Instrumental variable estimation with heteroskedasticity and many instruments
2012; Wiley; Volume: 3; Issue: 2 Linguagem: Inglês
10.3982/qe89
ISSN1759-7331
AutoresJerry A. Hausman, Whitney K. Newey, Tiemen Woutersen, John C. Chao, Norman R. Swanson,
Tópico(s)Housing Market and Economics
ResumoQuantitative EconomicsVolume 3, Issue 2 p. 211-255 Open Access Instrumental variable estimation with heteroskedasticity and many instruments Jerry A. Hausman, Jerry A. Hausman Department of Economics, M.I.T; [email protected]Search for more papers by this authorWhitney K. Newey, Whitney K. Newey Department of Economics, M.I.T; [email protected]Search for more papers by this authorTiemen Woutersen, Tiemen Woutersen Department of Economics, University of Arizona; [email protected]Search for more papers by this authorJohn C. Chao, John C. Chao Department of Economics, University of Maryland; [email protected]Search for more papers by this authorNorman R. Swanson, Norman R. Swanson Department of Economics, Rutgers University; [email protected] The NSF provided financial support for this paper under Grant 0136869. Helpful comments were provided by A. Chesher and participants in seminars at CalTech, CEMMAP, Harvard, MIT, Pittsburgh, UC Berkeley, UCL, and USC. Capable research assistance was provided by H. Arriizumi, S. Chang, A. Kowalski, R. Lewis, N. Lott, and K. Menzel. K. Menzel derived the vectorized form of the variance estimator.Search for more papers by this author Jerry A. Hausman, Jerry A. Hausman Department of Economics, M.I.T; [email protected]Search for more papers by this authorWhitney K. Newey, Whitney K. Newey Department of Economics, M.I.T; [email protected]Search for more papers by this authorTiemen Woutersen, Tiemen Woutersen Department of Economics, University of Arizona; [email protected]Search for more papers by this authorJohn C. Chao, John C. Chao Department of Economics, University of Maryland; [email protected]Search for more papers by this authorNorman R. Swanson, Norman R. Swanson Department of Economics, Rutgers University; [email protected] The NSF provided financial support for this paper under Grant 0136869. Helpful comments were provided by A. Chesher and participants in seminars at CalTech, CEMMAP, Harvard, MIT, Pittsburgh, UC Berkeley, UCL, and USC. Capable research assistance was provided by H. Arriizumi, S. Chang, A. Kowalski, R. Lewis, N. Lott, and K. Menzel. K. Menzel derived the vectorized form of the variance estimator.Search for more papers by this author First published: 25 July 2012 https://doi.org/10.3982/QE89Citations: 65 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract This paper gives a relatively simple, well behaved solution to the problem of many instruments in heteroskedastic data. Such settings are common in microeconometric applications where many instruments are used to improve efficiency and allowance for heteroskedasticity is generally important. The solution is a Fuller (1977) like estimator and standard errors that are robust to heteroskedasticity and many instruments. We show that the estimator has finite moments and high asymptotic efficiency in a range of cases. The standard errors are easy to compute, being like White's (1982), with additional terms that account for many instruments. They are consistent under standard, many instrument, and many weak instrument asymptotics. We find that the estimator is asymptotically as efficient as the limited-information maximum likelihood (LIML) estimator under many weak instruments. In Monte Carlo experiments, we find that the estimator performs as well as LIML or Fuller (1977) under homoskedasticity, and has much lower bias and dispersion under heteroskedasticity, in nearly all cases considered. 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