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Forecasting methods: a comparative analysis

Author

Listed:
  • Iqbal, Javed

Abstract

Forecasting is an important tool for management, planning and administration in various fields. In this paper forecasting performance of different methods is considered using time series data of Pakistan's export to United Sates and money supply. It is found that, like other studies of this nature, no single forecasting method provides better forecast for both the series. The techniques considered are ARIMA, Regression Analysis, Vector Autoregression (VAR), Error Correction Model (ECM) and ARCH/GARCH models.

Suggested Citation

  • Iqbal, Javed, 2001. "Forecasting methods: a comparative analysis," MPRA Paper 23856, University Library of Munich, Germany, revised 2001.
    • Handle: RePEc:pra:mprapa:23856
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    References listed on IDEAS

    as
    1. Manmohan S. Kumar, 1992. "The Forecasting Accuracy of Crude Oil Futures Prices," IMF Staff Papers, Palgrave Macmillan, vol. 39(2), pages 432-461, June.
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    3. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    4. LeSage, James P, 1990. "A Comparison of the Forecasting Ability of ECM and VAR Models," The Review of Economics and Statistics, MIT Press, vol. 72(4), pages 664-671, November.
    5. Lin Chan, Hing & Kam Lee, Shu, 1997. "Modelling and forecasting the demand for coal in China," Energy Economics, Elsevier, vol. 19(3), pages 271-287, July.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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