The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression function. However, the a priori specification of a functional form involves the risk of choosing one that is not similar to the “true” but unknown relationship between the regressors and the dependent variable. This problem, known as parametric misspecification, can result in biased parameter estimates and hence, also in biased measures, which are derived from the estimated parameters. This, in turn, can result in incorrect economic conclusions and recommendations for managers, politicians and decision makers in general.
This PhD thesis focuses on a nonparametric econometric approach that can be used to avoid this problem. The main objective is to investigate the applicability of the nonparametric kernel regression method in applied production analysis. The focus of the empirical analyses included in this thesis is the agricultural sector in Poland. Data on Polish farms are used to investigate practically and politically relevant problems and to illustrate how nonparametric regression methods can be used in applied microeconomic production analysis both in panel data and cross-section data settings.
The thesis consists of four papers. The first paper addresses problems of parametric and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis.
The results provided in this PhD thesis show that nonparametric kernel methods are well-suited to econometric production analysis and can outperform traditional parametric methods. Although the empirical focus of this thesis is on the application of nonparametric kernel regression in applied production analysis, the findings are also applicable to econometric estimations in general.
Associate professor Arne Henningsen, Department of Food and Resource Economics, University of Copenhagen.
Senior associate professor Svend Rasmussen, Department of Food and Resource Economics, University of Copenhagen.
- Professor Mette Asmild, Department of Food and Resource Economics, University of Copenhagen (chairman);
- Professor Timo Kuosmanen, Aalto University, School of Business, Finland;
- Professor Alfons Oude Lansink, Wageningen University, Business Group, The Netherlands.