the policy interest rate fixed until May 2018.
- Dynamics of inflation expectations:
  - Inflation expectations increased markedly over 2018H1 in the extended model, helping replicate sharp exchange rate depreciation observed in 2018Q1, while slightly overestimating inflation dynamics in the initial quarter.
  - In the standard model, inflation expectations are not contemporaneously affected by the considered shocks, increasing only marginally in the following quarter.
- Output effects:
  - The direct link between the inflation expectations bias and the output gap in the extended model ensures a negative effect of the shock scenario on aggregate demand.
  - Filtered real GDP data registers a slight increase in 2018Q1 and then a sharp fall starting 2018Q2, possibly reflecting a delayed launch of the monetary policy tightening cycle in May 2018.
  - Turning off the direct effect of credibility on aggregate demand (훽6 =0) confirms the importance of this mechanism for obtaining a negative output reaction to depreciation shocks.
- Fixed interest rate counterfactual:
  - Complementing the shock scenario with a constant interest rate assumption in 2018Q1 (replicating actual BSP decisions) yields results close to the baseline extended-model outcomes: better match for exchange rate depreciation and slight overestimation of initial-quarter inflation dynamics.

### Implications for Integrated Policy Frameworks (IPFs) and hybrid regimes
- Relevance:
  - The credibility channel findings are particularly relevant for IT frameworks because MP credibility is associated with deviation of inflation expectations from the target.
  - They also have important implications for frameworks with multiple objectives and tools (IPFs) due to complexity and communication challenges in establishing credibility.
- Key considerations for hybrid regimes:
  - Central banks often pursue multiple objectives (price stability, economic growth, full employment, financial and exchange rate stability) and use multiple tools (various interest rates and corridors, open market operations, macroprudential and capital flow measures, foreign exchange interventions).
  - The choice and specification of hybrid regimes depend on shock configuration and country-specific factors.
  - Simultaneous arrival of shocks with uncertain identification raises non-trivial tradeoffs; optimal policy design implies coordination and communication challenges.
  - Multifaceted policy design is difficult to communicate, increasing opacity and complicating the anchoring of inflation expectations.
  - Departures from "pure" IT complicate the mapping of instruments to objectives and can weaken credibility unless the CB has greater clarity of its operational framework.

### Suggested model adaptations and research avenues
- Potential avenue to adapt the model to hybrid regimes:
  - (i) Specify policy rules for additional instruments.
  - (ii) Extend the definition of credibility build-up to account for deviations of all relevant objective variables from their corresponding target values.
- Example application:
  - For an exchange rate objective complementing price stability, a rule for foreign exchange interventions could model convergence of the nominal exchange rate to the central bank’s exchange rate target level/path.
- Challenges and open questions:
  - Determining weights for individual components of the credibility process, especially without clear subordination of objectives.
  - Policymaker preferences likely shock-specific, in line with loss function frameworks used to estimate optimal policy strategies.
  - These issues are identified as areas for further exploration.

### Conclusion (from Sections IV–V)
- Model extension and conceptual framework:
  - The paper extends a standard semi-structural model to account for nonlinear and asymmetric effects of monetary policy credibility.
  - Credibility is linked to the deviation of inflation expectations from the announced inflation goal, with positive deviations being more costly than negative ones.
  - Credibility effects are modeled in the Phillips curve, the UIP equations, and directly on aggregate demand.
- Main mechanisms and outcomes:
  - A loss in policy credibility due to shocks leads to a more persistent, backward-looking inflation process and is associated with lower output.
  - Losses in credibility produce both build-up in inflationary pressures and falling aggregate demand, highlighting tradeoffs and the importance of rebuilding public confidence to re-anchor expectations.
- Simulation evidence:
  - Extended model matches Indonesia data during the “taper tantrum” episode in 2013 and Philippines data during the inflation-acceleration episode in 2018 more accurately than the standard model.
  - Extending the standard model with a nonlinear monetary policy credibility channel helps reproduce economic developments and central bank policy reactions during adverse-shock episodes.
- Policy relevance:
  - Extending semi-structural models with nonlinear policy credibility can better capture stylized facts and dynamic macroeconomic responses to shocks.
  - Incorporating monetary policy credibility into models is particularly pertinent for IPFs and hybrid regimes where multiple objectives and tools complicate policy communication and credibility.

*Source: wpiea2020201-print-pdf - Section 4*

### Section 5

### Section 5

### Cited works

- Macroeconomic Model for Policy Analysis and Insight (A Dynamic Stochastic General Equilibrium Model for the Bangko Sentral ng Pilipinas). Technical Report 2009-01, Bangko Sentral ng Pilipinas.  
- Mimir, Y., & Sunel, E. (2015). External shocks, banks and optimal monetary policy in an open economy. BIS Working Paper No. 528.  
- Sahay, R., Arona, V., Arvanitis, T., Faruqee, H., N’Diaye, P., & Mancini-Griffoli, T. (2014). Emerging Market Volatility: Lessons from the Taper Tantrum. IMF Staff Discussion Note 14/09, International Monetary Fund.  
- Sangaré, I. (2016). External shocks and exchange rate regimes in Southeast Asia: A DSGE Model. Economic Modelling, Elsevier, 58, 365-382.  
- Svensson, L. E. O. (1997). Inf lation f orecast targeting: Implementing and monitoring inf lation targets. European Economic Review, 41 1111-1146.  
- Unsal, F. (2020). Monetary Policy Frameworks and COVID-19. Presentation at the COVID-19 Pandemic in Africa Conference: Macroeconomic Impacts and Policy Responses. https://custom.cvent.com/49696820ADE54E53A4E8AE95054F8677/files/4f37086683d84b118f427e59b02bd78e.pdf

*Source: wpiea2020201-print-pdf - Section 5*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020201-print-pdf.pdf_