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The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics

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  • Hu, Yingyao

Abstract

This paper reviews the recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to an observed distribution. The identification and estimation of measurement error models focus on how to obtain the latent distribution and the measurement error distribution from the observed distribution. Such a framework is suitable for many microeconomic models with latent variables, such as models with unobserved heterogeneity or unobserved state variables and panel data models with fixed effects. Recent developments in measurement error models allow very flexible specification of the latent distribution and the measurement error distribution. These developments greatly broaden economic applications of measurement error models. This paper provides an accessible introduction of these technical results to empirical researchers so as to expand applications of measurement error models.

Suggested Citation

  • Hu, Yingyao, 2017. "The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics," Journal of Econometrics, Elsevier, vol. 200(2), pages 154-168.
    • Handle: RePEc:eee:econom:v:200:y:2017:i:2:p:154-168
    DOI: 10.1016/j.jeconom.2017.06.002
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    3. Shibata, Ippei, 2022. "Reassessing classification errors in the analysis of labor market dynamics," Labour Economics, Elsevier, vol. 78(C).
    4. Ekaterina Oparina & Sorawoot Srisuma, 2022. "Analyzing Subjective Well-Being Data with Misclassification," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 730-743, April.
    5. Chen, Linkun & Clarke, Philip M. & Petrie, Dennis J. & Staub, Kevin E., 2021. "The effects of self-assessed health: Dealing with and understanding misclassification bias," Journal of Health Economics, Elsevier, vol. 78(C).
    6. Daisuke Kurisu & Taisuke Otsu, 2019. "On the uniform convergence of deconvolution estimators from repeated measurements," STICERD - Econometrics Paper Series 604, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    7. Luo, Yao, 2020. "Unobserved heterogeneity in auctions under restricted stochastic dominance," Journal of Econometrics, Elsevier, vol. 216(2), pages 354-374.
    8. Hao Dong & Taisuke Otsu & Luke Taylor, 2022. "Nonparametric estimation of additive models with errors-in-variables," Econometric Reviews, Taylor & Francis Journals, vol. 41(10), pages 1164-1204, November.
    9. Yingyao Hu & Yang Liu & Jiaxiong Yao, 2022. "Revealing Unobservables by Deep Learning: Generative Element Extraction Networks (GEEN)," Papers 2210.01300, arXiv.org.
    10. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On the uniform convergence of deconvolution estimators from repeated measurements," LSE Research Online Documents on Economics 107533, London School of Economics and Political Science, LSE Library.
    11. Hahn, Jinyong & Ridder, Geert, 2017. "Instrumental variable estimation of nonlinear models with nonclassical measurement error using control variables," Journal of Econometrics, Elsevier, vol. 200(2), pages 238-250.
    12. Yong Cai, 2022. "Linear Regression with Centrality Measures," Papers 2210.10024, arXiv.org.
    13. Kenichi Nagasawa, 2018. "Treatment Effect Estimation with Noisy Conditioning Variables," Papers 1811.00667, arXiv.org, revised Sep 2022.
    14. Brantly Callaway & Tong Li & Irina Murtazashvili, 2021. "Nonlinear Approaches to Intergenerational Income Mobility allowing for Measurement Error," Papers 2107.09235, arXiv.org, revised Dec 2021.
    15. Vitor Possebom, 2021. "Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification," Papers 2106.00536, arXiv.org, revised Jul 2023.
    16. An, Yonghong & Hong, Shengjie & Zhang, Daiqiang, 2023. "A structural analysis of simple contracts," Journal of Econometrics, Elsevier, vol. 236(2).
    17. Erhao Xie, 2018. "Inference in Games Without Nash Equilibrium: An Application to Restaurants, Competition in Opening Hours," Staff Working Papers 18-60, Bank of Canada.
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    20. Luo, Yao & Xiao, Ruli, 2023. "Identification of auction models using order statistics," Journal of Econometrics, Elsevier, vol. 236(1).

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    More about this item

    Keywords

    Measurement error model; Errors-in-variables; Latent variable; Unobserved heterogeneity; Unobserved state variable; Mixture model; Hidden Markov model; Dynamic discrete choice; Nonparametric identification; Conditional independence; Endogeneity; Instrument; Type; Unemployment rates; IPV auction; Multiple equilibria; Incomplete information game; Belief; Learning model; Fixed effects; Panel data model; Cognitive and non-cognitive skills; Matching; Income dynamics;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • D20 - Microeconomics - - Production and Organizations - - - General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • I20 - Health, Education, and Welfare - - Education - - - General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J60 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - General
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General

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