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Journal of Quantitative Economics

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15.04.2022 | Original Article

Instrumental Variables Estimation without Outside Instruments

Erschienen in: Journal of Quantitative Economics | Ausgabe 3/2022

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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.

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It is well known that even with a very small correlation with the error term, IV methods can perform worse than just using OLS method.

The term is used widely in the ANN literature and denotes the nonlinear functions that involve different linear combinations of the elements of \(\varvec{x}_{t}\) in (5). Another popular activation function is \(\varphi (z)=\mathrm {tanh}(z)\). The results of the paper have been found to be robust to the selection of different activation functions.

For validation, we have used a Metropolis–Hastings random walk chain. The effective sample size of the Metropolis–Hastings algorithm was typically two orders of magnitude higher in all applications and did not converge within less than 50,000 iterations.

For more details on \(\texttt {AdMit}\), see https://cran.r-project.org/web/packages/AdMit/AdMit.pdf.

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Titel: Instrumental Variables Estimation without Outside Instruments
Publikationsdatum: 15.04.2022
Erschienen in: Journal of Quantitative Economics / Ausgabe 3/2022
Print ISSN: 0971-1554
Elektronische ISSN: 2364-1045
DOI: https://doi.org/10.1007/s40953-022-00300-3

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