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The Forecasting Accuracy of Five Time Series Models: Evidence from the Portuguese Car Market

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  • Francisco F. R. Ramos

    (Faculty of Economics, University of Porto, Portugal)

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    Abstract

    This paper compares the out-of-sample forecasting accuracy of five classes of time series models for market shares of the six most important Portuguese car market competitors over differents horizons. As representative time series models I employ a random-walk with drift (Naive), a univariate ARIMA, a near-VAR and a general BVAR. The out-of- sample forecasts are also compared against forecasts generated from structural econometric market share models (SEM). Using four accuracy measures I find the forecasts from the near-VAR and the BVAR models really more accurate. With regard to these models, I could say that the BVAR model is the best for longer forecasts (12-steps ahead), while the n-VAR is superior over the shorter horizon of one to six steps.

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    Bibliographic Info

    Paper provided by EconWPA in its series Econometrics with number 9604002.

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    Length: 21 pages
    Date of creation: 09 Apr 1996
    Date of revision:
    Handle: RePEc:wpa:wuwpem:9604002

    Note: Type of Document - Word for Windows 2.0; prepared on IBM PC ; to print on HP Laser Jet; pages: 21 ; figures: one figure and one table
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    Related research

    Keywords: Accuracy measures; ARIMA; Automobile market; BVAR; Market share; Portugal; Random-walk; SEM; VAR;

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    References

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    1. Danaher, Peter J. & Brodie, Roderick J., 1992. "Predictive accuracy of simple versus complex econometric market share models: Theoretical and empirical results," International Journal of Forecasting, Elsevier, vol. 8(4), pages 613-626, December.
    2. Brodie, Roderick J. & De Kluyver, Cornelis A., 1987. "A comparison of the short term forecasting accuracy of econometric and naive extrapolation models of market share," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 423-437.
    3. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    4. Hsiao, Cheng, 1979. "Causality tests in econometrics," Journal of Economic Dynamics and Control, Elsevier, vol. 1(4), pages 321-346, November.
    5. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January.
    6. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
    7. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
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