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Bias Nonmonotonicity in Stochastic Difference Equations

Author

Listed:
  • Karim Abadir
  • Kaddour Hadri

Abstract

We show that the bias of estimated parameters in autoregressive models can increase as the sample size grows. This unusual result is due to the effect of the initial sample observations that are typically neglected in theoretical asymptotoc analysis, in spite of their empirical relevance. Implications for practical economic modelling are considered.
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Suggested Citation

  • Karim Abadir & Kaddour Hadri, "undated". "Bias Nonmonotonicity in Stochastic Difference Equations," Discussion Papers 96/15, Department of Economics, University of York.
    • Handle: RePEc:yor:yorken:96/15
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    Cited by:

    1. Cheung Ip, Wai & Phillips, Garry D. A., 1998. "The non-monotonicity of the bias and mean squared error of the two stage least squares estimators of exogenous variable coefficients," Economics Letters, Elsevier, vol. 60(3), pages 303-310, September.
    2. Kaddour Hadri & Cherif Guermat & Julie Whittaker, 2003. "Estimating Farm Efficiency in the Presence of Double Heteroscedasticity Using Panel Data," Journal of Applied Economics, Universidad del CEMA, vol. 6, pages 255-268, November.

    More about this item

    JEL classification:

    • 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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