Park, Byeong and Simar, Leopold and Zelenyuk, Valentin (2006): Local likelihood estimation of truncated regression and its partial derivatives: theory and application. Published in: Journal of Econometrics , Vol. 1, No. 146 (2008): pp. 185-198.
Download (399kB) | Preview
In this paper we propose a very flexible estimator in the context of truncated regression that does not require parametric assumptions. To do this, we adapt the theory of local maximum likelihood estimation. We provide the asymptotic results and illustrate the performance of our estimator on simulated and real data sets. Our estimator performs as good as the fully parametric estimator when the assumptions for the latter hold, but as expected, much better when they do not (provided that the curse of dimensionality problem is not the issue). Overall, our estimator exhibits a fair degree of robustness to various deviations from linearity in the regression equation and also to deviations from the specification of the error term. So the approach shall prove to be very useful in practical applications, where the parametric form of the regression or of the distribution is rarely known.
|Item Type:||MPRA Paper|
|Original Title:||Local likelihood estimation of truncated regression and its partial derivatives: theory and application|
|Keywords:||Nonparametric Truncated Regression, Local Likelihood|
|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 > C2 - Single Equation Models ; Single Variables > C24 - Truncated and Censored Models ; Switching Regression Models ; Threshold Regression Models
|Depositing User:||Valentin Zelenyuk|
|Date Deposited:||14 Nov 2011 01:39|
|Last Modified:||03 Mar 2017 00:28|
Chambers, R.G., Y. Chung, R. F¨are (1996), Benefit and distance function, Journal of Econonomic Theory, 70, 407–419.
Debreu, G. (1951), The coefficient of resource utilization, Econometrica, 19, 273–292.
Carroll, R. J., Fan, J., Gijbels, I. and Wand, M. P. (1997), Generalized partially linear single-index models, Journal of the American Statistical Association, 92, 477–489
Fan, J. and Gijbels, I. (1996), Local Polynomial Modelling and Its Applications, Chapman and Hall, London.
Fan, J., Heckman, N. E and Wand, M. P. (1995), Local polynomial kernel regression for generalized linear models and quasi-likelihood functions, Journal of the American Statistical Association, 90, 141–150.
Farrell, M.J. (1957), The Measurement of Productive Efficiency, Journal of Royal Statistical Society, Series A, 120:3, 253–281.
Kumar, S. and R.R. Russell (2002), Technological Change, Technological Catch-up, and Capital Deepening: Relative Contributions to Growth and Convergence, American Economic Review, 92, 3, 527–548.
Kumbhakar, S. C., Park, B. U., Simar, L. and Tsionas, E. G. (2006), Nonparametric stochastic frontiers: a local maximum likelihood approach, Journal of Econometrics, forthcoming.
Lewbel, A. and O. Linton, (2002), Nonparametric censored and truncated regression, Econometrica, 70, 765–779.
Luenberger, D.G. (1994), Optimality and the theory of value,Journal of Econonomic Theory, 63, 147–169.
Simar L. and P. Wilson (2006), Estimation and inference in two-stage, semi-parametric models of production processes, Discussion Paper (0307) of Institute of Statistics, University Catholique de Louvain, Journal of Econometrics, forthcoming.
Tibshirani, R. and Hastie, T. J. (1987), Local likelihood estimation, Journal of the American Statistical Association, 82, 559–567.
Zelenyuk, V. and V. Zheka (2006), Corporate governance and firm’s efficiency: the case of a transitional country, Ukraine, Journal of Productivity Analysis, 25, 143–168.