Panel Data Specifications in Nonparametric Kernel Regression: An Application to Production Functions - Tomasz Czekaj; Arne Henningsen (University of Copenhagen, Department of Food and Resource Economics (IFRO), 2013)
We 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.
Keywords: C14; C23; D24; Q12; 330; nonparametric kernel regression; panel data; choice of the kernel; kernels for categorical variables; production function