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Is More Information a Good Thing? Bias Nonmonotonicity in Stochastic Difference Equations

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  • Abadir, Karim M
  • Hadri, Kaddour

Abstract

It is shown that the bias of estimated parameters in autoregressive models can increase as the sample size grows. This bias is also a nonmonotonic function of the largest autoregressive root, contrary to what asymptotic approximations had indicated so far in the literature. These unusual results are due to the effect of the initial sample observations that are typically neglected in theoretical asymptotic analysis, in spite of their empirical relevance. Implications for practical economic modelling are considered, including a comparison of the likely inaccuracies of parameter estimates in alternative models based on competing macroeconomic theories. Copyright 2000 by Blackwell Publishing Ltd and the Board of Trustees of the Bulletin of Economic Research

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Bulletin of Economic Research.

Volume (Year): 52 (2000)
Issue (Month): 2 (April)
Pages: 91-100

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Handle: RePEc:bla:buecrs:v:52:y:2000:i:2:p:91-100

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Cited by:
  1. Kaddour Hadri & Yao Rao, 2008. "Panel Stationarity Test with Structural Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(2), pages 245-269, 04.
  2. Steve Lawford & Michalis P. Stamatogiannis, 2008. "The Finite-Sample E ects of VAR Dimensions on OLS Bias, OLS Variance, and Minimum MSE Estimators," Working Paper Series 13-08, The Rimini Centre for Economic Analysis, revised Jan 2008.
  3. Vougas, Dimitrios V., 2006. "Remark on the asymptotic distribution of the OLS estimator in a simple Gaussian unit-root autoregression," Statistics & Probability Letters, Elsevier, vol. 76(1), pages 27-34, January.
  4. Abadir, Karim M. & Talmain, Gabriel, 2005. "Autocovariance functions of series and of their transforms," Journal of Econometrics, Elsevier, vol. 124(2), pages 227-252, February.

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