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Moment Approximation for Least Squares Estimators in Dynamic Regression Models with a Unit Root

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  • Jan F. Kiviet

    ()
    (University of Amsterdam)

  • Garry D.A. Phillips

    (Cardiff Business School, Cardiff, Wales, UK)

Abstract

This discussion paper led to a publication in 'The Econometrics Journal' .Asymptotic expansions are employed in a dynamic regression model with a unit root inorder to find approximations for the bias, the variance and for the mean squared error of theleast-squares estimator of all coefficients. It is found that in this particular context suchexpansions exist only when the autoregressive model contains at least one non-redundant exogenousexplanatory variable and that local to zero asymptotic approaches are here without avail.Surprisingly the large sample and small disturbance asymptotic techniques give closely relatedresults, which is not the case in stable dynamic regression models. The expressions for momentapproximations are specialized to the random walk with (trend in) drift model and their accuracyis examined in Monte Carlo experiments.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 01-118/4.

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Date of creation: 06 Dec 2001
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Handle: RePEc:dgr:uvatin:20010118

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Web page: http://www.tinbergen.nl

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Cited by:
  1. Pesaran, M.H. & Timmermann, A., 2003. "Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks," Cambridge Working Papers in Economics 0331, Faculty of Economics, University of Cambridge.
  2. Liu-Evans, Gareth, 2010. "An alternative approach to approximating the moments of least squares estimators," MPRA Paper 26550, University Library of Munich, Germany.
  3. Chevillon, Guillaume, 2007. "Inference in the Presence of Stochastic and Deterministic Trends," ESSEC Working Papers DR 07021, ESSEC Research Center, ESSEC Business School.

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