TY - JOUR
T1 - Robust data-driven inference for density-weighted average derivatives
AU - Cattaneo, Matias D.
AU - Crump, Richard K.
AU - Jansson, Michael
N1 - Funding Information:
Matias D. Cattaneo is Assistant Professor of Economics, Department of Economics, University of Michigan, Ann Arbor, MI 48109-1220 (E-mail: [email protected]). Richard K. Crump is Economist, Capital Markets Function, Federal Reserve Bank of New York, New York, NY 10045. Michael Jans-son is Associate Professor of Economics, Department of Economics, University of California at Berkeley and CREATES, Berkeley, CA 94720. The authors thank Sebastian Calonico, Lutz Kilian, seminar participants at Georgetown, Michigan, Penn State and Wisconsin, and conference participants at the 2009 Latin American Meeting of the Econometric Society and 2010 North American Winter Meeting of the Econometric Society for comments. We also thank the editor, associate editor, and a referee for comments and suggestions that improved this paper. The first author gratefully acknowledges financial support from the National Science Foundation (SES 0921505). The third author gratefully acknowledges financial support from the National Science Foundation (SES 0920953) and the research support of CREATES (funded by the Danish National Research Foundation).
PY - 2010/9
Y1 - 2010/9
N2 - This paper presents a novel data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth-dependent robust inference procedures proposed by Cattaneo, Crump, and Jansson (2009) are coupled with this new data-driven bandwidth selector. The resulting robust data-driven confidence intervals compare favorably to the alternative procedures available in the literature. The online supplemental material to this paper contains further results from the simulation study.
AB - This paper presents a novel data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth-dependent robust inference procedures proposed by Cattaneo, Crump, and Jansson (2009) are coupled with this new data-driven bandwidth selector. The resulting robust data-driven confidence intervals compare favorably to the alternative procedures available in the literature. The online supplemental material to this paper contains further results from the simulation study.
KW - Averaged derivative
KW - Bandwidth selection
KW - Robust inference
KW - Small bandwidth asymptotics
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U2 - 10.1198/jasa.2010.tm09590
DO - 10.1198/jasa.2010.tm09590
M3 - Article
AN - SCOPUS:78649397979
SN - 0162-1459
VL - 105
SP - 1070
EP - 1083
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 491
ER -