TY - UNPB
T1 - Comparing parametric and nonparametric regression methods for panel data
T2 - the optimal size of Polish crop farms
AU - Czekaj, Tomasz Gerard
AU - Henningsen, Arne
PY - 2012
Y1 - 2012
N2 - We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs. The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test rejects both the Cobb-Douglas and the Translog functional form, while a recently developed nonparametric kernel regression method with a fully nonparametric panel data specification delivers plausible results. On average, the nonparametric regression results are similar to results that are obtained from the parametric estimates, although many individual results differ considerably. Moreover, the results from the parametric estimations even lead to incorrect conclusions regarding the technology and the optimal firm size.
AB - We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs. The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test rejects both the Cobb-Douglas and the Translog functional form, while a recently developed nonparametric kernel regression method with a fully nonparametric panel data specification delivers plausible results. On average, the nonparametric regression results are similar to results that are obtained from the parametric estimates, although many individual results differ considerably. Moreover, the results from the parametric estimations even lead to incorrect conclusions regarding the technology and the optimal firm size.
M3 - Working paper
T3 - FOI Working Paper
SP - 1
EP - 30
BT - Comparing parametric and nonparametric regression methods for panel data
PB - Institute of Food and Resource Economics, University of Copenhagen
ER -