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The Combined Estimator Approach to Model Transferability and Updating

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  • Ben-Akiva, Moshe
  • Bulduc, Denis
  • Pene, Kalulumia

Abstract

The idea of transferability is to employ in model estimation, fitted model parameters computed from a different data set. The combined estimator approach to the transferability problem is expressed as a linear combination of the unbiased direct estimators on the two data sets. The major gain is in variance reduction. The combined estimator is shown to have superior accuracy, in a Mean Square Error sense, to a unbiased direct estimator whenever the transfer bias is relatively small. A test that indicates if the combined estimator is superior to the direct estimator is provided. Variances of the direct estimators are assumed to be known. Monte Carlo experiments are performed to assess the quality of the approximations. The results show that the approximations used are highly conservative. An empirical example of the combined estimator applied to a discrete choice problem is presented.

Suggested Citation

  • Ben-Akiva, Moshe & Bulduc, Denis & Pene, Kalulumia, 1995. "The Combined Estimator Approach to Model Transferability and Updating," Empirical Economics, Springer, vol. 20(1), pages 167-175.
    • Handle: RePEc:spr:empeco:v:20:y:1995:i:1:p:167-75
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    Cited by:

    1. Franco Chingcuanco & Eric Miller, 2014. "A meta-model of vehicle ownership choice parameters," Transportation, Springer, vol. 41(5), pages 923-945, September.

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