Inverse Probability Tilting for Moment Condition Models with Missing Data
NBER Working Paper No. 13981
---- Acknowledgements ----
We would like to thank David Card, Stephen Cosslett, Jinyong Hahn, Patrick Kline, Justin McCrary, Richard Smith, Tom Rothenberg, members of the Berkeley Econometrics Reading Group and especially Michael Jansson for helpful discussions. We are particularly grateful to Gary Chamberlain, Guido Imbens, Geert Ridder, Enrique Sentana and three anonymous referees for detailed comments on earlier drafts. We also acknowledge feedback and suggestions from participants in seminars at the University of Pittsburgh, Ohio State University, University of Southern California, University of California - Riverside, University of California - Davis, University of Maryland, Georgetown University, Duke University, the University of California - Berkeley, CEMFI (Madrid), Harvard University, Pontifícia Universidade Católica do Rio do Janeiro and the 2009 Latin American Meetings of the Econometric Society. This is a heavily revised and extended version of NBER Working Paper w13981 titled "Inverse probability tilting and missing data problems". Previous versions of this paper also circulated under the title "A new method of estimating moment condition models with missing data when selection is on observables." Material in Section 4 of the initial NBER paper is not included in this version of the paper, but may be found in the companion paper "Efficient estimation of data combination problems by the method of auxiliary-to-study tilting" (NBER Working Paper w16928 ). All the usual disclaimers apply. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.