## _wp12269 - Executive Summary ## Source details **Canonical URL:** [_wp12269 - Executive Summary](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2012/_wp12269.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2012/_wp12269.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2012/_wp12269.pdf.json) --- ### Introduction and purpose - Purpose: Assess the role of countercyclical interest rate cuts and exchange rate flexibility in mitigating the economic fallout from three major shocks that hit Thailand during 2008–2011. - Method: Develop and estimate a small open economy dynamic stochastic general equilibrium (DSGE) model tailored to capture salient features of the Thai economy. - Model frictions and features: sticky prices, sticky wages, variable capital utilization, investment adjustment costs, habit persistence, and a financial accelerator mechanism à la Bernanke and others (1999) in an open-economy setup. - Main quantitative headline: without flexible exchange rates and active countercyclical monetary policy under an inflation targeting framework, the three major shocks’ impact would have been substantially more severe—countercyclical monetary policy and exchange rate flexibility contributed to growth by up to 1.6 and 2.1 percentage points, respectively, for a total of up to 3.7 percentage points during the six quarters when output was most severely affected. ### Three major shocks and monetary policy timeline (2008–2011) - Institutional backdrop: - Thailand adopted an inflation targeting framework in May 2000. - Exchange rate allowed to float freely. - Main objective of the BOT: ensure price stability while taking into consideration economic growth and stability. - Pre-shock performance: - Average annual inflation of 2.6 percent during 2001–2007. - Average annual GDP growth of 5.1 percent during 2001–2007. - Export performance: average growth of over 10 percent during 2006–07. - The three shocks: - Global financial crisis (GFC; intensified end-2008): - Features: reversal of capital inflows, market volatility, stock declines, plunge in exports in last quarter of 2008. - BOT action: cut policy rate by 250 basis points to 1¼ percent. - March 2011 earthquake in Japan: - Disrupted supply chains; some components over 90 percent sourced from Japan; abrupt production slowdowns. - 2011 Thai floods (August–November), worst in at least 50 years: - Over 800 people killed; millions displaced; 66 of 77 provinces inundated; about one third of the country experienced flooding. - GDP growth fell from 7.8 percent in 2010 to 0.1 percent in 2011, with a contraction of 11 percent in the last quarter of 2011 (annualized rate of about 50 percent seasonally adjusted data). - Policy response: broad fiscal stimulus, infrastructure investment plan, and BOT cut policy rates by 50 basis points to support recovery. ### Structural model overview - Framework: open-economy New Keynesian DSGE augmented with real and nominal rigidities and a financial accelerator. - Agents: households, producers (entrepreneurs, capital producers, retailers), and government (monetary and fiscal authorities). - Purpose: capture transmission channels of export demand shocks, sudden stops in capital flows (UIP shocks), financial uncertainty (net worth) shocks, and supply/disaster-related shocks; use model for counterfactual simulations. ### Transmission of shocks — representation and intuition - Export demand shock: - Captured via aggregate demand identity; collapse in export demand reduces gross exports and domestic output through trade channel. - Sudden stop (UIP) shock: - Modeled by augmenting the UIP condition with an exogenous shock term denoted ߔ_t (notation preserved); depreciation of nominal exchange rate ܵ_t (Thai baht per US dollar—an increase represents a depreciation). - Financial uncertainty (net worth) shock: - Real cost of capital ܴ_t^k equals real interest rate ܴ_t^r augmented by an external finance premium ߯_t(·). - External finance premium ߯_t = τ(ܳ_t / ܰ_t^n) depends on leverage (assets scaled by net worth). - Entrepreneurial net worth evolves with exogenous net worth shock ߷_t that directly affects aggregate net worth and ߯_t. - Natural disaster / supply shocks: - Temporary supply shocks lower total factor productivity in production function ܻ_t = ܣ_t ݖ_t^α ܮ_t^(1−α). - Investment-specific technology shock ߝ_t^I affects investment; capital evolution includes adjustment costs ߰(·) and depreciation ߜ. ### Monetary policy rule and role - Policy rule: log-linear empirical interest rate rule where nominal interest rate responds to inflation deviation from a time-varying inflation target, the output gap, the rate of exchange rate depreciation, and lagged interest rate (policy smoothing). Monetary policy shock ߳_t^r captures discretionary deviations. - Time-varying inflation target ොߨ_t captures structural changes (example: core inflation target range narrowed to 0.5–3.0 percent in 2009 from 0–3.5 percent). - Key intuition: under inflation targeting with a flexible exchange rate, the central bank uses countercyclical interest rate cuts (consistent with the inflation target) and allows exchange rate flexibility to absorb external shocks, increasing resilience. ### Counterfactual experiment designs - Objective: quantify how much deeper output contractions would have been absent the BOT’s inflation targeting framework underpinned by a flexible exchange rate. - Four counterfactuals (baseline = estimated policy under flexible exchange rate): - No monetary policy shocks: ߳_t^r set to zero (strict adherence to empirical rule; no discretionary loosening). - No response to output gap: output gap coefficient ߬_g = 0 and ߳_t^r = 0 (strict inflation targeting ignoring output gap). - Peg: strict fixed exchange rate regime implemented by BOT (no discretionary deviations; nominal exchange rate stabilized). - Peg with heightened financial vulnerability: fixed exchange rate plus leverage ratio calibrated to three (rather than baseline of two). - Presentation: figures normalize real GDP at 2008:Q3 = 100; comparisons focused on 2008:Q3–2009:Q4 where divergences are most noticeable. ### Key quantitative findings (preserved figures and language) - Six quarters most affected by the three shocks: - Countercyclical monetary policy contributed up to 1.6 percentage points to growth. - Exchange rate flexibility contributed up to 2.1 percentage points to growth. - Combined contribution: up to 3.7 percentage points. - Additional findings: - Without countercyclical monetary policy, cumulative growth losses associated with the three shocks would have been about 1.6 percentage points. - Illustrative counterfactual with heightened financial fragilities and a fixed exchange rate suggests cumulative growth losses close to 4.1 percentage points. - Additional total output loss implied by simulations without countercyclical monetary policies: up to 2.5 percentage points. - Grand total reduction in growth without both policies across the episodes: up to 4.1 percentage points. - For the Japanese earthquake spillovers, exchange rate flexibility helped support growth by up to about 1¼ percentage points. - Episode-specific highlights: - BOT cut policy rates by 250 basis points to 1¼ percent starting in the third quarter of 2008. - In response to the 2011 floods, BOT cut policy rates by 50 basis points. - Real exchange rate depreciation in Thailand during the GFC: about 4 percent. - Monetary shocks (discretionary deviations): total average contribution to output growth during the three episodes: about 70 basis points. - Accounting for active response to the output gap: total growth contribution of 91 basis points over the three shock episodes. - Discretionary loosening by the BOT added ¼ percentage point to average year-over-year real GDP growth during 2008:Q4–2009:Q3; additional support from responding to the widening output gap: 55 basis points; simulated effects of reduced financial fragilities and flexible exchange rate added up to 35 and 43 basis points to growth respectively. - Without countercyclical monetary policy, average growth across the three episodes would have been lower by a total of up to 1.6 percentage points. - Without exchange rate flexibility (and with a more financially fragile economy), growth would have been lower by an additional up to 2.5 percentage points. ### Estimation, calibration, and key parameter estimates - Data and estimation: - Quarterly data Q3 2000–Q1 2012 (2000–12); 12 standard time series matched. - Log-linearized model estimated using Bayesian methods (Schorfheide (2000); Smets and Wouters methodology). - Metropolis-Hastings algorithm: 500,000 draws, two independent chains; convergence per Brooks and Gelman (1998). - Calibration targets and steady state: - Discount factor: 0.9988 (implying an annual riskless real interest rate of approximately 2.5 percent). - Steady-state external finance premium target: around 320 basis points (based on 2001–07 average spread between bank lending rate and BOT policy rate). - Leverage ratio targeted: around two. - Number of entrepreneurs who survive each period (steady state): 0.9728. - Fraction of monitoring cost: 0.15. - Variance of idiosyncratic shock to entrepreneur production: 0.40. - Selected posterior means and intervals (preserved figures): - Calvo parameter — Wages: Mean 0.705; 5% 0.593; 95% 0.817. - Indexation — Domestic prices: Mean 0.536; 5% 0.374; 95% 0.695. - Interest rate smoothing: Mean 0.37; 5% 0.21; 95% 0.54. - Inflation response (to deviation from target): Mean 1.94; 5% 1.65; 95% 2.23. - Output gap response: Mean 0.13; 5% 0.07; 95% 0.18. - Habit formation: Mean 0.752; 5% 0.671; 95% 0.840. - Investment adjustment cost: Mean 4.224; 5% 3.519; 95% 4.925. - UIP shock persistence: posterior mean 0.99; 95th percentile estimated 0.998. - Log data density (baseline): 1,394.9 (Table 2). - Sensitivity and model selection: - 43 parameters estimated; priors chosen (Beta, Inverse gamma, Normal) consistent with literature. - Seven alternative model specifications considered; baseline decisively outperforms all alternatives (posterior odds ratios well above decisive thresholds; lowest odds ratio comfortably over 100). - Excluding the financial accelerator is decisively rejected; data decisively rejects a fixed exchange rate regime relative to baseline. ### Monetary transmission mechanism (impulse responses) — selected magnitudes - One standard deviation contractionary monetary policy shock: - Corresponds to an 80 basis point (quarterly) increase in the nominal interest rate (Table 2), implying an annual increase in the policy rate of about three percent. - Output gap dips below steady state by 31 basis points. - Year-over-year inflation rate troughs about 63 basis points below steady state after four periods. - Three propagation channels: - Domestic demand channel: higher real interest rates raise saving and the opportunity cost of investment, reducing consumption and investment. - Open-economy/exchange-rate channel: higher nominal interest rates appreciate the real exchange rate, suppress net exports (expenditure switching). - Financial accelerator channel: higher interest rates depress asset prices, deteriorate net worth, raise external finance premium, and sharply contract investment. - Model includes 15 structural shocks; supplementary results indicate foreign demand, trend productivity, and government spending explain main structural shocks accounting for year-over-year output growth. ### Policy-relevant conclusions and recommendations - Main conclusion: Thailand’s inflation targeting framework underpinned by a flexible exchange rate materially softened the impact of the GFC, the Japanese earthquake spillovers, and the 2011 floods. - Monetary policy conduct matters: - Countercyclical interest rate cuts consistent with the inflation target increased resilience; discretionary loosening and responsiveness to the output gap added measurable growth support. - Flexible exchange rate served as an important shock absorber, especially for trade-transmitted shocks. - Financial frictions matter: - The financial accelerator is essential to fit emerging market dynamics; heightened financial fragilities amplify output declines. - Policy implications: - Policy frameworks that preserve exchange rate flexibility and enable countercyclical monetary responses are well suited to mitigate severe external shocks in an open economy like Thailand. - The simulations focus on monetary policy and exchange rate flexibility; other countercyclical measures (for example fiscal policy and liquidity support) are not explicitly captured and warrant future research. *Source: _wp12269 - Executive Summary and Appendix (IMF working paper content provided).* ### Executive Summary ...................................................................................................... ### _wp12269 - Executive Summary ### Introduction - Purpose: Assess the role of countercyclical interest rate cuts and exchange rate flexibility in mitigating the economic fallout from three major shocks that hit Thailand during 2008–2011. - Method: Develop and estimate a small open economy dynamic stochastic general equilibrium (DSGE) model designed to capture salient features of the Thai economy. - Model frictions and features (preserved terminology): sticky prices, sticky wages, variable capital utilization, investment adjustment costs, habit persistence, and a financial accelerator mechanism à la Bernanke and others (1999) in an open-economy setup. - Main quantitative headline from the model: without flexible exchange rates and active countercyclical monetary policy under an inflation targeting framework, the impact of the three major shocks would have been substantially more severe—countercyclical monetary policy and exchange rate flexibility contributed to growth by up to 1.6 and 2.1 percentage points, respectively, for a total of up to 3.7 percentage points during the six quarters when output was most severely affected. ### Three Major Shocks and Monetary Policy in Thailand - Institutional backdrop: - Thailand formally adopted an inflation targeting framework in May 2000. - Exchange rate is allowed to float freely. - Main objective of the BOT: ensure price stability (low and stable inflation) while taking into consideration economic growth and stability. - Pre-shock performance: - Average annual inflation of 2.6 percent during 2001–2007. - Average annual GDP growth of 5.1 percent during 2001–2007. - Export performance: average growth of over 10 percent during 2006–07. - The three major shocks (2008–2011): - The global financial crisis (GFC) which intensified with the collapse of Lehman Brothers (end-2008), featuring a reversal of capital inflows, marked market volatility, sharp stock market declines, and a plunge in exports in the last quarter of 2008. - BOT action: cut policy rate by 250 basis points to a historically low level of 1¼ percent. - The March 2011 earthquake in Japan, which disrupted supply chains for Thailand because Japan is a key source of sophisticated intermediate and capital goods (some components over 90 percent sourced from Japan), causing abrupt production slowdowns. - The 2011 Thai floods (August–November), described as the worst flooding in at least 50 years: - Human and geographic impact: over 800 people were killed; millions displaced; 66 of the country’s 77 provinces inundated; about one third of the country experienced flooding. - Economic impact: manufacturing and primary sectors severely hit; GDP growth fell from 7.8 percent in 2010 to 0.1 percent in 2011, with a contraction of 11 percent in the last quarter of 2011 (which corresponds to an annualized rate of about 50 percent seasonally adjusted data). - Policy response: broad fiscal stimulus, an infrastructure investment plan, and the BOT cut policy rates by 50 basis points to support recovery. ### A Model for Thailand’s Monetary Policy Framework — Overview - The structural framework is an open-economy New Keynesian DSGE model augmented with: - Real and nominal rigidities (including investment adjustment costs and sticky wages). - A financial accelerator mechanism to capture financial market imperfections. - Agents: households, producers (entrepreneurs, capital producers, retailers), and government (implements monetary and fiscal policy). - Purpose: capture transmission channels of export demand shocks, sudden stops in capital flows, financial uncertainty shocks, and supply/disaster-related shocks; use model for counterfactual simulations. ### The Transmission of Shocks — Intuition and Representation - Export demand (foreign demand) shock: - Captured via aggregate demand identity: domestic output equals consumption of domestically produced goods (household and entrepreneurial consumption), domestic investment goods, government expenditures, and (gross) exports. - Collapse in export demand is reflected in a decline in gross exports. - Sudden stop (capital flow reversal) shock: - Modeled by augmenting the uncovered interest parity (UIP) condition with an exogenous shock term (the “sudden stop” or UIP shock). - Notation preserved: domestic and international (gross) interest rates represented as ݅௧ and ݅௧כ, nominal exchange rate as ܵ௧ (Thai baht per US dollar—an increase represents a depreciation), expectations operator ܧ௧, and shock term ߔ௧. - The exogenous UIP shock captures large capital outflows and sharp depreciation episodes observed during the GFC. - Financial uncertainty (net worth) shock: - Real cost of capital departs from standard representation because of an external finance premium: real cost of capital, ܴ௧௞, equals the real interest rate, ܴ௧ାଵ, augmented by an external finance premium ߯௧(·). - External finance premium ߯௧ depends on the leverage ratio (assets scaled by net worth): ߯௧ = τ(ܳ௧ / ܰ௧ାଵ). - Entrepreneurial net worth evolves with an exogenous net worth shock term ߷௧ that directly affects aggregate net worth and the external finance premium; this shock captures heightened uncertainty and counterparty risk that impair assets and disrupt the financial system. - Natural disaster / supply shocks: - Temporary supply shocks represented as decreases in total factor productivity (TFP) in the production function ܻ௧ = ܣ௧ ܭ௧^α ܮ௧^(1−α), with ܣ௧ and ݖ௧ denoting temporary and permanent technology shocks respectively. - Investment-specific technology shock ߝ௧௜ affects investment rather than the capital stock; capital evolution accounts for adjustment costs ߰(·) and depreciation ߜ. ### What Role for Monetary Policy? - Policy rule (log-linear empirical interest rate rule) preserves notation and determinants: - Nominal interest rate responds to: inflation deviation from a time-varying inflation target, the output gap, the rate of exchange rate depreciation, and the previous period’s interest rate (policy smoothing). - Key intuition: under an inflation targeting framework with a flexible exchange rate, the central bank uses countercyclical interest rate cuts (consistent with the inflation target) and allows exchange rate flexibility to absorb external shocks, thereby increasing the resilience of the economy. - Model use: the policy rule and the structural model are used to generate counterfactual simulations to quantify the contribution of countercyclical monetary policy and exchange rate flexibility to output stabilization in the face of the three shocks. ### Key Quantitative Findings and Policy Implications (preserved language and figures) - Quantitative result for the three shocks (six quarters when output most severely affected): - Countercyclical monetary policy contributed to growth by up to 1.6 percentage points. - Exchange rate flexibility contributed to growth by up to 2.1 percentage points. - Combined contribution of up to 3.7 percentage points. - Interpretation: - Exchange rate flexibility served as a shock absorber. - Countercyclical interest rate cuts consistent with the inflation target increased the resilience of the economy to the shocks. - Overall, the Bank of Thailand’s monetary policy framework appears to have increased the robustness of the Thai economy to the three major shocks. - Contextual policy actions and outcomes: - BOT cut policy rate by 250 basis points to 1¼ percent starting in the third quarter of 2008. - In response to the 2011 floods, BOT cut policy rates by 50 basis points to further support recovery. *Source: _wp12269 - Executive Summary* ### Appendix for further details). Note that ߳ ### 11.1 percent in the aftermath of the floods (for a difference of 1.2 percentage points). ### _wp12269 - 11.1 percent in the aftermath of the floods (for a difference of 1.2 percentage points). ### Key quantitative findings - Additional total output loss implied by simulations without countercyclical monetary policies: up to 2.5 percentage points. - Peak-to-trough real GDP decline during the 1990s U.S. recession (for context): about 1.3 percent. - Real exchange rate depreciation in Thailand during the GFC: about 4 percent. - Bank of Thailand (BOT) policy rate cut to a historically low level: 1¼ percent by mid-2009. - Monetary shocks (discretionary deviations from the empirical interest rate rule): total average contribution to output growth during the three episodes: about 70 basis points. - Accounting for active response to the output gap: total growth contribution of 91 basis points over the three shock episodes. - During the six quarters when output was most severely affected by the three shocks: - Countercyclical monetary policy contributed up to 1.6 percentage points to growth. - Exchange rate flexibility contributed up to 2.1 percentage points to growth. - Combined contribution: up to 3.7 percentage points. - Table-specific GFC example: - Discretionary loosening by the BOT added ¼ percentage point to average year-over-year real GDP growth during 2008:Q4–2009:Q3 (column [1]). - Additional support from responding to the widening output gap: 55 basis points (column [2]). - Simulated effects of reduced financial fragilities and flexible exchange rate added up to 35 and 43 basis points to growth (columns [4] and [3], respectively). - Without countercyclical monetary policy, average growth across the three episodes would have been lower by a total of up to 1.6 percentage points. - Without exchange rate flexibility (and with a more financially fragile economy), growth would have been lower by an additional up to 2.5 percentage points. - Grand total reduction in growth without both policies across the episodes: up to 4.1 percentage points. - For the Japanese earthquake spillovers, exchange rate flexibility helped support growth by up to about 1¼ percentage points. ### Comparisons with the literature - Christiano and others (2007) benchmark: - U.S. value reported: 75 basis points. - Euro area value reported: 127 basis points. - The paper’s estimate of 70 basis points for monetary shocks is broadly in line with these values. - Including output-gap responses increases comparable contributions (paper: 91 basis points). ### Interpretation of counterfactual simulations - Counterfactuals explore roles of: - Countercyclical monetary policy (discretionary rate cuts and output-gap responses). - Exchange rate flexibility. - Financial fragility (heightened financial fragilities as an illustrative scenario). - Key mechanisms: - Exchange rate flexibility acts as a shock absorber, particularly for shocks transmitted via the trade channel. - Greater financial fragilities amplify the balance sheets channel, producing sharper output declines. - Quantitative implication: - The illustrative counterfactuals highlight the importance of exchange rate flexibility and financial reforms in promoting macroeconomic stability and financial system soundness. ### Main policy implications (summary) - Thailand’s inflation targeting framework underpinned by a flexible exchange rate contributed materially to macroeconomic stabilization during 2008–2011 shocks. - Countercyclical interest rate cuts and exchange rate flexibility served as shock absorbers and facilitated recovery after the GFC, the Japanese earthquake spillovers, and the 2011 floods. - Given Thailand’s openness through financial and trade channels, policy frameworks that preserve flexibility and resilience are well suited to mitigate severely disruptive exogenous shocks. - The simulations focus on monetary policy and exchange rate flexibility; other countercyclical measures (for example fiscal policy and liquidity support) are not explicitly captured and warrant future research. ### Appendix — model and methodological notes - Analytical framework: an open economy New Keynesian DSGE model with additional features to better fit the data, including: - Nominal and real rigidities. - A stochastic trend. - A financial accelerator mechanism. - Model builds on elements from Adolfson and others (2007), Bernanke and others (1999), Elekdag and others (2006), Gertler and others (2007), and related work such as Alp and Elekdag (2011). - Agents: households, entrepreneurs, capital producers, retailers, and government (monetary and fiscal policy). - Households’ consumption aggregator: standard CES index of domestically produced and imported goods. - Preferences incorporate habit persistence (term ܥܾ ௧ିଵ) and stochastic preference and labor supply shocks (ߝ ௧ ௖ and ߝ ௧ ௟) modeled as AR(1) processes. - Foreign interest rate modeled as an exogenous AR(1) process; country borrowing premium (ߔ ௧) depends on total net foreign indebtedness and an exogenous AR(1) process (ߝ ௧ ః). - Counterfactual simulations are based on time series of year-over-year growth rates and an estimated structural model that captures salient features of the Thai economy. *Source: IMF working paper content provided in the supplied PDF excerpt.* ### introduction of this risk-premium is needed in order to ensure a well-defined steady state in ### _wp12269 - introduction of this risk-premium is needed in order to ensure a well-defined steady state in ### Household optimization, Euler equation, and UIP shocks - Euler equation for household intertemporal utility maximization: equation (10) as given in the source. - Marginal utility of the consumption index: equation (11) with notation ߣ_t and specification in the source. - Foreign bond optimality yields an uncovered interest parity (UIP) condition where the exogenous process ߝ_t^UIP can be interpreted as a risk premium (UIP) shock: equation (12). - Role of UIP shocks: typically used to imitate a sudden stop shock (Calvo et al., 2004) causing large capital outflows; in this paper the UIP shock is used to capture the financial aspect of the global financial crisis. ### Wage setting and labor demand - Households are monopolistic suppliers of differentiated labor services and set wages ܹ_{j,t}; they inelastically supply labor at the going wage. - Production of homogeneous labor input from household labor: production function given by equation (13) (includes wage markup ߤ_w). - Individual household labor demand: equation (14). - Wage stickiness / reoptimization: Calvo-type setup following Kollmann (1997) and Erceg et al. (2000): - Non-reoptimizing household wage update rule: equation (15) with wage indexation parameter ߛ_w and π_t^c = π_t/π_{t-1}. - Re-optimizing household solves dynamic program (16). - First-order condition for wage choice: equation (17). - Log-linearized real wage equation and definition of parameter ߤ in equation (19); the log-linearized expression is given by equation (18). ### Foreign economy and trade shocks - Wholesale (import) law of one price holds: equation (20). - Foreign demand for the home tradable good specification following Gertler et al. (2007): equation (21). - A shock to ܻ_t^X captures the trade channel of the global financial crisis. ### Entrepreneurs, production, and financial frictions - Entrepreneurs are risk neutral, manage production, and finance capital subject to a finite expected horizon: survival probability ߷_t is time-varying and stochastic. - Production with capital services and composite labor: aggregate output per entrepreneur and related relations given by equations (22)–(24). - Technology shocks: - Stationary productivity shock ܣ_t and permanent technology shock ݖ_t with law of motion in equations (25)–(26). - Capital utilization and depreciation: - Depreciation rate as function of utilization: ߜ_t + ߜ = ߬(1 + ݑ_t)^{1+ɣ} or specified in equation (27). - Optimality condition for capital utilization: equation (30). - Gross project output ܻ^p_t and idiosyncratic shock ߱_t enter project returns: equation (23) and production technology (24). - Capital financing and balance sheet: - Balance sheet identity for capital acquisition: equation (31) with net worth N_{t+1} and nominal bonds B_{t+1}. - Marginal return to capital 𝓡_{t+1}^k: equation (32) and expectation form (33). - External finance premium and financial accelerator: - External finance premium ߯_t(.) expressed as function of leverage (real price of capital ܳ_t) and net worth: equation (34). - Entrepreneur marginal cost of funds multiplied by external premium and gross real opportunity cost yields investment demand condition: equation (35). - Evolution of entrepreneurial firm capital net of borrowing costs 𝓧_t and net worth equations: (36) and (37). - Time-varying survival rate process for entrepreneurs: ߷_t = ߝ_t^ω with log process in equations (38)–(39); net worth shock ε_t^N interpreted as financial uncertainty or counterparty risk, directly affecting aggregate net worth and external finance premium. - Consumption of exiting entrepreneurs: equation (40). ### Capital producer and investment - Capital producers transform investment goods into capital; capital accumulation equation with adjustment costs: equation (41). - Capital adjustment cost ߰(·) properties and identification: only parameter ߰'' is identified in log-linearized model. - Investment CES aggregation of domestic and imported investment goods: equation (42), import share ߛ_I and elasticity ߩ_I. - Investment demand (domestic vs imported) and aggregate investment price: equations (43)–(44). - Capital producer optimization problem and first-order condition: equations (45)–(46). ### Retailers of domestic goods and price-setting - Continuum of monopolistically competitive retailers; final domestic output is a CES composite of individual retail goods: equation (47). - Time-varying markup process for domestic retailers: equation (48). - Isoelastic demand for each retailer and price index definitions: equations (49)–(50). - Calvo price-setting with indexation for domestic retailers: - Non-reoptimizing price update: equation (51) with definitions of inflation rates. - Reoptimizing retailer optimization problem: equation (52); first-order condition in equation (53). - Aggregate price index and average price expression: equation (54). - Log-linearized aggregate Phillips curve relation for domestic inflation: equation (55). ### Retailers of imported goods and imported inflation - Imported goods sector: imported retailers buying homogeneous world market good at price P_t^w and transforming into differentiated imported consumption and investment goods. - Imported retailers follow Calvo pricing with local currency stickiness: non-reoptimizing price update rule equation (56). - Final imported good CES aggregation and markup process for importers: equations (57) and (58). - Isoelastic demand and imported price index: equations (59)–(60). - Reoptimizing imported retailer optimization problem and first-order condition: equations (61)–(62). - Log-linearized Phillips curve for imported good inflation: equation (63). ### Monetary policy rule and inflation target - Central bank sets nominal interest rate via log-linear interest rate rule (64): - Policy reacts to inflation deviation from time-varying target, output gap, exchange rate depreciation, and lagged interest rate. - Monetary policy shock ߳_{t}^r is i.i.d. - Time-varying inflation target process (for Thailand example): equation (65). The example references the core inflation target range narrowed to 0.5–3.0 percent in 2009 from 0–3.5 percent. - Discretionary deviations from the rule are captured by the monetary policy shock. ### Market clearing, stationarity, and model solution - Goods market equilibrium condition (aggregate demand components): equation (66) where external AR(1) spending process q_t captures fiscal policy in rudimentary form. - Stationarity: nominal variables scaled by CPI P_t, real variables (except labor) scaled by real stochastic trend ݖ_t; log-linearization around steady state. ### Estimation, data, and calibration - Estimation approach: log-linearized model estimated using Bayesian methods (Schorfheide, Smets and Wouters methodology). - Sample and observables: - Quarterly data from Q3 2000 to Q1 2012 using 12 standard time series (some shown in Figure 1 in the source). - Matched variables: levels of domestic policy and foreign interest rates; inflation rates of domestic GDP deflator, core consumer price, and foreign CPI; growth rates of GDP, consumption, investment, exports, imports, foreign GDP, and the real exchange rate. - Observed vector in model notation given in the source (explicit vector shown). - Transformations: interest rates divided by four; data demeaned by removing sample mean except inflation and interest rates (demeaned by subtracting steady-state values). - Foreign variables: weighted average of China, United States, Japan, and Hong Kong for foreign real GDP, interest rate, and inflation. - Calibrated parameters: - Parameters α, δ, γ, γ_i chosen to calibrate consumption-, investment-, government expenditures-, and exports-to-GDP ratios to around 62, 21, 5, and 70 percent, respectively, in line with Thailand’s longer-term averages. - Discount factor β fixed at the steady-state value (as indicated in the source continuation). *Source: _wp12269 - introduction of this risk-premium is needed in order to ensure a well-defined steady state in (IMF PDF content provided).* ### 0.9988 implying an annual riskless real interest rate of approximately 2.5 percent, close to ### _wp12269 - 0.9988 implying an annual riskless real interest rate of approximately 2.5 percent, close to ### Calibration of the model and key calibrated parameters - Discount factor: 0.9988 (implying an annual riskless real interest rate of approximately 2.5 percent). - Steady-state external finance premium target: around 320 basis points (based on 2001–07 average spread between the bank lending rate and the BOT policy rate). - Leverage ratio targeted: around two (as in Bernanke and others, 1999). - Parameters chosen to achieve the above steady state values: - Number of entrepreneurs who survive each period (at steady state): 0.9728 - Fraction of monitoring cost: 0.15 - Variance of idiosyncratic shock to entrepreneur production: 0.40 - Other calibrated parameter values (Table 1): - Consumption intra-temporal elasticity of substitution: 1.00 - Share of domestic goods in consumption: 0.075 - Investment intra-temporal elasticity of substitution: 0.25 - Share of domestic goods in investment: 0.125 - Inverse of the elasticity of work effort with respect to the real wage: 1.00 - Share of capital in production function: 0.50 - Elasticity of marginal depreciation with respect to utilization rate: 1.00 - Steady state markup rate for domestically produced goods: 1.15 - Steady state markup rate for imported goods: 1.15 - Steady state markup rate for wages: 1.15 - Share of entrepreneurial labor: 0.01 - Steady state external finance premium: 1.03 - Depreciation rate (at steady state): 0.025 - Elasticity of country risk premium with respect to net foreign debt: 0.001 ### Prior distributions and estimation setup - Number of parameters estimated: 43 (shown in Table 2). - Distributional choices for priors: - Parameters bounded between 0 and 1: Beta distribution. - Positive-only parameters (standard deviations): Inverse gamma distribution. - Unbounded parameters: Normal distribution. - Specific prior means and references: - Calvo (1983) parameters mean: 0.5 (as in Teo (2009)). - Indexation parameter mean: 0.5 (as in Adolfson and others (2007)). - Baseline monetary policy rule: - Interest rate persistence prior: 0.7 (in line with Elekdag and others (2006)). - Responsiveness to inflation prior (Taylor principle): 1.5. - Habit persistence prior: 0.7 (as in Adolfson and others (2007)). - Shock persistence prior: 0.8 (compared to 0.85 in Adolfson and others (2007), and 0.5 in Elekdag and others (2006) and Garcia-Cicco (2010)). - Priors for most standard deviations: inverse gamma distribution typically centered on 0.05 with one degree of freedom. - Estimation algorithm and diagnostics: - Metropolis-Hastings sampling algorithm. - Total draws: 500,000. - Number of independent chains: two. - Convergence criteria: Brooks and Gelman (1998) achieved. ### Posterior distributions and key parameter estimates - Posterior reporting: means and 5th and 95th percentiles (Table 2). - Nominal rigidities and indexation: - Wage-Calvo parameter (posterior mean): 0.7 (implies wages adjusted on average every 10 months / 3.33 quarters). - Domestic price Calvo parameter (implied): domestic prices adjust on average every 5 months (1.63 quarters). - Indexation parameters: in the 0.5 range (significant backward-looking components in the Phillips curve). - Real rigidities: - Habit formation (posterior mean): 0.75. - Investment adjustment cost (posterior mean): 4.2. - Monetary policy rule posteriors: - Interest smoothing (posterior mean): 0.4 (lower relative to other studies; partly reflecting unprecedented BOT interest rate cuts during the global financial crisis). - Responsiveness to inflation deviation from target (posterior mean): 1.9 (greater than unity; similar to 1.6 found by Adolfson and others (2007)). - Responsiveness to nominal exchange rate depreciation: smaller (echoing Elekdag and others (2006)). - Responsiveness to the output gap (posterior mean): 0.13. - Shock persistence posteriors: - Stationary technology shock persistence: posterior mean 0.26. - UIP shock persistence: posterior mean 0.99; 95th percentile estimated to be 0.998 (high but indicates absence of unit roots). - Range of persistence parameters noted across shocks: from 0.26 (unit root technology shock) to 0.99 (UIP shock). - Shock volatility posteriors: - Foreign interest rate shock: least volatile. - Investment shocks: notably more volatile. - Unit-root technology shock: more volatile than stationary technology shock (consistent with Aguiar and Gopinath (2007)). - Selected Table 2 posterior/posterior details (posterior means, 5% and 95%): - Calvo parameter — Wages: Mean 0.705; 5% 0.593; 95% 0.817. - Indexation — Domestic prices: Mean 0.536; 5% 0.374; 95% 0.695. - Interest rate smoothing: Mean 0.37; 5% 0.21; 95% 0.54. - Inflation response: Mean 1.94; 5% 1.65; 95% 2.23. - Output gap response: Mean 0.13; 5% 0.07; 95% 0.18. - Habit formation: Mean 0.752; 5% 0.671; 95% 0.840. - Investment adjustment cost: Mean 4.224; 5% 3.519; 95% 4.925. - Shock persistence examples: - Stationary technology: Mean 0.794; 5% 0.649; 95% 0.957. - Unit root technology: Mean 0.258; 5% 0.176; 95% 0.339. - Investment specific technology: Mean 0.926; 5% 0.911; 95% 0.940. - Shock volatility examples (posteriors reported as means with 5% and 95%): - Stationary technology (Inverse gamma prior): Mean 0.018; 5% 0.012; 95% 0.024. - Unit root technology: Mean 0.052; 5% 0.043; 95% 0.062. - Investment specific technology: Mean 0.243; 5% 0.204; 95% 0.284. - Log data density reported: 1,394.9 (Table 2) and elsewhere log data density 1,265 noted for a different summary note. ### Sensitivity analysis and model selection - Method: Compare baseline to 7 alternative specifications using posterior odds ratio (Jeffreys (1961) guidance). - Decision rule: Ratios above 100 provide decisive evidence in favor of the baseline model; the lowest odds ratio calculated was comfortably over 100. - Summary outcome: Baseline model decisively outperforms all 7 alternatives (Table 3). - Key sensitivity findings: - Excluding the financial accelerator is decisively rejected in favor of baseline — underscores importance of financial frictions for emerging markets. - Models with low nominal rigidities are disfavored — New Keynesian features (nominal frictions) are needed to better fit the data versus canonical RBC. - Structural shocks: - Technology shocks are important (consistent with Aguiar and Gopinath (2007) and Justiniano and others (2007)). - Demand shocks (preference and government spending shocks) are important; models omitting them are rejected. - Eliminating financial shocks (financial uncertainty and UIP shocks) leads to rejection in favor of baseline. - Monetary policy rules: - Baseline specification decisively favored over alternatives tested. - Data favors some weight on nominal depreciation rate but decisively rejects a fixed exchange rate regime (peg). - Table 3 log data densities and model superiority (selected entries): - Baseline model log data density: 1394.889 (Is the alternative model superior? Baseline: —). - 1 Financial accelerator (alternative removing accelerator): log data density 1353.392 — alternative not superior. - 2 Low stickiness including wages: 1364.028 — alternative not superior. - 3 Technology (all shocks removed): 1162.531 — alternative not superior. - 4 Preference and Government shocks removed: 1156.878 — alternative not superior. - 5 Financial (uncertainty and UIP removed): 1390.121 — alternative not superior. - 6 Baseline rule without ∆S rule: 1338.884 — alternative not superior. - 7 Fixed exchange rate regime: 1276.392 — alternative not superior. ### Counterfactual scenarios and the role of monetary policy, exchange rate flexibility, and financial reforms - Counterfactual exercises illustrate contributions to real GDP across episodes; figures depict indices with 2008Q1=100 for real GDP (Figures 4 and 5). - Table 4: Average year-over-year growth contributions of monetary policy owing to: - Columns labeled [1] to [5]: Responsiveness to the output gap, Flexible exchange rate, Reduced financial vulnerability, Total (column [1]—[4]) across episodes. - Episodes and numeric contributions (in percent): - Global financial crisis (2008Q4—2009Q3), Quarters: 4: - [1] Responsiveness to output gap: 0.24 - [2] Flexible exchange rate: 0.55 - [3] Reduced financial vulnerability: 0.43 - [4] to [5] Total: 0.35 and 1.57 (table format indicates aggregate columns; Table 4 lists Total Episode values and comparisons). - Japan earthquake (2011Q2), Quarters: 1: - [1]: 0.07 - [3]: 1.18 - Total: 1.26 - Thai floods (2011Q4), Quarters: 1: - [1]: 0.40 - [2]: 0.37 - [3]: 0.45 - Total: 0.11 and 1.32 - Total (6 quarters across episodes): - [1]: 0.71 - [2]: 0.91 - [3]: 2.06 - [4]: 0.45 - [5] Total: 4.14 - Comparative entries for Christiano and others (2008): - United States (2001Q2-2002Q2), Quarters: 4 — entry: 0.75 - Euro area (2001Q4-2004Q4), Quarters: 13 — entry: 1.27 - Table 5: Summary of the role of monetary policy — Real GDP growth (year-over-year) in percent under different scenarios: - Columns: - [1] Actual (Baseline) - [2] Without countercyclical monetary policy - [3] Difference (= [2] – [1]) - [4] Without peg with financial fragility - [5] Difference (= [4] – [2]) - Episodes and values (in percent): - Global financial crisis (2008Q4—2009Q3): - Baseline (Actual): -4.8 - Without countercyclical monetary policy: -5.6 - Difference: -0.8 - Without peg with financial fragility: -6.4 - Difference: -0.8 - Japanese earthquake (2011Q2): - Baseline: 2.8 - Without countercyclical monetary policy: 2.8 - Difference: -0.1 - Without peg with financial fragility: 1.6 - Difference: -1.2 - Thai floods (2011Q4): - Baseline: -8.9 - Without countercyclical monetary policy: -9.7 - Difference: -0.8 - Without peg with financial fragility: -10.3 - Difference: -0.6 - Total growth drag: - Baseline to alternatives aggregations: -1.6 and -2.5 (presented as totals in table). ### Policy-relevant conclusions and implications emphasized in the content - Financial frictions matter: exclusion of the financial accelerator is decisively rejected — models for emerging markets should incorporate financial frictions when analyzing macro dynamics. - Monetary policy conduct matters: - The baseline monetary policy (which includes interest-rate responsiveness to inflation above unity and some weight on nominal depreciation) improves fit and supports stabilization. - Interest smoothing is lower (posterior mean 0.4), reflecting active policy rate adjustments (notable BOT cuts during the global financial crisis). - Data decisively rejects a fixed exchange rate regime for describing past policy behavior; flexible exchange rate and responsiveness to depreciation receive empirical support. - Structural shocks importance: - Technology and demand shocks are critical for accounting for output variability. - Unit-root technology shocks are found to be both persistent and volatile, consistent with theories assigning them a prominent role in business cycles. - Robustness: The baseline specification decisively outperforms alternative models across frictions, shocks, and policy rule specifications (posterior odds ratios well above decisive thresholds). *Italic: Source — Authors’ calculations and tables and figures as provided in the content unit.* ### REFERENCES ### REFERENCES ### Bayesian estimation, DSGE methods, and model evaluation - Adolfson, Malin, Stefan Laseen, Jesper Linde, Mattias Villani, 2007, “Bayesian Estimation of an Open Economy DSGE Model with Incomplete Pass-Through,” Journal of International Economics, Vol. 72, pp. 481–511. - Adolfson, Malin, Stefan Laseen, Jesper Linde, Mattias Villani, 2008, “Evaluating an Estimated New Keynesian Small Open Economy Model,” Journal of Economic Dynamics and Control, Vol. 32, pp. 2690–2721. - Dib, Ali and Ian Christensen, 2008, “The Financial Accelerator in an Estimated New Keynesian Model,” Review of Economic Dynamics, Vol. 11, pp. 155–78. - Justiniano, Alejandro, Giorgio E. Primiceri, and Andrea Tambalotti, 2007, “Investment Shocks and Business Cycles,” Federal Reserve Bank of Chicago Working Paper 2008–12 (Chicago: Federal Reserve Bank of Chicago). - Lubik, Thomas A., and Frank Schorfheide, 2007, “Do Central Banks Respond to Exchange Rate Movements? 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F. Mejia, 2004, “On the Empirics of Sudden Stops: The Relevance of Balance-Sheet Effects,” NBER Working Paper, No. 10520 (Cambridge, Massachusetts: National Bureau of Economic Research). - Cardarelli, Roberto, Selim Elekdag, and Subir Lall, 2011, “Financial Stress and Economic Contractions,” Journal of Financial Stability, Vol. 7, No. 2 (June), pp. 78–97. - Cespedes, Luis F., Roberto Chang and Andres Velasco, 2004, “Balance Sheets and Exchange Rate Policy,” American Economic Review, Vol. 94, pp. 1183–93. - Curdia, Vasco, 2007, “Monetary Policy under Sudden Stops,” FRBNY Staff Report No. 278 (New York: Federal Reserve Bank of New York). - Elekdag, Selim, Alejandro Justiniano, and Ivan Tchakarov, 2006, “An Estimated Small Open Economy Model of the Financial Accelerator,” IMF Staff Papers, Vol. 53, pp. 219–41 (Washington: International Monetary Fund). - Elekdag, Selim and Ivan Tchakarov, 2007, “Balance Sheets, Exchange Rate Policy, and Welfare,” Journal of Economic Dynamics and Control, Vol. 31, pp. 3986–4015. - Gertler, Mark, Simon Gilchrist, Fabio Natalucci, 2007, “External Constraints on Monetary Policy and the Financial Accelerator,” Journal of Money, Credit and Banking, Vol. 39, No. 2–3, pp. 295–330. - Kiyotaki, Nobuhiro, and John Moore, 1997, “Credit Cycles,” Journal of Political Economy, Vol. 105 (April), pp. 211–48. - Mendoza, Enrique, 1991, “Real Business Cycles in a Small Open Economy,” American Economic Review, Vol. 81, pp. 7973–818. ### Small open economy models, exchange rates, and monetary policy - Gali, Jordi, and Tommaso Monacelli, 2005, “Monetary Policy and Exchange Rate Volatility in a Small Open Economy,” Review of Economic Studies, Vol. 72, No. 3, pp. 707–34. - Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno, 2003, “The Great Depression and the Friedmand-Schwartz Hypothesis,” Journal of Money, Credit, and Banking, Vol. 35, No. 6, pp. 1119–97. - Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno, 2008, “Shocks, Structures, or Monetary Policies? The Euro Area and U.S. After 2001,” Journal of Economic Dynamics and Control, Vol. 32, pp. 2476–2506. - Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno, 2010, “Financial Factors in Economic Fluctuations,” ECB Working Paper No. 1192 (Frankfurt, Germany: European Central Bank). - Taylor, John B., 1993, “Discretion versus Policy Rules in Practice,” Carnegie-Rochester Conference Series on Public Policy, Vol. 39, pp. 195–214. - Lubik, Thomas A., and Frank Schorfheide, 2007, “Do Central Banks Respond to Exchange Rate Movements? A Structural Investigation,” Journal of Monetary Economics, Vol. 54, pp. 1069–87. ### Emerging markets, capital flows, and policy in crisis episodes - Aguilar, Mark, and Gita Gopinath, 2007, “Emerging Market Business Cycles: The Cycle is the Trend,” Journal of Political Economy, Vol. 115. No. 1, pp. 69–102. - Alp, Harun, and Selim Elekdag, 2011, “The Role of Monetary Policy in Turkey during the Global Financial Crisis,” IMF Working Paper, No. 11/150 (Washington: International Monetary Fund). - Garcia-Cicco, Javier, 2010, “Estimating Models for Monetary Policy Analysis in Emerging Countries,” Central Bank of Chile Working Paper No. 561 (Chile: Central Bank of Chile). - Kaminsky, Graciela L., Carmen M. Reinhart, and Carlos A. Vegh, 2005, “When It Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies,” in NBER Macroeconomics Annual 2004, Vol. 19, ed. By Mark Gertler and Kenneth Rogoff, pp. 11-82 (Cambridge, Massachusetts: National Bureau of Economic Research). ### Classic theory and foundational texts - Calvo, Guillermo, 1983, “Staggered Prices in a Utility-Maximizing Framework,” Journal of Monetary Economics, Vol. 12, pp. 383–98. - Knight, Frank H., 1921, Risk, Uncertainty, and Profit. (Boston, MA: Hart, Schaffner & Marx; Houghton Mifflin Company). *Compiled from the document _wp12269 - REFERENCES.* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2012/_wp12269.pdf_