Generalizations of the Censored and Truncated Least Absolute Deviations Estimators
Author | : Stanford University. Institute for Mathematical Studies in the Social Sciences |
Publisher | : |
Total Pages | : 38 |
Release | : 1983 |
Genre | : |
ISBN | : |
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Author | : Stanford University. Institute for Mathematical Studies in the Social Sciences |
Publisher | : |
Total Pages | : 38 |
Release | : 1983 |
Genre | : |
ISBN | : |
Author | : Kazumitsu Nawata |
Publisher | : |
Total Pages | : 36 |
Release | : 1990 |
Genre | : Estimation theory |
ISBN | : |
Author | : James Leo Powell |
Publisher | : |
Total Pages | : 262 |
Release | : 1981 |
Genre | : Econometrics |
ISBN | : |
Author | : Takeshi Amemiya |
Publisher | : Harvard University Press |
Total Pages | : 540 |
Release | : 1985 |
Genre | : Business & Economics |
ISBN | : 9780674005600 |
The main features of this text are a thorough treatment of cross-section models—including qualitative response models, censored and truncated regression models, and Markov and duration models—and a rigorous presentation of large sample theory, classical least-squares and generalized least-squares theory, and nonlinear simultaneous equation models.
Author | : Society for Industrial and Applied Mathematics |
Publisher | : |
Total Pages | : 736 |
Release | : 1986 |
Genre | : Mathematical analysis |
ISBN | : |
Author | : Cheng Hsiao |
Publisher | : Cambridge University Press |
Total Pages | : 563 |
Release | : 2014-12-08 |
Genre | : Business & Economics |
ISBN | : 1107038693 |
This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling cross-sectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined. Other new material includes correlated random coefficient models, pseudo-panels, duration and count data models, quantile analysis, and alternative approaches for controlling the impact of unobserved heterogeneity in nonlinear panel data models.
Author | : Yannis Bilias |
Publisher | : |
Total Pages | : 47 |
Release | : 2018 |
Genre | : |
ISBN | : |
We show that exact computation of the censored least absolute deviations (CLAD) estimator proposed by Powell (1984) may be achieved by formulating the estimator as a linear Mixed Integer Programming (MIP) problem with disjunctive constraints. We apply our approach to three previously studied datasets and find that widely used approximate optimization algorithms can lead to erroneous conclusions. Extensive simulations confirm that MIP- based computation using available solvers is effective for datasets typically encountered in econometric applications and that, despite the proliferation of competitors, CLAD remains a useful estimator.