Panel Data Specifications in Nonparametric Kernel Regression: An Application to Production Functions
AbstractWe discuss nonparametric regression models for panel data. A fully nonparametric panel data specification that uses the time variable and the individual identifier as additional (categorical) explanatory variables is considered to be the most suitable. We use this estimator and conventional parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we found the estimates of the fully nonparametric panel data model to be more reliable.
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Bibliographic InfoPaper provided by University of Copenhagen, Department of Food and Resource Economics in its series IFRO Working Paper with number 2013/5.
Length: 56 pages
Date of creation: Mar 2013
Date of revision:
nonparametric kernel regression; panel data; choice of the kernel; kernels for categorical variables; production function;
Find related papers by 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; Longitudinal Data; Spatial Time Series
- D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
- Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-05-05 (All new papers)
- NEP-DCM-2013-05-05 (Discrete Choice Models)
- NEP-EFF-2013-05-05 (Efficiency & Productivity)
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