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Forecasting Thailand’s Core Inflation

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  • Tao Sun

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.

Suggested Citation

  • Tao Sun, 2004. "Forecasting Thailand’s Core Inflation," IMF Working Papers 2004/090, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2004/090
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    References listed on IDEAS

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    1. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    2. Mark A. Wynne, 2008. "Core inflation: a review of some conceptual issues," Review, Federal Reserve Bank of St. Louis, vol. 90(May), pages 205-228.
    3. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    4. Hendry, David F., 2001. "Achievements and challenges in econometric methodology," Journal of Econometrics, Elsevier, vol. 100(1), pages 7-10, January.
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    Cited by:

    1. 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;Central Eastern European University Network (CEEUN), vol. 16(3), pages 671-692, October.
    2. Bilal Raza & Khurram S Mughal, 2022. "Fiscal Determinants of Inflation in Pakistan," SBP Working Paper Series 108, State Bank of Pakistan, Research Department.
    3. 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, vol. 24(3), pages 399-413.
    4. Jalil, Abdul & Tariq, Rabbia & Bibi, Nazia, 2014. "Fiscal deficit and inflation: New evidences from Pakistan using a bounds testing approach," Economic Modelling, Elsevier, vol. 37(C), pages 120-126.

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