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How to implement the Bootstrap in Static or Stable Dynamic Regression Models

Author

Listed:
  • Noud P.A. van Giersbergen

    (University of Amsterdam)

  • Jan F. Kiviet

    (University of Amsterdam)

Abstract

By combining two alternative formulations of a test statistic with two alternative resamplingschemes we obtain four different bootstrap tests. In the context of static linear regression modelstwo of these are shown to have serious size and power problems, whereas the remaining two areadequate and in fact equivalent. The equivalence between the two valid implementations is shown tobreak down in dynamic regression models. Then the procedure based on the test statistic approachperforms best, at least in the AR(l)-model. Similar finite-sample phenomena are illustrated in theARMA(l,l)-model through a small-scale Monte Carlo study and an empirical example.

Suggested Citation

  • Noud P.A. van Giersbergen & Jan F. Kiviet, 2001. "How to implement the Bootstrap in Static or Stable Dynamic Regression Models," Tinbergen Institute Discussion Papers 01-119/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20010119
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    More about this item

    Keywords

    Asymptotic rejection probabilities; Autoregressive models; Bootstrap; Hypothesis testing; Resampling schemes;
    All these keywords.

    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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