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Conditional Inference in Cointegrating Vector Autoregressive Models

Author

Listed:
  • Sophocles Mavroeidis
  • Kees Jan van Garderen

Abstract

A Vector Autoregressive model (VAR) with normally distributed innovations is a Curved Exponential Model (CEM). Cointegration imposes further curvature on the model and this means that in addition to the important reasons for conditioning in non-stationary time series as given by Johansen (1995, EJ), there are further reasons due to the curvature of the model. This paper investigates the effects of conditioning on the likelihood ratio test statistic for the cointegrating rank, which in this case is a natural approximate ancillary statistic. We investigate the effect of conditioning on this test statistic for inference on the long-run (beta) and also on the speed-of-adjustment (alpha) coefficients. We show that this conditioning gives virtually the same estimates of the estimator variance as using the observed information instead of the expected information. We examine the possibility of achieving asymptotic refinements for inference on alpha using a conditioning parametric bootstrap procedure

Suggested Citation

  • Sophocles Mavroeidis & Kees Jan van Garderen, 2004. "Conditional Inference in Cointegrating Vector Autoregressive Models," Econometric Society 2004 Australasian Meetings 211, Econometric Society.
    • Handle: RePEc:ecm:ausm04:211
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    More about this item

    Keywords

    Ancillary statistics; Multivariate Non-stationary time series;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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