Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)
NBER Working Paper No. 16928
---- Acknowledgements -----
We would like to thank David Card, Stephen Cosslett, Jinyong Hahn, Michael Jansson, Patrick Kline, Richard Smith, Tom Rothenberg, and members of the Berkeley Econometrics Reading Group for helpful discussions. We are particularly grateful to Gary Chamberlain, Guido Imbens, Justin McCrary, Geert Ridder and Enrique Sentana for detailed comments on earlier drafts. This draft has benefited from comments by the co-editor, associate editor and three anonymous referees. We thank Jing Qin and Biao Zhang for assistance in replicating the Monte Carlo designs in Qin and Zhang (2008). 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, University of California - Berkeley, CEMFI (Madrid) and Pontifícia Universidade Católica do Rio de Janeiro. Portions of the current paper previously appeared in Section 4 of a NBER Working Paper titled 'Inverse probability tilting and missing data problems'. The latest revision of that paper excludes the material reported here. A supplemental appendix with proofs and additional details regarding computation may be found on the second author's web page. All the usual disclaimers apply. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.