Bias Nonmonotonicity in Stochastic Difference Equations
AbstractWe 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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Bibliographic InfoPaper provided by Department of Economics, University of York in its series Discussion Papers with number 96/15.
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Postal: Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
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Other versions of this item:
- Abadir, Karim & Hadri, K., 1995. "Bias Nonmonotonicity in Stochastic Difference Equations," Discussion Papers 9512, Exeter University, Department of Economics.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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- 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. 0, pages 255-268, November.
- 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.
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