10.6084/m9.figshare.5496955.v1
Feng Yao
Feng
Yao
Fan Zhang
Fan
Zhang
Subal C. Kumbhakar
Subal C.
Kumbhakar
Semiparametric smooth coefficient stochastic frontier model with panel data
Taylor & Francis Group
2017
Semiparametric smooth coefficient model
Technical efficiency
Pseudo maximum likelihood estimation
Computer productivity
2017-10-12 20:47:18
Journal contribution
https://tandf.figshare.com/articles/journal_contribution/Semiparametric_smooth_coefficient_stochastic_frontier_model_with_panel_data/5496955
<p>We investigate the semiparametric smooth coefficient stochastic frontier model for panel data in which the distribution of the composite error term is assumed to be of known form but depends on some environmental variables. We propose multi-step estimators for the smooth coefficient functions as well as the parameters of the distribution of the composite error term and obtain their asymptotic properties. The Monte Carlo study demonstrates that the proposed estimators perform well in finite samples. We also consider an application and perform model specification test, construct confidence intervals, and estimate efficiency scores that depend on some environmental variables. The application uses a panel data on 451 large U.S. firms to explore the effects of computerization on productivity. Results show that two popular parametric models used in the stochastic frontier literature are likely to be misspecified. Compared with the parametric estimates, our semiparametric model shows a positive and larger overall effect of computer capital on the productivity. The efficiency levels, however, were not much different among the models.</p>