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


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  • Noud P.A. van Giersbergen

    (University of Amsterdam)

  • Jan F. Kiviet

    (University of Amsterdam)


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.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 01-119/4.

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Date of creation: 06 Dec 2001
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Handle: RePEc:dgr:uvatin:20010119

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Keywords: Asymptotic rejection probabilities; Autoregressive models; Bootstrap; Hypothesis testing; Resampling schemes;

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Cited by:
  1. J. Isaac Miller & Shawn Ni, 2010. "Long-Term Oil Price Forecasts: A New Perspective on Oil and the Macroeconomy," Working Papers 1012, Department of Economics, University of Missouri.
  2. repec:ebl:ecbull:v:3:y:2004:i:13:p:1-8 is not listed on IDEAS


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