Weak identification robust tests in an instrumental quantile model
We develop a testing procedure that is robust to identification quality in an instrumental quantile model. In order to reduce the computational burden, a multi-step approach is taken, and a two-step Anderson–Rubin (AR) statistic is considered. We then propose an orthogonal decomposition of the AR st...
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| Veröffentlicht in: | Journal of econometrics 2008-05, Vol.144 (1), p.118-138 |
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| container_title | Journal of econometrics |
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| creator | Jun, Sung Jae |
| description | We develop a testing procedure that is robust to identification quality in an instrumental quantile model. In order to reduce the computational burden, a multi-step approach is taken, and a two-step Anderson–Rubin (AR) statistic is considered. We then propose an orthogonal decomposition of the AR statistic, where the null distribution of each component does not depend on the assumption of a full rank of the Jacobian. Power experiments are conducted, and inferences on returns to schooling using the Angrist and Krueger data are considered as an empirical example. |
| doi_str_mv | 10.1016/j.jeconom.2007.12.006 |
| format | Article |
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| ispartof | Journal of econometrics, 2008-05, Vol.144 (1), p.118-138 |
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| language | eng |
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| source | RePEc; Elsevier ScienceDirect Journals |
| subjects | Applications Distribution theory Econometric models Economic models Exact sciences and technology Generalized method of moments GMM Instruments Insurance, economics, finance Mathematics Matrix Probability and statistics Probability theory and stochastic processes Quantile regression Regression analysis Sciences and techniques of general use Statistical methods Statistics Studies Weak identification |
| title | Weak identification robust tests in an instrumental quantile model |
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