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Residual Autocorrelation Testing for Vector Error Correction Models

Author

Listed:
  • Ralf BRUEGGEMANN
  • Helmut LUETKEPOHL
  • Pentti SAIKKONEN

Abstract

In applied time series analysis, checking for autocorrelation in a fitted model is a routine diagnostic tool. Therefore it is useful to know the asymptotic and small sample properties of the standard tests for the case when some of the variables are cointegrated. The properties of residual autocorrelations of vector error correction models (VECMs) and tests for residual autocorrelation are derived. In particular, the asymptotic distributions of Lagrange multiplier (LM) and portmanteau tests are given. Monte Carlo simulations show that the LM tests have advantages if autocorrelation of small order is tested whereas portmanteau tests are preferable for higher order residual autocorrelation. Their critical values have to be adjusted for the cointegration rank of the system, however.

Suggested Citation

  • Ralf BRUEGGEMANN & Helmut LUETKEPOHL & Pentti SAIKKONEN, 2004. "Residual Autocorrelation Testing for Vector Error Correction Models," Economics Working Papers ECO2004/08, European University Institute.
    • Handle: RePEc:eui:euiwps:eco2004/08
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    References listed on IDEAS

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    1. Ralf Brüggemann & Helmut Lütkepohl, 2005. "Practical Problems with Reduced‐rank ML Estimators for Cointegration Parameters and a Simple Alternative," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(5), pages 673-690, October.
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    6. Efstathios Paparoditis, 2005. "Testing the Fit of a Vector Autoregressive Moving Average Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(4), pages 543-568, July.
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    More about this item

    Keywords

    cointegration; dynamic econometric models; vector autoregressions; vector error correction models; residual autocorrelation;
    All these keywords.

    JEL classification:

    • 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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