Semiparametric Smooth Coefficient Stochastic Frontier Model With Panel Data
Bibliographic Data
| ID | 19418682 |
|---|---|
| Authors | Yao Feng (0000-0001-9874-1031, West Virginia University), Feng Yao (0000-0002-9635-9581, China Center for Special Economic Zone Research, Shenzhen University, Guangdong Sheng 518060, China, and Department of Economics, West Virginia University, Morgantown, WV 26505 ()), Fan Zhang (0000-0002-3643-018X, Ripon College), Subal C Kumbhakar (0000-0002-1366-379X, Binghamton University) |
| Year | 2019 |
| Volume | 37 |
| Issue | 3 |
| Pages | 556-572 |
| Publication date | 2019-07-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2017.1390467 |
| OpenAlex | W2761014404 |
| Language | EN |
| Citations received | 4 |
| References cited | 36 |
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. Supplementary materials for this article are available online
Econometrics · Estimator · Monte Carlo method · Panel data · Parametric model · Parametric statistics · Semiparametric model · Semiparametric regression · Statistics · Economic Growth and Productivity · Efficiency Analysis Using DEA · Mathematics · Spatial and Panel Data Analysis
Stochastic Frontier Analysis
Frontier production functions, technical efficiency and panel data
One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels
Production frontiers with cross-sectional and time-series variation in efficiency levels
On the estimation of technical inefficiency in the stochastic frontier production function model
A model for technical inefficiency effects in a stochastic frontier production function for panel data
Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error
Formulation and estimation of stochastic frontier production function models
Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity
Semiparametric Smooth Coefficient Models
Estimation of a Doubly Heteroscedastic Stochastic Frontier Cost Function
Semiparametric Estimation of Stochastic Production Frontier Models
A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms
Production Frontiers and Panel Data
Computing Productivity
The measurement and sources of technical inefficiency in the Indonesian weaving industry
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,8 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |