## New Phillips Curve for Singapore

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---

### Introduction
- Objective: Investigate Singapore’s monetary policy process by estimating a reaction function characterizing how the monetary policy stance (proxied by changes in the trade-weighted nominal exchange rate index, TWI) has historically reacted to shifts in expected inflation and its determinants.
- Analytical steps:
  - Variance decomposition to assess key macroeconomic factors affecting inflation and activity.
  - Specification and estimation of a forward-looking reaction function for changes in the TWI.
  - Computation and evaluation of a New Phillips Curve (NPC) to forecast inflation.
- Contextual points:
  - MAS manages the Singapore dollar against an undisclosed trade-weighted currency basket since 1981.
  - Policy is forward-looking and centered on maintaining price stability by guiding the TWI.

### Current monetary framework
- Operational facts:
  - MAS manages the Singapore dollar against an undisclosed trade-weighted basket of currencies; composition is reviewed periodically.
  - MAS guides the exchange rate to appreciate or depreciate depending mainly on expected inflationary pressures.
  - Implementation tools: direct foreign exchange sales/purchases of the U.S. dollar; money market operations (foreign exchange swaps, interbank lending/borrowing, sales/purchases or repurchase agreements in government securities) to offset short-term liquidity fluctuations.
- Historical highlights (selected):
  - TWI appreciated by 30 percent during 1981–1985 (averaging about 5 percent per year) to contain inflation.
  - TWI depreciated about 16 percent during 1985–1988.
  - MAS allowed the TWI to appreciate from 1988 through 1997; trend appreciation ended during the Asian crisis when policy shifted to a zero-appreciation band.
  - In July 2001 MAS eased to a neutral setting centered on a zero percent appreciation band; post-September 11 the band boundaries were widened then narrowed subsequently.

### Sources of output and inflation volatility (VAR analysis)
- Model specification:
  - Six-variable VAR using monthly data (sample 1991:01–2002:04): industrial production, CPI inflation, world commodity price index, foreign interest rate (U.S. Federal Funds rate), domestic interest rate, and the TWI.
  - Two variables treated as exogenous: world commodity price index and foreign interest rate.
  - VAR performed using log differences in 12 months except interest rates in levels; twelve lags used.
- CPI inflation variance decomposition (Table 1, 1991:01–2002:04):
  - Horizon = 1 (Std. Error 0.003): ∆World Commodity Price Index 4.627, ∆Industrial Production 1.865, CPI Inflation 93.508, Foreign Interest Rate 0.000, Domestic Interest Rate 0.000, ∆TWI 0.000.
  - Horizon = 6 (Std. Error 0.006): ∆World Commodity Price Index 7.069, ∆Industrial Production 48.292, CPI Inflation 25.123, Foreign Interest Rate 7.242, Domestic Interest Rate 3.594, ∆TWI 8.679.
  - Horizon = 12 (Std. Error 0.010): ∆World Commodity Price Index 7.560, ∆Industrial Production 55.603, CPI Inflation 9.248, Foreign Interest Rate 2.958, Domestic Interest Rate 1.701, ∆TWI 22.930.
  - Horizon = 18 (Std. Error 0.012): ∆World Commodity Price Index 11.737, ∆Industrial Production 49.568, CPI Inflation 8.900, Foreign Interest Rate 5.277, Domestic Interest Rate 1.664, ∆TWI 22.854.
  - Horizon = 24 (Std. Error 0.015): ∆World Commodity Price Index 8.726, ∆Industrial Production 45.062, CPI Inflation 6.412, Foreign Interest Rate 20.066, Domestic Interest Rate 2.476, ∆TWI 17.257.
- Key inflation-decomposition findings:
  - Real domestic activity (industrial output) explains a large share of CPI inflation forecast errors in the short-to-medium term (over 50 percent of variance in the first year).
  - The monetary policy shock (changes in the TWI) has relatively little impact on CPI inflation initially but becomes more influential after the third quarter (reaching around 22.930 percent at 12 months and similar values at 18 months).
  - Empirical pass-through: ceteris paribus, a 1 percent depreciation in the TWI would increase import prices by about 0.25 percent by the fourth quarter.
- Industrial production variance decomposition (Table 2, 1991:01–2002:04):
  - Horizon = 1 (Std. Error 0.055): ∆World Commodity Price Index 0.196, ∆Industrial Production 99.804, CPI Inflation 0.000, Foreign Interest Rate 0.000, Domestic Interest Rate 0.000, ∆TWI 0.000.
  - Horizon = 6 (Std. Error 0.083): ∆World Commodity Price Index 7.262, ∆Industrial Production 74.542, CPI Inflation 4.829, Foreign Interest Rate 8.092, Domestic Interest Rate 4.275, ∆TWI 1.000.
  - Horizon = 12 (Std. Error 0.098): ∆World Commodity Price Index 21.391, ∆Industrial Production 58.197, CPI Inflation 4.534, Foreign Interest Rate 8.067, Domestic Interest Rate 5.259, ∆TWI 2.551.
  - Horizon = 18 (Std. Error 0.129): ∆World Commodity Price Index 17.358, ∆Industrial Production 64.572, CPI Inflation 3.517, Foreign Interest Rate 7.989, Domestic Interest Rate 3.876, ∆TWI 2.686.
  - Horizon = 24 (Std. Error 0.145): ∆World Commodity Price Index 24.043, ∆Industrial Production 55.972, CPI Inflation 3.076, Foreign Interest Rate 10.065, Domestic Interest Rate 3.175, ∆TWI 3.667.
- Key output-decomposition findings:
  - Foreign shocks explain a large share of industrial production volatility (world commodity price shock explains 24.043 percent after two years; foreign interest rate explains about 10.065 percent).
  - Changes in the TWI have relatively little impact on domestic industrial output throughout the forecast horizon.

### Monetary policy reaction function (TWI as policy instrument)
- Conceptual specification:
  - MAS target change in TWI, e*_t ∆ , is a function of expected inflation (between t and t+n) and expected real output (between t and t+m), with partial adjustment and exchange rate smoothing parameter ρ ∈ [0,1].
  - Estimation via GMM with instruments: lags 1–6, 9, and 12 of CPI inflation, industrial production gap, and the TWI.
  - Sample: 1991:01–2002:04, using year-on-year CPI inflation, detrended industrial output (Hodrick-Prescott), and year-on-year change in the TWI.
- Baseline estimated coefficients (Table 3; selected reported horizons):
  - Current Inflation (n = 0): α = 0.005 (t = 0.82); β = 0.975 (t = 2.56); γ = 0.137 (t = 1.28); ρ = 0.838 (t = 32.44); 2R = 0.86; p-value 0.66; J-test 16.87.
  - Expected Inflation (n = 6): α = -0.006 (t = -0.79); β = 1.44 (t = 3.37); γ = 0.235 (t = 2.30); ρ = 0.831 (t = 30.27); 2R = 0.83; p-value 0.75; J-test 15.47.
  - Expected Inflation (n = 9): α = -0.008 (t = -0.91); β = 1.891 (t = 3.41); γ = 0.423 (t = 3.07); ρ = 0.847 (t = 37.80); 2R = 0.86; p-value 0.85; J-test 13.70.
  - Expected Inflation (n = 12): α = -0.005 (t = -0.41); β = 1.824 (t = 2.34); γ = 0.525 (t = 2.49); ρ = 0.871 (t = 40.68); 2R = 0.86; p-value 0.77; J-test 15.06.
- Interpretation (n = 9 highlighted as most statistically significant):
  - β = 1.891: a one-percent rise in expected inflation induces a 1.891 percent appreciation of the TWI, implying a real exchange rate appreciation of 0.891 percent, ceteris paribus.
  - γ = 0.423: a one-percent positive industrial output gap leads to a 0.423 percent appreciation of the TWI.
  - ρ ≅ 0.85: high degree of policy inertia; MAS adjusts the exchange rate slowly to its projected target.
- Robustness and nonlinear response:
  - Estimates excluding observations where CPI inflation is within 1 standard deviation (high inflation volatility) and excluding observations where industrial output is within 1 standard deviation (high output volatility) continue to show significant positive coefficients on expected inflation and output gaps (Tables 4 and 5).
  - Evidence suggests MAS reacts primarily to large deviations in inflation and output; small shocks elicit little policy response.
- Fit and historical performance:
  - The estimated reaction function tracks actual TWI changes relatively well for 1991–April 2001.
  - Deviations during the Asian crisis: model tracked well at inception; policy tighter than rule in early 1998, then easier at end-1998/beginning-1999.

### New Phillips Curve (NPC) for Singapore
- Rationale:
  - NPC bases inflation dynamics on real marginal costs (empirically proxied by real unit labor cost or domestic supply price index) and microfounded price setting (Calvo-style staggered pricing).
  - Hybrid NPC combines forward-looking and backward-looking components.
- Estimated specification and results (quarterly data 1981:01–2002:01):
  - Marginal cost coefficient: 0.025 (positive and significantly different from zero).
  - Estimated degree of price rigidity θ = 0.743 → implies average duration of prices around 4 quarters.
  - Fraction of forward-looking price setters π ≈ 0.464.
  - Backward- and forward-looking inflation components bα and fα ≈ 0.4 and 0.6, respectively.
- Performance:
  - The estimated NPC tracks actual inflation relatively well (see Figure 3), suggesting the NPC is a viable forecasting tool to reduce inflation forecast errors and assist monetary policy.

### Conclusions and policy implications
- Empirical evidence:
  1. Inflation volatility is driven by multiple macro factors; TWI has relatively little short-run effect on CPI inflation but becomes more influential in the medium term (TWI shares rise to ~22.930 percent at 12 months in the CPI variance decomposition).
  2. Estimated reaction function indicates MAS pursues a forward-looking orientation toward stabilizing expected inflation and output, with significant exchange rate smoothing (ρ ≅ 0.85).
  3. The New Phillips Curve fits Singapore data well; NPC parameters imply moderate price rigidity (θ = 0.743) and a meaningful role for forward-looking behavior (π ≈ 0.464).
- Policy recommendations and implications:
  - Link the exchange-rate-centered operating instrument to observable economic fundamentals and improve communication of how fundamentals affect policy to enhance transparency and reduce market noise.
  - Use NPC-based inflation forecasts to improve forecasts of expected inflation—important because the forward-looking reaction function relies on the policymaker’s capacity to forecast inflation accurately.
  - Continue disclosure steps (e.g., Monetary Policy Statement, Macroeconomic Review) to improve transparency, better anchor inflation expectations, and explain how medium-term trends affect price stability.
- Additional observations:
  - The VAR and reaction-function evidence imply that systematic (predictable) policy actions may be more important than unanticipated monetary shocks for explaining output and inflation dynamics.
  - The MAS reaction function provides a useful yardstick for characterizing historical monetary policy behavior in Singapore.

*Source: _wp0410 - References.*

### References..............................................................................................................

### _wp0410 - References

### Tables
- 1.   CPI   Inflation   Decomposition ............................................................................................... 9
- 2.   Industrial Production Decomposition .................................................................................. 9
- 3.   MAS Reaction Function, Baseline .................................................................................... 12
- 4.   MAS Reaction Function, High Inflation Volatility Case................................................... 13
- 5.   MAS Reaction Function, High Output Gap Volatility Case.............................................. 13

### Figures
- 1a. CPI Versus TWI................................................................................................................... 5
- 1b. Real GDP Versus TWI.........................................................................................................5
- 2.   MAS Target Variation in the TWI Versus Actual TWI .................................................... 14

*Source: _wp0410 - References.*

### 3.   New Phillips Curve for Singapore ..................................................................................

### 3.   New Phillips Curve for Singapore

### Introduction
- Objective: Investigate Singapore’s monetary policy process by estimating a reaction function characterizing how the monetary policy stance (proxied by changes in the trade-weighted nominal exchange rate index, TWI) has historically reacted to shifts in expected inflation and its determinants.
- Analytical steps:
  - Variance decomposition to assess key macroeconomic factors affecting inflation and activity.
  - Specification and estimation of a forward-looking reaction function for changes in the TWI.
  - Computation and evaluation of a New Phillips Curve (NPC) to forecast inflation.
- Key contextual points:
  - MAS manages the Singapore dollar against an undisclosed trade-weighted currency basket since 1981.
  - Policy is forward-looking and centered on maintaining price stability by guiding the TWI.

### Current monetary framework
- Operational facts:
  - MAS manages the Singapore dollar against an undisclosed trade-weighted basket of currencies; composition is reviewed periodically.
  - MAS guides the exchange rate to appreciate or depreciate depending mainly on expected inflationary pressures.
  - Implementation: direct foreign exchange sales/purchases of the U.S. dollar; money market operations (foreign exchange swaps, interbank lending/borrowing, sales/purchases or repurchase agreements in government securities) to offset short-term liquidity fluctuations.
- Historical narrative highlights (selected):
  - TWI appreciated by 30 percent during 1981–1985 (averaging about 5 percent per year) to contain inflation.
  - TWI depreciated about 16 percent during 1985–1988.
  - MAS allowed the TWI to appreciate from 1988 through 1997; trend appreciation ended during the Asian crisis when policy shifted to a zero-appreciation band.
  - In July 2001 MAS eased to a neutral setting centered on a zero percent appreciation band; post-September 11 the band boundaries were widened then narrowed subsequently.

### Sources of output and inflation volatility (VAR analysis)
- Model specification:
  - Six-variable VAR using monthly data (sample 1991:01–2002:04): industrial production, CPI inflation, world commodity price index, foreign interest rate (U.S. Federal Funds rate), domestic interest rate, and the TWI.
  - Two variables treated as exogenous: world commodity price index and foreign interest rate.
  - VAR performed using log differences in 12 months except interest rates in levels; twelve lags used.
- CPI inflation variance decomposition (Table 1, 1991:01–2002:04):
  - Horizon = 1 (Std. Error 0.003): ∆World Commodity Price Index 4.627, ∆Industrial Production 1.865, CPI Inflation 93.508, Foreign Interest Rate 0.000, Domestic Interest Rate 0.000, ∆TWI 0.000.
  - Horizon = 6 (Std. Error 0.006): ∆World Commodity Price Index 7.069, ∆Industrial Production 48.292, CPI Inflation 25.123, Foreign Interest Rate 7.242, Domestic Interest Rate 3.594, ∆TWI 8.679.
  - Horizon = 12 (Std. Error 0.010): ∆World Commodity Price Index 7.560, ∆Industrial Production 55.603, CPI Inflation 9.248, Foreign Interest Rate 2.958, Domestic Interest Rate 1.701, ∆TWI 22.930.
  - Horizon = 18 (Std. Error 0.012): ∆World Commodity Price Index 11.737, ∆Industrial Production 49.568, CPI Inflation 8.900, Foreign Interest Rate 5.277, Domestic Interest Rate 1.664, ∆TWI 22.854.
  - Horizon = 24 (Std. Error 0.015): ∆World Commodity Price Index 8.726, ∆Industrial Production 45.062, CPI Inflation 6.412, Foreign Interest Rate 20.066, Domestic Interest Rate 2.476, ∆TWI 17.257.
- Key inflation-decomposition findings:
  - Real domestic activity (industrial output) explains a large share of CPI inflation forecast errors in the short-to-medium term (over 50 percent of variance in the first year).
  - The monetary policy shock (changes in the TWI) has relatively little impact on CPI inflation initially but becomes more influential after the third quarter (reaching around 22.930 percent at 12 months and similar values at 18 months).
  - Empirical pass-through: ceteris paribus, a 1 percent depreciation in the TWI would increase import prices by about 0.25 percent by the fourth quarter.
- Industrial production variance decomposition (Table 2, 1991:01–2002:04):
  - Horizon = 1 (Std. Error 0.055): ∆World Commodity Price Index 0.196, ∆Industrial Production 99.804, CPI Inflation 0.000, Foreign Interest Rate 0.000, Domestic Interest Rate 0.000, ∆TWI 0.000.
  - Horizon = 6 (Std. Error 0.083): ∆World Commodity Price Index 7.262, ∆Industrial Production 74.542, CPI Inflation 4.829, Foreign Interest Rate 8.092, Domestic Interest Rate 4.275, ∆TWI 1.000.
  - Horizon = 12 (Std. Error 0.098): ∆World Commodity Price Index 21.391, ∆Industrial Production 58.197, CPI Inflation 4.534, Foreign Interest Rate 8.067, Domestic Interest Rate 5.259, ∆TWI 2.551.
  - Horizon = 18 (Std. Error 0.129): ∆World Commodity Price Index 17.358, ∆Industrial Production 64.572, CPI Inflation 3.517, Foreign Interest Rate 7.989, Domestic Interest Rate 3.876, ∆TWI 2.686.
  - Horizon = 24 (Std. Error 0.145): ∆World Commodity Price Index 24.043, ∆Industrial Production 55.972, CPI Inflation 3.076, Foreign Interest Rate 10.065, Domestic Interest Rate 3.175, ∆TWI 3.667.
- Key output-decomposition findings:
  - Foreign shocks explain a large share of industrial production volatility (world commodity price shock explains 24.043 percent after two years; foreign interest rate explains about 10.065 percent).
  - Changes in the TWI have relatively little impact on domestic industrial output throughout the forecast horizon.

### Monetary policy reaction function (TWI as policy instrument)
- Conceptual specification:
  - MAS target change in TWI, e*_t ∆ , is a function of expected inflation (between t and t+n) and expected real output (between t and t+m), with partial adjustment and exchange rate smoothing parameter ρ ∈ [0,1].
  - Estimation via GMM with instruments: lags 1–6, 9, and 12 of CPI inflation, industrial production gap, and the TWI.
  - Sample: 1991:01–2002:04, using year-on-year CPI inflation, detrended industrial output (Hodrick-Prescott), and year-on-year change in the TWI.
- Baseline estimated coefficients (Table 3; selected reported horizons):
  - Current Inflation (n = 0): α = 0.005 (t = 0.82); β = 0.975 (t = 2.56); γ = 0.137 (t = 1.28); ρ = 0.838 (t = 32.44); 2R = 0.86; p-value 0.66; J-test 16.87.
  - Expected Inflation (n = 6): α = -0.006 (t = -0.79); β = 1.44 (t = 3.37); γ = 0.235 (t = 2.30); ρ = 0.831 (t = 30.27); 2R = 0.83; p-value 0.75; J-test 15.47.
  - Expected Inflation (n = 9): α = -0.008 (t = -0.91); β = 1.891 (t = 3.41); γ = 0.423 (t = 3.07); ρ = 0.847 (t = 37.80); 2R = 0.86; p-value 0.85; J-test 13.70.
  - Expected Inflation (n = 12): α = -0.005 (t = -0.41); β = 1.824 (t = 2.34); γ = 0.525 (t = 2.49); ρ = 0.871 (t = 40.68); 2R = 0.86; p-value 0.77; J-test 15.06.
- Interpretation (n = 9 highlighted as most statistically significant):
  - β = 1.891: a one-percent rise in expected inflation induces a 1.891 percent appreciation of the TWI, implying a real exchange rate appreciation of 0.891 percent, ceteris paribus.
  - γ = 0.423: a one-percent positive industrial output gap leads to a 0.423 percent appreciation of the TWI.
  - ρ ≅ 0.85: high degree of policy inertia; MAS adjusts the exchange rate slowly to its projected target.
- Robustness and nonlinear response:
  - Estimates excluding observations where CPI inflation is within 1 standard deviation (high inflation volatility) and excluding observations where industrial output is within 1 standard deviation (high output volatility) continue to show significant positive coefficients on expected inflation and output gaps (Tables 4 and 5).
  - Evidence suggests MAS reacts primarily to large deviations in inflation and output; small shocks elicit little policy response.
- Fit and historical performance:
  - The estimated reaction function tracks actual TWI changes relatively well for 1991–April 2001.
  - Deviations during the Asian crisis: model tracked well at inception; policy tighter than rule in early 1998, then easier at end-1998/beginning-1999.

### New Phillips Curve (NPC) for Singapore
- Rationale:
  - NPC bases inflation dynamics on real marginal costs (empirically proxied by real unit labor cost or domestic supply price index) and microfounded price setting (Calvo-style staggered pricing).
  - Hybrid NPC combines forward-looking and backward-looking components.
- Estimated specification and results (quarterly data 1981:01–2002:01):
  - Marginal cost coefficient: 0.025 (positive and significantly different from zero).
  - Estimated degree of price rigidity θ = 0.743 → implies average duration of prices around 4 quarters.
  - Fraction of forward-looking price setters π ≈ 0.464.
  - Backward- and forward-looking inflation components bα and fα ≈ 0.4 and 0.6, respectively.
- Performance:
  - The estimated NPC tracks actual inflation relatively well (see Figure 3), suggesting the NPC is a viable forecasting tool to reduce inflation forecast errors and assist monetary policy.

### Conclusions and policy implications
- Empirical evidence summarized:
  1. Inflation volatility is driven by multiple macro factors; TWI has relatively little short-run effect on CPI inflation but becomes more influential in the medium term (TWI shares rise to ~22.930 percent at 12 months in the CPI variance decomposition).
  2. Estimated reaction function indicates MAS pursues a forward-looking orientation toward stabilizing expected inflation and output, with significant exchange rate smoothing (ρ ≅ 0.85).
  3. The New Phillips Curve fits Singapore data well; NPC parameters imply moderate price rigidity (θ = 0.743) and a meaningful role for forward-looking behavior (π ≈ 0.464).
- Policy recommendations and implications:
  - A policy framework that links the exchange-rate-centered operating instrument to observable economic fundamentals (and clearer communication of how fundamentals affect policy) could enhance transparency and reduce market noise.
  - NPC-based inflation forecasts can provide MAS with improved forecasts of expected inflation—important because the forward-looking reaction function relies on the policymaker’s capacity to forecast inflation accurately.
  - Continued disclosure steps (e.g., Monetary Policy Statement, Macroeconomic Review) are consistent with improving transparency to better anchor inflation expectations and explain how medium-term trends affect price stability.
- Additional observations:
  - The VAR and reaction-function evidence imply that systematic (predictable) policy actions may be more important than unanticipated monetary shocks for explaining output and inflation dynamics.
  - The MAS reaction function provides a useful yardstick for characterizing historical monetary policy behavior in Singapore.

*Source: IMF staff estimates and analysis in “New Phillips Curve for Singapore” (section 3 of the provided document, sample periods and figures as reported).*

### REFERENCES

### REFERENCES

### Cited works

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- Clarida, Richard, and Mark Gertler, 1997, “How the Bundesbank Conducts Monetary Policy,” in Reducing Inflation: Motivation and Strategy, ed. by Christina Romer and David Romer (Chicago: University of Chicago Press).  
- Clarida, Richard, Jordi Galí, and Mark Gertler, 1999, “The Science of Monetary Policy: A New Keynesian Perspective,” Journal of Economic Literature, Vol. 37, No. 4, pp. 1661–707.  
- Cushman, David, and Tao Zha, 1997, “Identifying Monetary Policy in a Small Open Economy under Flexible Exchange Rates,” Journal of Monetary Economics, Vol. 39, No. 3, pp. 433–48.  
- Fung, Ben, 2002, “A VAR Analysis of the Effects of Monetary Policy in East Asia,” BIS Working Paper 119 (Basel: Bank for International Settlements).  
- Gagnon, Joseph and Jane Ihrig, 2001, “Monetary Policy and Exchange Rate Pass-Through,” International Finance Discussion Papers 704 (Washington DC: Board of Governors of the Federal Reserve System).  
- Galí, Jordi and Mark Gertler, 1999, “Inflation Dynamics: A Structural Econometric Analysis, Journal of Monetary Economics, Vol. 44, No. 2, pp. 195–222.  
- Gerlach, Stefan, and Lars Svensson, 2001, “Money and Inflation in the Euro Area: A Case for Monetary Indicators,” BIS Working Paper 98 (Basel: Bank for International Settlements).  
- Haldane, Andrew, and Danny Quah, 1999, “UK Phillips Curves and Monetary Policy,” Journal of Monetary Economics, Vol. 44, No. 2, pp. 259–78.  
- Hansen, Lars, 1982, “Large Sample Properties of Generalized Method of Moments Estimators,” Econometrica, Vol. 50, No. 4, pp. 1029–54.  
- Kim, Soyoung, 1999, “Do Monetary Policy Shocks Matter in the G-7 Countries? Using Common Identifying Assumptions about Monetary Policy across Countries,” Journal of International Economics, Vol. 48, No. 2, pp. 387–412.  
- ———, and Nouriel Roubini, 2000, “Exchange Rate Anomalies in the Industrial Countries: A Solution with a Structural VAR Approach,” Journal of Monetary Economics, Vol. 45, No. 3, pp. 561–86.  
- King, Robert, and Mark Watson, 1994, “The Post-War U.S. Phillips Curve: A Revisionist Econometric History,” Carnegie-Rochester Conference on Public Policy, Vol. 41, pp. 157–219.  
- Kool, Clemens, and John Tatom, 1994, “The P-Star Model in Five Small Economies,” Federal Reserve Bank of St. Louis Quarterly Review, pp. 11–29.  
- Monetary Authority of Singapore, 1998, Economic Issues (Singapore).  
- ———, 2000, “A Survey of Singapore’s Monetary Policy,” Occasional Paper No. 18 (Singapore).  
- ———, 2001a, “Singapore’s Exchange Rate Policy.”  
- ———, 2001b, Quarterly Bulletin, Vol. III, Issue 3, October.  
- ———, 2002, Macroeconomic Review, Vol. 1, Issues 1 and 2.  
- ———, 2003, “Investigating the Relationship Between Exchange Rate Volatility and Macroeconomic Volatility in Singapore,” MAS Staff Paper No. 25 (Singapore).  
- Nadal De Simone, 2000, “Monetary and Fiscal Policy Interaction in a Small Open Economy: The Case of Singapore,” Asian Economic Journal, Vol. 14, No. 2, pp. 211-31.  
- Parrado, Eric, and Andrés Velasco, 2001, “Alternative Monetary Rules in the Open Economy: A Welfare-Based Approach,” in Inflation Targeting: Design, Performance, Challenges, ed. by Norman Loayza and Raimundo Soto (Santiago: Central Bank of Chile).  
- ———, 2002, “Optimal Interest Rate Policy in a Small Open Economy,” NBER Working Paper No. 8721 (Cambridge, Massachusetts: National Bureau of Economic Research).  
- Sbodorne, Argia, 1999, “Prices and Unit Labor Costs: A New Test of Price Stickiness,” (unpublished; New Jersey: Rutgers University).  
- Sims, Christopher, 1992, “Interpreting the Macroeconomic Time Series Facts: The Effects of Monetary Policy,” European Economic Review, Vol. 36, No. 5, pp. 975-1000.  
- Stock, James, and Mark Watson, 1999, “Forecasting Inflation,” Journal of Monetary Economics, Vol. 44, No. 2, pp. 293–335.  
- Taylor, John, 1993, “Discretion versus Policy Rules in Practice,” Carnegie-Rochester Conference Series on Public Policy, Vol. 39, pp.195–214.  
- ———, 2000, “Low Inflation, Pass-Through, and the Pricing Power of Firms,” European Economic Review, Vol. 44, No. 7, pp.1389–408.  
- Woodford, Michael, 2003, “A Neo-Wicksellian Framework for the Analysis of Monetary Policy,” in Interest & Prices (Princeton: Princeton University Press).

*Source: _wp0410 - REFERENCES*

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