Hsu, Chih-Chiang and Lin, Chang-Ching and Yin, Shou-Yung (2015): Estimation of a Panel Stochastic Frontier Model with Unobserved Common Shocks.
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Abstract
This paper proposes a panel stochastic frontier model with unobserved common shocks to control cross-sectional dependence among individual firms. The novel feature is that we separate technical inefficiency (decision-dependent heterogeneity) from the effects induced by individual heterogeneity (decision-independent) caused by unobserved common shocks. We propose a feasible maximum likelihood method that does not require estimating the effects of unobserved common shocks and discuss its asymptotic properties. Monte Carlo simulations show that the proposed method has satisfactory finite sample properties when cross-sectional dependence exists. Application is illustrated by comparison of the efficiency of savings and commercial banking industries in the US.
Item Type: | MPRA Paper |
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Original Title: | Estimation of a Panel Stochastic Frontier Model with Unobserved Common Shocks |
Language: | English |
Keywords: | fixed effects, common correlated effects, factor structure, cross-sectional dependence, stochastic frontier |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models |
Item ID: | 65051 |
Depositing User: | Shou Yung Yin |
Date Deposited: | 15 Jun 2015 13:22 |
Last Modified: | 29 Sep 2019 06:21 |
References: | Ackerberg, D. A., K. Caves, and G. Frazer, 2006. Structural identification of production functions, mimeo, UCLA Department of Economics. Ahn, S. G., Y. H. Lee, and P. Schmidt, 2001. GMM estimation of linear panel data models with time-varying individual effects. Journal of Econometrics, 101, 219–255. Ahn, S. G., Y. H. Lee, and P. Schmidt, 2007. Stochastic frontier models with multiple time-varying individual effects, Journal of Productivity Analysis, 27, 1–12. Andrews, D. W. K. 2005. Cross-section regression with common shocks, Econometrica, 73, 1551–1585. Bai, J., 2009. Panel data models with interactive fixed effects, Econometrica, 77, 1229– 1279. Beck, T. , O. D. Jonghe and G. Schepens, 2013. Bank competition and stability: Crosscountry heterogeneity, Journal of Financial Intermediation, 22, 218–244. Berger, A. N. and L. J. Mester, 1997. Inside the black box: What explains differences in the efficiencies of financial institutions? Journal of Banking and Finance, 21, 895–947. Berger, A. N., I. Hasan and M. Zhou, 2009a. Bank ownership and efficiency in China: What will happen in the world’s largest nation? Journal of Banking and Finance, 33, 113–130. Berger, A. N., L. F. Klapper and R. Turk-Ariss, 2009b. Bank Competition and Financial Stability, Journal of Financial Services Research, 35, 99–118. Cornwell, C., P. Schmidt and R. Sickles, 1990. Production frontiers with cross-sectional and time-series variation in efficiency levels, Journal of Econometrics, 46, 185 200. Delis, M. D. and N. I. Papanikolaou, 2009. Determinants of bank efficiency: evidence from a semi-parametric methodology, Managerial Finance, 35, 260–275. Fama, E. F. and M. C. Jensen, 1983. Separation of Ownership and Control, Journal of Law and Economics, 26, 301–325. Filippini, M. and E. Tosetti, 2014. Stochastic Frontier Models for Long Panel Data Sets: Measurement of the Underlying Energy Efficiency for the OECD Countries. CER- ETHCenter of Economic Research at ETH Zurich Working Paper. Greene, W., 2003. Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization’s panel data on national health care systems. Working Paper 03–10, Department of Economics, Stern School of Business, New York University. Greene, W., 2005a. Fixed and random effects in stochastic frontier models, Journal of Productivity Analysis, 23, 7–32. Greene, W., 2005b. Reconsidering heterogeneity and inefficiency: Alternative estimators for stochastic frontier models, Journal of Econometrics, 126, 269–303. Han, C., L. Orea and P. Schmidt, 2005. Estimation of a panel data model with parametric temporal variation in individual effects, Journal of Econometrics, 126, 241–267. Jensen, M. C. andW. H. Meckling, 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure, Journal of Financial Economics, 3, 305–360. Jondrow, J., C. A. K. Lovell, I. S. Materov and P. Schmidt, 1984. On the estimation of technical inefficiency in the stochastic frontier production function model, Journal of Econometrics, 19, 233–238. Khatri, C. G., 1968. Some results for the singular normal multivariate regression models, Sankhya, 30, 267–280. Koopmans, T. C., 1951. Analysis of production as an efficient combination of activities. Activity analysis of production and allocation, 13, 33–37. Kristensen, D. and Y. Shin, 2012. Estimation of dynamic models with nonparametric simulated maximum likelihood, Journal of Econometrics, 167, 76-9-4. Lee, Y. H., 2006. A stochastic production frontier model with group-specific temporal variation in technical efficiency, European Journal of Operational Research, 174, 1616–1630. Lensink, R., A. Meesters and I. Naaborg, 2008. Bank efficiency and foreign ownership: Do good institutions matter? Journal of Banking and Finance, 32, 834–844. Levinsohn, J. and A. Petrin, 2003. Estimating production functions using inputs to control for unobservables, Review of Economic Studies, 70, 317–341. Mastromarco, C., L. Serlenga and Y. Shin, 2012. Is Globalization Driving Efficiency? A Threshold Stochastic Frontier Panel Data Modeling Approach. Review of Interna- tional Economics, 20, 563–579. Mastromarco, C., L. Serlenga and Y. Shin, 2013. Globalisation and technological convergence in the EU, Journal of Productivity Analysis, 40, 15–29. Mastromarco, C., L. Serlenga and Y. Shin, 2015. Modelling Technical Efficiency in Cross Sectionally Dependent Stochastic Frontier Panels. Journal of Applied Econometrics, doi: 10.1002/jae.2439. Olley, S. and A. Pakes, 1996. The dynamics of productivity in the telecommunications equipment industry, Econometrica, 64, 1263–1298. Pesaran, M. H., 2006. Estimation and inference in large heterogeneous panels with a multifactor error structure, Econometrica, 74, 967–1012. Sun, L. and T. P. Chang, 2010. A comprehensive analysis of the effects of risk measures on bank efficiency: Evidence from emerging Asian countries, Journal of Banking and Finance, 35, 1727–1735. Wang, H. J. and C. W. Ho, 2010. Estimating fixed-effect panel stochastic frontier models by model transformation, Journal of Econometrics, 157, 286–296. Wang, H. J. and P. Schmidt, 2002. One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels, Journal of Productivity Analysis, 18, 129–144. Wheelock, D. C. and P. W. Wilson, 2012. Do Large Banks Have Lower Costs? New Estimates of Returns to Scale for U.S. Banks, Journal of Money, Credit and Banking, 44, 171–199. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/65051 |
Available Versions of this Item
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Estimation of a panel stochastic frontier model with unobserved common shocks. (deposited 13 Mar 2012 12:55)
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Estimation of a panel stochastic frontier model with unobserved common shocks. (deposited 31 May 2014 18:12)
- Estimation of a Panel Stochastic Frontier Model with Unobserved Common Shocks. (deposited 15 Jun 2015 13:22) [Currently Displayed]
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Estimation of a panel stochastic frontier model with unobserved common shocks. (deposited 31 May 2014 18:12)