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Estimation of a varying coefficient, fixed-effects Cobb–Douglas production function in levels

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  • Wang, Taining
  • Henderson, Daniel J.

Abstract

We propose a semiparametric varying coefficient estimator for a Cobb–Douglas production function for panel data with several practical features. First, we estimate the model without a log transformation to avoid induced non-negligible estimation bias. Second, we disentangle the impact of traditional inputs from that of environment variables, which impact output indirectly through altering the output elasticity of inputs and the state of technology via unknown functions. We introduce a linear index structure in the unknown functions to circumvent the curse of dimensionality, and allow the output elasticity of different inputs to depend on different environment variables. Third, our technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs and/or environment variables. Our estimator combines series and kernel methods for both the unknown parameters and functions. We demonstrate that the proposed estimator exhibits promising finite-sample performance.

Suggested Citation

  • Wang, Taining & Henderson, Daniel J., 2022. "Estimation of a varying coefficient, fixed-effects Cobb–Douglas production function in levels," Economics Letters, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:ecolet:v:213:y:2022:i:c:s0165176522000465
    DOI: 10.1016/j.econlet.2022.110354
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    References listed on IDEAS

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    More about this item

    Keywords

    Panel data; Profile nonlinear least-squares; Semiparametric regression; Smooth coefficient;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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