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Exact joint forecast regions for vector autoregressive models

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  • Wai-Sum Chan
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    Abstract

    Assume that a k-element vector time series follows a vector autoregressive (VAR) model. Obtaining simultaneous forecasts of the k elements of the vector time series is an important problem. Based on the Bonferroni inequality, Lutkepohl (1991) derived the procedures which construct the conservative joint forecast regions for the VAR model. In this paper, we propose to use an exact method which provides shorter prediction intervals than does the Bonferroni method. Three illustrative examples are given for comparison of the various VAR forecasting procedures.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

    Volume (Year): 26 (1999)
    Issue (Month): 1 ()
    Pages: 35-44

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    Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:35-44

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    1. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-44, April.
    2. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    3. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-35, April.
    4. Edlund, Per-Olov & Karlsson, Sune, 1993. "Forecasting the Swedish unemployment rate VAR vs. transfer function modelling," International Journal of Forecasting, Elsevier, vol. 9(1), pages 61-76, April.
    5. Otter, Pieter W, 1990. "Canonical Correlation in Multivariate Time Series Analysis with an Application to One-Year-Ahead and Multiyear-Ahead Macroeconomic Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(4), pages 453-57, October.
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