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Robustness against priors and mixing distributions

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  • Tiemen Woutersen

Abstract

Neyman and Scott define the incidental-parameter problem. In panel data with $T$ observations per individual, the estimator of the common parameter is usually constistent with O(1/T). This paper shows that the integrated likelihood estimator becomes consistent with O(1/T^2) if an information-orthogonal likelihood is used. Information-orthogonal likelihoods for the general linear model are derived along with an index model with weakly exogenous variables. An approximate solution for the incidental-parameter problem for a wide range of models is given. The paper further argues that reparametrizations are easier in a Bayesian framework. An example shows how to use the O(1/T^2) result to increase robustness against choosing the mixing distribution. Likelihood methods that use sufficient statistics for the individual effects are seen to be a special case of the integrated-likelihood estimator.

Suggested Citation

  • Tiemen Woutersen, 2001. "Robustness against priors and mixing distributions," Computing in Economics and Finance 2001 168, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:168
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    More about this item

    Keywords

    Heterogeneity; Adaptive Estimation;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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