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Bias Nonmonotonicity in Stochastic Difference Equations

Author

Listed:
  • Abadir, Karim
  • Hadri, K.

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.

Suggested Citation

  • Abadir, Karim & Hadri, K., 1995. "Bias Nonmonotonicity in Stochastic Difference Equations," Discussion Papers 9512, University of Exeter, Department of Economics.
    • Handle: RePEc:exe:wpaper:9512
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    Cited by:

    1. Hadri, Kaddour & Guermat, Cherif & Whittaker, Julie, 2003. "Estimating Farm Efficiency in the Presence of Double Heteroscedasticity Using Panel Data," Journal of Applied Economics, Universidad del CEMA, vol. 6(2), pages 1-14, November.
    2. 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.

    More about this item

    Keywords

    ECONOMETRICS;

    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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