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

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  • De Silva, Dakshina G.
  • Hubbard, Timothy P.
  • Schiller, Anita R.
  • Tsionas, Mike G.

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.

Suggested Citation

  • De Silva, Dakshina G. & Hubbard, Timothy P. & Schiller, Anita R. & Tsionas, Mike G., 2023. "Estimating outcomes in the presence of endogeneity and measurement error with an application to R&D," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 278-294.
    • Handle: RePEc:eee:quaeco:v:88:y:2023:i:c:p:278-294
    DOI: 10.1016/j.qref.2023.01.010
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    More about this item

    Keywords

    Measurement error; Endogeneity; Empirical likelihood; Bayesian methods; Markov chain Monte Carlo; Research and development;
    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
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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