| Shakeeb Khan, Professor
Please note: Shakeeb has left the "Economics" group at Duke University; some info here might not be up to date. Professor Khan is on leave at Boston College for the 2016-17 academic year.
Professor Khan specializes in the fields of mathematical economics, statistics, and applied econometrics. His studies have explored a variety of subjects from covariate dependent censoring and non-stationary panel data, to causal effects of education on wage inequality and the variables affecting infant mortality rates in Brazil. He was awarded funding by National Science Foundation grants for his projects entitled, “Estimation of Binary Choice and Nonparametric Censored Regression Models” and “Estimation of Cross-Sectional and Panel Data Duration Models with General Forms of Censoring.” He has published numerous papers in leading academic journals, including such writings as, “Heteroskedastic Transformation Models with Covariate Dependent Censoring” with E. Tamer and Y. Shin; “The Identification Power of Equilibrium in Simple Games;” “Partial Rank Estimation of Duration Models with General Forms of Censoring” with E. Tamer; and more. He is currently collaborating with D. Nekipelov and J.L. Powell on the project, “Optimal Point and Set Inference in Competing Risk Models;” with A. Lewbel on, “Identification and Estimation of Stochastic Frontier Models;” and with E. Tamer on, “Conditional Moment Inequalities in Roy Models with Cross-Section and Panel Data.”
- Contact Info:
- Education:
Ph.D. | Princeton University | 1997 |
M.S. | University of Toronto (Canada) | 1995 |
B.A. | McGill University (Canada) | 1992 |
- Specialties:
-
Mathematical and Quantitative Methods
Econometrics
- Research Interests:
Professor Khan specializes in the fields of mathematical economics, statistics, and applied econometrics. His studies have explored a variety of subjects from covariate dependent censoring and non-stationary panel data, to causal effects of education on wage inequality and the variables affecting infant mortality rates in Brazil. He was awarded funding by National Science Foundation grants for his projects entitled, “Estimation of Binary Choice and Nonparametric Censored Regression Models” and “Estimation of Cross-Sectional and Panel Data Duration Models with General Forms of Censoring.” He has published numerous papers in leading academic journals, including such writings as, “Heteroskedastic Transformation Models with Covariate Dependent Censoring” with E. Tamer and Y. Shin; “The Identification Power of Equilibrium in Simple Games;” “Partial Rank Estimation of Duration Models with General Forms of Censoring” with E. Tamer; and more. He is currently collaborating with D. Nekipelov and J.L. Powell on the project, “Optimal Point and Set Inference in Competing Risk Models;” with A. Lewbel on, “Identification and Estimation of Stochastic Frontier Models;” and with E. Tamer on, “Conditional Moment Inequalities in Roy Models with Cross-Section and Panel Data.”
- Keywords:
- Rank estimation • censored duration models • moment inequalities
- Curriculum Vitae Bio
- Recent Publications
(More Publications)
- Khan, S; Ponomareva, M; Tamer, E, Identification of panel data models with endogenous censoring,
Journal of Econometrics, vol. 194 no. 1
(September, 2016),
pp. 57-75, ISSN 0304-4076 [doi] [abs]
- Chen, S; Khan, S; Tang, X, Informational content of special regressors in heteroskedastic binary response models,
Journal of Econometrics, vol. 193 no. 1
(July, 2016),
pp. 162-182 [doi]
- Chen, SH; Khan, S, Semi-parametric estimation of program impacts on dispersion of potential wages,
Journal of Applied Econometrics, vol. 29 no. 6
(January, 2014),
pp. 901-919, ISSN 0883-7252 [doi] [abs]
- Khan, S; Nekipelov, D, On Uniform Inference in Nonlinear Models with Endogeneity no. 153
(September, 2013),
pp. 64 pages [abs]
- DeLeire, T; Khan, S; Timmins, C, ROY MODEL SORTING AND NONRANDOM SELECTION IN THE VALUATION OF A STATISTICAL LIFE,
International Economic Review, vol. 54 no. 1
(February, 2013),
pp. 279-306, ISSN 0020-6598 [Gateway.cgi], [doi]
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