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Forecasting inflation in Malaysia

Author

Listed:
  • Jarita Duasa

    (Department of Economics, Kuliyyah of Economics and Management Sciences, International Islamic University Malaysia, Kuala Lumpur, Malaysia)

  • Nursilah Ahmad

    (Fakulti Ekonomi and Muamalat, University Sains Islam Malaysia, Negeri Sembilan, Malaysia)

  • Mansor H. Ibrahim

    (Department of Economics, Faculty of Economics and Management, Universiti Putra Malaysia, Selangor Darul Ehsan, Malaysia)

  • Mohd Pisal Zainal

    (International Centre for Education in Islamic Finance (INCEIF), Kuala Lumpur, Malaysia)

Abstract

This paper aims to identify the best indicator in forecasting inflation in Malaysia. In methodology, the study constructs a simple forecasting model that incorporates the indicator|variable using the vector error correction (VECM) model of quasi-tradable inflation index and selected indicators: commodity prices, financial indicators and economic activities. For each indicator, the forecasting horizon used is 24 months and the VECM model is applied for seven sample windows over sample periods starting with the first month of 1980 and ending with the 12th month of every 2 years from 1992 to 2004. The degree of independence of each indicator from inflation is tested by analyzing the variance decomposition of each indicator and Granger causality between each indicator and inflation. We propose that a simple model using an aggregation of indices improves the accuracy of inflation forecasts. The results support our hypothesis. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Jarita Duasa & Nursilah Ahmad & Mansor H. Ibrahim & Mohd Pisal Zainal, 2010. "Forecasting inflation in Malaysia," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(6), pages 573-594.
  • Handle: RePEc:jof:jforec:v:29:y:2010:i:6:p:573-594 DOI: 10.1002/for.1154
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    References listed on IDEAS

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    1. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    2. Stephen G. Cecchetti & Rita S. Chu & Charles Steindel, 2000. "The unreliability of inflation indicators," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 6(Apr).
    3. Obstfeld, Maurice & Taylor, Alan M., 1997. "Nonlinear Aspects of Goods-Market Arbitrage and Adjustment: Heckscher's Commodity Points Revisited," Journal of the Japanese and International Economies, Elsevier, vol. 11(4), pages 441-479, December.
    4. Rana, Pradumna B & Dowling, J Malcolm, Jr, 1985. "Inflationary Effects of Small but Continuous Changes in Effective Exchange Rates: Nine Asian LDCs," The Review of Economics and Statistics, MIT Press, vol. 67(3), pages 496-500, August.
    5. Michael F. Bryan & Stephen G. Cecchetti, 1993. "The consumer price index as a measure of inflation," Economic Review, Federal Reserve Bank of Cleveland, issue Q IV, pages 15-24.
    6. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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    Cited by:

    1. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    2. Hayashi, Masayoshi, 2014. "Forecasting welfare caseloads: The case of the Japanese public assistance program," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 105-114.

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