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Forecasting with VARMA Models

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Helmut Luetkepohl

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Abstract

Vector autoregressive moving-average (VARMA) processes are suitable models for producing linear forecasts of sets of time series variables. They provide parsimonious representations of linear data generation processes (DGPs). The setup for these processes in the presence of cointegrated variables is considered. Moreover, a unique or identified parameterization based on the echelon form is presented. Model specification, estimation, model checking and forecasting are discussed. Special attention is paid to forecasting issues related to contemporaneously and temporally aggregated processes.

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Paper provided by European University Institute in its series Economics Working Papers with number ECO2004/25.

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Date of creation: 2004
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Handle: RePEc:eui:euiwps:eco2004/25

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C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models

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  7. Lutkepohl, Helmut, 1986. "Forecasting Vector ARMA Processes with Systematically Missing Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(3), pages 375-90, July.
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  12. Gregoir, Stephane & Laroque, Guy, 1994. "Polynomial cointegration estimation and test," Journal of Econometrics, Elsevier, vol. 63(1), pages 183-214, July. [Downloadable!] (restricted)
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  22. Masarotto, Guido, 1990. "Bootstrap prediction intervals for autoregressions," International Journal of Forecasting, Elsevier, vol. 6(2), pages 229-239, July. [Downloadable!] (restricted)
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    Other versions:
  24. Lutkepohl, Helmut, 1985. "The joint asymptotic distribution of multistep prediction errors of estimated vector autoregressions," Economics Letters, Elsevier, vol. 17(1-2), pages 103-106. [Downloadable!] (restricted)
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