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

Taining Wang () and Daniel Henderson ()

Economics Letters, 2022, vol. 213, issue C

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.

Keywords: Panel data; Profile nonlinear least-squares; Semiparametric regression; Smooth coefficient (search for similar items in EconPapers)
JEL-codes: C14 C23 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:213:y:2022:i:c:s0165176522000465

DOI: 10.1016/j.econlet.2022.110354

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