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Robustness of the CUSUM and CUSUM-of-Squares Tests to Serial Correlation, Endogeneity and Lack of Structural Invariance. Some Monte Carlo Evidence

Author

Listed:
  • Caporale, Guglielmo Maria

    (London South Bank University)

  • Pittis, Nikitas

    (University of Piraeus)

Abstract

This paper investigates by means of Monte Carlo techniques the robustness of the CUSUM and CUSUM-of-squares tests (Brown et al., 1975) to serial correlation, endogeneity and lack of structural invariance. Our findings suggest that these tests perform better in the context of a dynamic model of the ADL type, which is not affected by serial correlation or nonpredetermined regressors even if over-specified. In this case, the empirical sizes of both tests are close to the nominal ones, whether a stationary or a cointegration environment is considered. The CUSUM-of-squares test is to be preferred, as it is very powerful to detect changes in the conditional model parameters, whether or not the variance of the regression error is included in the set of parameters shifting, especially towards the end of the sample.

Suggested Citation

  • Caporale, Guglielmo Maria & Pittis, Nikitas, 2004. "Robustness of the CUSUM and CUSUM-of-Squares Tests to Serial Correlation, Endogeneity and Lack of Structural Invariance. Some Monte Carlo Evidence," Economics Series 157, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihsesp:157
    as

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    File URL: https://irihs.ihs.ac.at/id/eprint/1569
    File Function: First version, 2004
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    References listed on IDEAS

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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