Forecasting with VARMA Models

Helmut Luetkepohl
Authors registered in the RePEc Author Service: Helmut Lütkepohl

No ECO2004/25, Economics Working Papers from European University Institute

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.

JEL-codes: C32 (search for similar items in EconPapers)
Date: 2004
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (6)

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Chapter: Forecasting with VARMA Models (2006)
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