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Periodic VAR Processes and Intervention Models

  • Helmut Lütkepohl

Abstract

In the previous chapter we have considered nonstationary VAR models with time invariant parameters. Nonstationarity, that is, time varying first and/or second moments of a process, can also be modeled in the framework of time varying parameter processes. Suppose, for instance, that the time series show a seasonal pattern. In that case a VAR(p) process with different intercept terms for each season may be a reasonable model:
$$ y_t = v + A_1 y_{t - 1} + \cdots + A_p y_{t - p} + u_t $$
(12.1.1)
Here v i is a (K × 1) intercept vector associated with the i-th season, that is, in (12.1.1) the time index t is assumed to be associated with the i-th season of the year. It is easy to see that such a process has a potentially different mean for each season of the year.

Keywords

Intervention Model Exogenous Variable Likelihood Ratio Statistic Nonstationary Process Asymptotic Normal Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Helmut Lütkepohl
    • 1
  1. 1.Institute of Statistics and EconometricsUniversity of KielKielGermany

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