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Semiparametric nonlinear panel data models with measurement error

Author

Listed:
  • Oliver Linton

    (Institute for Fiscal Studies and University of Cambridge)

  • Ji-Liang Shiu

    (Institute for Fiscal Studies)

Abstract

This paper develops the identification and estimation of nonlinear semi-parametric panel data models with mismeasured variables and their corresponding average partial effects using only three periods of data. The past observables are used as instruments to control the measurement error problem, and the time averages of perfectly observed variables are used to restrict the unobserved individual-specific effect by a correlated random effects specification. The proposed approach relies on the Fourier transforms of several conditional expectations of observable variables. We then estimate the model via the semi-parametric sieve Generalized Method of Moments estimator. The finite-sample properties of the estimator are investigated through Monte Carlo simulations. We use our method to estimate the effect of the wage rate on labor supply using PSID.

Suggested Citation

  • Oliver Linton & Ji-Liang Shiu, 2018. "Semiparametric nonlinear panel data models with measurement error," CeMMAP working papers CWP09/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:09/18
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    References listed on IDEAS

    as
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    Keywords

    Correlated random effects; Measurement error; Nonlinear panel data models; Semi-parametric identification;
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