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Instrumental Variables Estimation without Outside Instruments

Author

Listed:
  • Kien C. Tran

    (University of Lethbridge)

  • Mike G. Tsionas

    (Lancaster University Management School
    Montpelier Business School)

Abstract

This paper considers an alternative estimation approach of regression models with endogenous regressors when external instruments are not available. An artificial neural network is used to model the correlation between error and regressors coupled with Bayesian exponentially tilted empirical likelihood to obtain a consistent estimation of the model’s parameters. Monte Carlo simulations indicate that the new approach performs well in finite samples. An empirical application is presented to illustrate the usefulness of our proposed approach.

Suggested Citation

  • Kien C. Tran & Mike G. Tsionas, 2022. "Instrumental Variables Estimation without Outside Instruments," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(3), pages 489-506, September.
    • Handle: RePEc:spr:jqecon:v:20:y:2022:i:3:d:10.1007_s40953-022-00300-3
    DOI: 10.1007/s40953-022-00300-3
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    References listed on IDEAS

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    More about this item

    Keywords

    Endogeneity; Instruments; Artificial neural networks; Empirical likelihood; Markov chain Monte Carlo; Bayesian inference;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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