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 Exeter University, Department of Economics in its series Discussion Papers with number 9512.
Length: 9 pages
Date of creation: 1995
Date of revision:
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Other versions of this item:
- Karim Abadir & Kaddour Hadri, . "Bias Nonmonotonicity in Stochastic Difference Equations," Discussion Papers 96/15, Department of Economics, University of York.
- 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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