Kuosmanen, Timo and Fosgerau, Mogens (2009): Neoclassical versus frontier production models? Testing for the skewness of regression residuals. Published in: Scandinavian Journal of Economics , Vol. 111, No. 2 : pp. 351-367.
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Abstract
The empirical literature on production and cost functions is divided into two strands: 1) the neoclassical approach that concentrates on model parameters, 2) the frontier approach that decomposes the disturbance term to a symmetric noise term and a positively skewed inefficiency term. We propose a theoretical justification for the skewness of the inefficiency term, arguing that this skewness is the key testable hypothesis of the frontier approach. We propose to test the regression residuals for skewness to distinguish the two competing approaches. Our test builds directly upon the asymmetry of regression residuals and does not require any prior distributional assumptions.
Item Type: | MPRA Paper |
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Original Title: | Neoclassical versus frontier production models? Testing for the skewness of regression residuals |
Language: | English |
Keywords: | Firms and production; Frontier estimation; Hypotheses testing; Production function; Productive efficiency analysis |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General A - General Economics and Teaching > A1 - General Economics > A10 - General D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity |
Item ID: | 24208 |
Depositing User: | Prof. Mogens Fosgerau |
Date Deposited: | 03 Aug 2010 07:36 |
Last Modified: | 28 Sep 2019 04:40 |
References: | Afriat, S. (1972), Efficiency Estimation of Production Functions, International Economic Review 13, 568-598. Ahmad, I.A. and Li Q. (1997), Testing the Symmetry of an Unknown Density Function by the Kernel Method, Journal of Nonparametric Statistics 7, 279–293. Bai, J. and Ng S. (2001), A Consistent Test for Conditional Symmetry in Time Series Models, Journal of Econometrics 103, 225–258. Christensen, L.R. and Greene W.H. (1976), Economies of Scale in U.S. Electric Power Generation, Journal of Political Economy 84, 655-676. D’Agostino, R.B. (1970), Transformation to Normality of the Null Distribution of g1, Biometrika 57, 679-681. D’Agostino, R.B. (1986), Tests for the Normal Distribution, Ch. 9 in R.B. D’Agostino and M.A. Stephens (eds.), Goodness-of-fit techniques, Statistics: textbooks and monographs 68, Marcel Dekker, New York. D’Agostino, R.B. and Pearson E. (1973), Tests for Departures from Normality: Empirical Results for the Distribution of and b2, Biometrika 60, 613-622. Debreu, G. (1951), The Coefficient of Resource Utilization, Econometrica 19, 273-292. Diewert, W.E. and Parkan C. (1983), Linear Programming Tests of Regularity Conditions for Production Frontiers, in W. Eichhorn et al. (eds.), Quantitative studies on production and prices, Physica-Verlag, Würzburg. Fan, Y., Li, Q. and Weersink A. (1996), Semiparametric Estimation of Stochastic Production Frontier Models, Journal of Business and Economic Statistics 14, 460-468. Farrell, M.J. (1957), The Measurement of Productive Efficiency, Journal of the Royal Statistical Society Series A. General 120, 253-282. Fried, H., Lovell C.A.K. and Schmidt S. (2008), The Measurement of Productive Efficiency and Productivity Change, Oxford University Press, New York. Goldstein, H. (2003), On the COLS and CGMM Moment Estimation Methods for Frontier Production Functions, Ch. 14 in B.P. Stigum (ed.), Econometrics and the Philosophy of Economics, Princeton University Press, Princeton. Godfrey, L.G. and Orme C.D. (1991), Testing for Skewness of Regression Disturbances, Economics Letters 37, 31-34. Greene, W.H. (2008a), The Econometric Approach to Efficiency Analysis, Ch. 2 in H. Fried et al. (eds.), The Measurement of Productive Efficiency and Productivity Change, Oxford University Press, New York. Greene, W.H. (2008b), Econometric Analysis, 6th Edition, Pearson Education, New Jersey. Groeneboom, P., Jongbloed G. and Wellner J.A. (2001), Estimation of a Convex Function: Characterizations and Asymptotic Theory, Annals of Statistics 29, 1653-1698. Hanoch G. and Rothschild M. (1972), Testing Assumptions of Production Theory: A Nonparametric Approach, Journal of Political Economy 80, 256-275. Hanson, D.L. and Pledger G. (1976), Consistency in Concave Regression, Annals of Statistics 4, 1038-1050. Hildreth, C. (1954), Point Estimates of Ordinates of Concave Functions, Journal of the American Statistical Association 49, 598-619. Hyndman, R.J. and Yao Q. (2002), Nonparametric Estimation and Symmetry Tests for Conditional Density Functions, Journal of Nonparametric Statistics 14, 259-278. Jarque, C.M. and Bera A.K. (1980), Efficient Tests for Normality, Heteroskedasticity, and Serial Independence of Regression Residuals, Economics Letters 6, 255–259. Jondrow, J. Lovell, C.A.K., Materov, I.S. and Schmidt P. (1982) On Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model, Journal of Econometrics 19, 233-238. Koopmans, T.C. (1951), An Analysis of Production as an Efficient Combination of Activities, in, T.C. Koopmans (ed.), Activity Analysis of Production and Allocation, Cowles Commision for Research in Economics, monograph no. 13, John Wiley & Sons Inc., New York. Kumbhakar, S.C. and Lovell C.A.K. (2000), Stochastic Frontier Analysis, Cambridge University Press, Cambridge. Kuosmanen, T. (2006), Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework, MTT Discussion Paper 3/2006. Kuosmanen, T. (2008), Representation Theorem for Convex Nonparametric Least Squares, Econometrics Journal 11, 308-325. Kuosmanen T. and Kortelainen M. (2007), Stochastic Nonparametric Envelopment of Data: Cross-Sectional Frontier Estimation Subject to Shape Constraints, University of Joensuu, Economics Discussion Paper 46. Kuosmanen, T., Post, G.T. and Scholtes S. (2007), Nonparametric Tests of Productive Efficiency with Errors-in-Variables, Journal of Econometrics 136, 131-162. Nerlove, M. (1963), Returns to Scale in Electricity Supply, Ch. 7 in C. Christ et al. (eds.), Measurement in Economics, Stanford University Press, Stanford. Ondrich, J. and Ruggiero J. (2001), Efficiency Measurement in the Stochastic Frontier Model, European Journal of Operational Research 129, 434-442. Pearson, E. and Hartley H. (1966), Tables for statisticians, 3rd ed. Biometrika Vol. I, Cambridge University Press, Cambridge. Pérez-Alonso, A. (2007), A Bootstrap Approach to Test the Conditional Symmetry in Time Series Models, Computational Statistics and Data Analysis 51, 3484–3504. Poitras, G. (2006), More on the Correct Use of Omnibus Tests for Normality, Economics Letters 90, 304-309. Shapiro, S.S. and Wilk M.B. (1965), An Analysis of Variance Test for Normality (Complete Samples), Biometrika 52, 591-611. Shapiro, S.S., Wilk, M.B. and Chen H.J. (1968), A Comparative Study of Various Tests for Normality, Journal of American Statistical Association 63, 1343-78. Throde, H. (2002), Testing for Normality, Marcel Dekker, New York. Varian, H.R. (1984), The Nonparametric Approach to Production Analysis, Econometrica 52, 579-598. Varian, H. (1985), Nonparametric Analysis of Optimizing Behavior with Measurement Error, Journal of Econometrics 30, 445-458. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/24208 |