Instrumental Variables Estimation without Outside Instruments

Kien Tran and Mike G. Tsionas ()
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Mike G. Tsionas: Lancaster University Management School

Journal of Quantitative Economics, 2022, vol. 20, issue 3, No 1, 489-506

Abstract: 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.

Keywords: Endogeneity; Instruments; Artificial neural networks; Empirical likelihood; Markov chain Monte Carlo; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C11 C13 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s40953-022-00300-3

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