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| Title: | 考慮內生性與樣本選擇之生產邊界估計方法—關聯結構法與共同邊界法之應用
An estimation of production frontiers taking account of endogeneity and selection under the framework of copula methods and metafrontier models |
| Authors: | 謝子雄
Xie, Zixiong |
| Contributors: | 黃台心
Huang, Tai Hsin
謝子雄
Xie, Zixiong |
| Keywords: | 生產函數
內生性
樣本選擇
生產力
技術效率
共同邊界
production function
endogeneity
selectivity
productivity
technical efficiency
metafrontier |
| Date: | 2012 |
| Issue Date: | 2013-09-02 16:03:54 (UTC+8) |
| Abstract: | 本論文嘗試解決在文獻上估計生產函數時所產生內生性及樣本選擇的問題。在模型設定上，我允許生產函數存在未觀察到的生產力，並引入技術無效率。在隨機邊際模型架構下，我利用 Olley and Pakes (1996) 及 Levinsohn and Petrin (2003) 所提之演算法先行解決內生性的問題。之後再利用關聯結構法 (copula method) 將樣本選擇問題考慮至生產函數中。如此，既可解決生產函數時所產生內生性及樣本選擇的問題，又可在此基礎上估計技術效率值。另外，根據本文所提之估計方法基礎下，我們透過共同邊界分析法 (metafrontier analysis) 比較留下 (stayer) 與離開 (exit) 市場廠商的技術效率與技術差距比率 (technology gap ratio, TGR)。
Plants in Taiwan’s manufacturing are characterized as small- and medium-size with frequent exit and entry and the scale of survivors varies considerably with business cycles. Plants` choices on whether to exit or to stay and continuing plants` options on input quantities count on both technical efficiency and productivity. This entails a selection and a simultaneity problems in the estimation of production frontiers.
This dissertation proposes a new approach to solve both issues under the framework of the stochastic frontier approach. More specific, we extend Olley and Pakes` (1996) and Levinsohn and Petrin`s (2003) approaches to a stochastic production frontier and use copula methods to deal with simultaneity and selection at the same time. Based on the proposed method, we further conduct a metafrontier analysis to compare the technical efficiency and technology gap ratio between exit and continuing firms, which are operating under different technologies and subject to simultaneity and selection. The data of Taiwan’s electronic and food products industries are arbitrarily chosen to illustrate our empirics. Some results are obtained in this dissertation: first, the proposed model solves the problems of simultaneity and selectivity in the production function that exists in ordinary least square estimation; second, there is a serious downward bias in technical efficiency when the conventional stochastic frontier approach ignores simultaneity or sample selection problem; third, the results of metafrontier analysis find that, there is little difference in technology gap ratio between exit and continuing firms. The primary determinant on whether a firm can keep operating in the industry is its managerial ability, rather than its adoption of technology. |
| Reference: | Aigner, D. J., C. A. K. Lovell, and P. Schmidt (1977) “Formulation and Estimation of Stochastic Frontier Production Function Models,” Journal of Econometrics, 6, 21-37.
Arellano, M. and S. Bond. (1991) “Some Tests of Specification for Panel Data: Monte Carlo Evidence and An Application to Employment Equations,” Review of Economic Studies, 58, 277-297.
Battese, G. E., D. S. P. Rao, and C. J. O’Donnell (2004) “Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies,” Journal of Productivity Analysis, 21, 91-103.
Blundell, R. and S. Bond (1998) “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, 87(1), 115-143.
Cherubini, U., E. Luciano and W. Vecchiato (2004) Copula methods in finance. John Wiley & Sons, Hoboken, NJ
Dunne, T. and M. Roberts (1991) Variation in Producer Turnover across US Manufacturing, in Entry and Market Contestability: An international comparison, (Eds) P. Geroski and J. Schwalbach, Blackwell, London.
Fan, Y., Q. Li, and A. Weersink (1996) “Semiparametric Estimation of Stochastic Production Frontier Models,” Journal of Business and Economic Statistics, 14, 460-468.
Feinberg, R. (2013) “Internation Competition and Small-firm Exit in US Manufacturing,” Eastern Economic Journal, 39, 402-414.
Fotopoulos, G. and N. Spence (1998) “Entry and Exit from Manufacturing Industries: Symmetry, Turbulence and Simultaneity: Some Empirical Evidence from Greek Manufacturing Industries,” Applied Economics, 30(2), 245–62.
Greene, W. (2010) “A Stochastic Frontier Model with Correction for Sample Selection,” Journal of Productivity Analysis, 34, 15-24.
Griliches, Z. (1957) “Specification Bias in Estimates of Production,” Journal of Farm Economics, 39, 8-20.
Harrison, A. E. (1994) “Productivity, Imperfect Competition and Trade Reform: Theory and Evidence,” Journal of International Economics, 36, 53-73.
Heckman J. (1979) “Sample Selection Bias as A Specification Error,” Econometrica, 47, 153–161.
Hoch, I. (1962) “Estimation of Production Parameters Combining Time-series and Cross-section Data,” Econometrica, 30(1), 34-53.
Huang, C. J., T.-H. Huang, and N.-H Liu (2012) “A New Approach to Estimating the Metafrontier Production Function Based on a Stochastic Frontier Framework,” Working Paper.
Jondrow, J, K. Lovell, I Materov, and P. Schmidt (1982) “On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model,” Journal of Econometrics, 19, 233-238.
Lai, H.-P., S. Polachek, and H.-J. Wang (2009) “Estimation of a Stochastic Frontier Model with a Sample Selection Problem. Working Paper, Department of Economics, National Chung Cheng University, Taiwan.
Levinsohn, J. and A. Petrin (2000) “When Industries Become More Productive, Do Firms? Investigating Productivity Dynamics,” NBER Working Paper 6893.
Levinsohn, J. and A. Petrin (2003) “Estimating Production Functions Using Inputs to Control for Unobservables,” Review of Economic Studies, 70(2), 341–372.
Marschak, J. and W. H. Andrews (1944) “Random Simultaneous Equations and the Theory of Production,” Econornetrica, 12, 143-205.
Meeusen, W. J. and van der Broeck (1977) “Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error,” International Economic Review, 18, 435-444.
Mundlak, Y. (1961) “Empirical Production Function Free of Management Bias,” Journal of Farm Economics, 43(1), 55-46.
O’Donnell, C. J., D.S.P. Rao, and G. E. Battese (2008) “Metafrontier Frameworks for the Study of Firm-Level Efficiencies and Technology Ratios,” Empirical Economics, 34, 231-255.
Olley, S. and A. Pakes (1996) “The Dynamics of Productivity in the Telecommunications Equipment Industry,” Econometrica, 64 (6), 1263–1298.
Ritter, C. and L. Simar (1997) “Pitfalls of Normal-Gamma Stochastic Frontier Models,” Journal of Productivity Analysis, 8 (2), 167-182.
Robinson, P. M. (1988) “Root-N Consistent Semiparametric Regression,” Econometrica, 55, 931-951.
Roncalli, T. (2002) Gestiondes Risques Multiples. Cours ENSAI de 3 e année. Groupe de Recherche Opérationelle, Cr´edit Lyonnais, working paper.
Tsay, W. J., C. J. Hung, T. T. Fu and I. L. Ho (2013) “A Simple Closed-Form Approximation for the Cumulative Distribution Function of the Composite Error of Stochastic Frontier Models.” Journal of Productivity Analysis, 39(3), 259-269.
Tsionas, E. and T. A. Papadogonas (2006) “Firm exit and Technical Inefficiency,” Empirical Economics, 31, 535-548.
White, H. (1982) “Maximum likelihood estimation of misspecifed models,” Econometrica, 50, 1-25. |
| Description: | 博士
國立政治大學
金融研究所
95352507
101 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0095352507 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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