Estimating outcomes in the presence of endogeneity and measurement error with an application to R&D

Dakshina De Silva, Timothy Hubbard, Anita Schiller and Mike G. Tsionas

The Quarterly Review of Economics and Finance, 2023, vol. 88, issue C, 278-294

Abstract: We adopt a Bayesian econometric technique to address issues of endogeneity and measurement error when estimating outcomes while also tackling censoring. We motivate our study based on the theoretical framework laid out by Dasgupta and Stiglitz (1980) to highlight the endogeneity issue by investigating the relationship between market structure and innovation. We apply our method to estimate the R&D expenditures for Chinese manufacturing firms to highlight the importance of the econometric issues. Reduced-form results suggest a nonlinear relationship between market concentration and R&D expenditures, while our approach suggests a strictly positive relationship consistent with canonical theoretical models built on oligopolistic competition.

Keywords: Measurement error; Endogeneity; Empirical likelihood; Bayesian methods; Markov chain Monte Carlo; Research and development (search for similar items in EconPapers)
JEL-codes: C11 C13 O30 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:88:y:2023:i:c:p:278-294

DOI: 10.1016/j.qref.2023.01.010

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