Technical change: It should be positive and make sense!
We present the results of eight models that differ with respect to the time behavior of technical inefficiency and the presence of country heterogeneity. When taken into account, heterogeneity raises average technical change estimates, however technical progress rankings become counter-intuitive.
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- Willam Greene, 2005.
"Fixed and Random Effects in Stochastic Frontier Models,"
Journal of Productivity Analysis,
Springer, vol. 23(1), pages 7-32, 01.
- William Greene, 2002. "Fixed and Random Effects in Stochastic Frontier Models," Working Papers 02-16, New York University, Leonard N. Stern School of Business, Department of Economics.
- Luis Orea, 2002. "Parametric Decomposition of a Generalized Malmquist Productivity Index," Journal of Productivity Analysis, Springer, vol. 18(1), pages 5-22, July.
- Kneller, Richard & Andrew Stevens, Philip, 2003. "The specification of the aggregate production function in the presence of inefficiency," Economics Letters, Elsevier, vol. 81(2), pages 223-226, November.
- Kumbhakar, Subal C. & Wang, Hung-Jen, 2005. "Estimation of growth convergence using a stochastic production frontier approach," Economics Letters, Elsevier, vol. 88(3), pages 300-305, September.
- Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
- Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June. Full references (including those not matched with items on IDEAS)
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