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Forecasting Thailand's Core Inflation

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  • Tao Sun
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Abstract

This paper develops an approach for forecasting in Thailand core inflation. The key innovation is to anchor the projections derived from the short-term time-series properties of core inflation to its longer-run evolution. This involves combining a short-term model, which attempts to distill the forecasting power of a variety of monthly indicators purely on goodness-of-fit criteria, with an equilibrium-correction model that pins down the convergence of core inflation to its longer-run structural determinants. The result is a promising model for forecasting Thai core inflation over horizons up to 10, 24, and 55 months, based on a root mean-squared error criterion as well as a mean absolute error criterion.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 04/90.

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Length: 28
Date of creation: 01 May 2004
Date of revision:
Handle: RePEc:imf:imfwpa:04/90

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Related research

Keywords: Forecasting models; inflation; forecasting; cointegration; statistics; equation; time series; standard error; absolute error; money supply; forecasting inflation; monetary policy; inflation rates; probability; fitted value; statistic; price level; heteroscedasticity; statistical model; statistical models; inflation forecasts; standard errors; inflation rate; probabilities; parsimonious model; data transformation; inflation targeting; normal distribution; autocorrelation; correlation; kurtosis; skewness; statistical analysis; econometrics; vector autoregression; nominal interest rate;

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  1. Mark A. Wynne, 2008. "Core inflation: a review of some conceptual issues," Review, Federal Reserve Bank of St. Louis, Federal Reserve Bank of St. Louis, issue May, pages 205-228.
  2. Hendry, David F., 2001. "Achievements and challenges in econometric methodology," Journal of Econometrics, Elsevier, Elsevier, vol. 100(1), pages 7-10, January.
  3. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 12(2-3), pages 231-254.
  4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, Econometric Society, vol. 49(4), pages 1057-72, June.
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Cited by:
  1. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas, 2008. "Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(3), pages 399-413.
  2. Chrigui Zouhair & Boujelbene Younes, 2009. "The Opportunities for Adopting Inflation Targeting in Tunisia: a Cointegration Study and Transmission Channels of Monetary Policy," Transition Studies Review, Springer, Springer, vol. 16(3), pages 671-692, October.
  3. Jalil, Abdul & Tariq, Rabbia & Bibi, Nazia, 2014. "Fiscal deficit and inflation: New evidences from Pakistan using a bounds testing approach," Economic Modelling, Elsevier, Elsevier, vol. 37(C), pages 120-126.

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