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Pooling Estimates With Different Rates Of Convergence: A Minimum Χ2 Approach With Emphasis On A Social Interactions Model

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  • Lee, Lung-fei

Abstract

This paper considers the extension of the classical minimum distance approach for the pooling of estimates with various rates of convergence. Under a setting where relatively high rates of convergence can be attained, the minimum distance estimators are shown to be consistent and asymptotically normally distributed. The constrained estimates can be efficient relative to the unconstrained ones. The minimized distance function is shown to be asymptotically χ 2 -distributed, and can be used as a goodness-of-fit test for the constraints. As the extension is motivated by some social interactions models, which are of interest in their own right, we discuss this approach for the estimation and testing of a social interactions model.

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  • Lee, Lung-fei, 2010. "Pooling Estimates With Different Rates Of Convergence: A Minimum Χ2 Approach With Emphasis On A Social Interactions Model," Econometric Theory, Cambridge University Press, vol. 26(01), pages 260-299, February.
  • Handle: RePEc:cup:etheor:v:26:y:2010:i:01:p:260-299_09
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    Cited by:

    1. Antoine, Bertille & Renault, Eric, 2012. "Efficient minimum distance estimation with multiple rates of convergence," Journal of Econometrics, Elsevier, vol. 170(2), pages 350-367.
    2. repec:bla:jorssc:v:66:y:2017:i:2:p:295-311 is not listed on IDEAS
    3. Galvao, Antonio F. & Wang, Liang, 2015. "Efficient minimum distance estimator for quantile regression fixed effects panel data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 1-26.

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