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DECONVOLUTING PREFERENCES AND ERRORS: AMODEL FOR BINOMIAL PANEL DATA

Published online by Cambridge University Press:  22 March 2010

Abstract

In many stated choice experiments researchers observethe random variables Vt,Xt, andYt =1{U +δXt+ εt <Vt},tT, whereδ is an unknown parameter andU andεt are unobservablerandom variables. We show that under weakassumptions the distributions of Uand εt and also theunknown parameter δ can beconsistently estimated using a sieved maximumlikelihood estimation procedure.

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Type
Brief Report
Copyright
Copyright © Cambridge University Press 2010

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Footnotes

We are grateful to Bo Honoré, the referees, andthe coeditor Jinyong Hahn for helpful comments.Mogens Fosgerau has received support from theDanish Social Science Research Council.

References

REFERENCES

Chen, X. (2007) Large sample sieve estimation of semi-nonparametric models. In Heckman, J. & Leamer, E. (eds.), Handbook of Econometrics, vol. 6, pp. 55495632. North-Holland.CrossRefGoogle Scholar
Fosgerau, M. & Nielsen, S.F. (2007) Deconvoluting preferences and Errors: A Model for Binomial Panel data. Munich Personal RePEc Archive, http://mpra.ub.uni-muenchen.de/3950/1/MPRA_paper_3950.pdf.Google Scholar
Honoré, B.E. & Lewbel, A. (2002) Semiparametric binary choice panel data models without strictly exogenous regressors. Econometrica 70, 20532063.CrossRefGoogle Scholar
Horowitz, J. & Markatou, M. (1996) Semiparametric estimation of regression models for panel data. Review of Economic Studies 63, 145168.CrossRefGoogle Scholar
Lewbel, A. (2000) Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables. Journal of Econometrics 97, 145177.CrossRefGoogle Scholar
Li, Q. & Racine, J.S. (2007) Nonparametric Econometrics: Theory and Practice. Princeton University Press.Google Scholar
van der Vaart, A. & Wellner, J. (1996) Weak Convergence and Empirical Processes, 1st ed., Springer Series in Statistics. Springer-Verlag.CrossRefGoogle Scholar