Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-27T18:20:45.106Z Has data issue: false hasContentIssue false

SOME EXTENSIONS OF A LEMMA OF KOTLARSKI

Published online by Cambridge University Press:  14 March 2012

Abstract

This note demonstrates that the conditions of Kotlarski’s (1967, Pacific Journal of Mathematics 20(1), 69–76) lemma can be substantially relaxed. In particular, the condition that the characteristic functions of M, U1, and U2 are nonvanishing can be replaced with much weaker conditions: The characteristic function of U1 can be allowed to have real zeros, as long as the derivative of its characteristic function at those points is not also zero; that of U2 can have an isolated number of zeros; and that of M need satisfy no restrictions on its zeros. We also show that Kotlarski’s lemma holds when the tails of U1 are no thicker than exponential, regardless of the zeros of the characteristic functions of U1, U2, or M.

Type
Brief Report
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We thank Stéphane Bonhomme, Susanne Schennach, the editor, the co-editor, and two anonymous referees for helpful comments. Evdokimov gratefully acknowledges the support from the Gregory C. Chow Econometric Research Program at Princeton University.

References

REFERENCES

Arellano, M. & Bonhomme, S. (2009) Identifying Distributional Characteristics in Random Coefficients Panel Data Models. Working paper, CEMFI.CrossRefGoogle Scholar
Bonhomme, S. & Robin, J.-M. (2010) Generalized non-parametric deconvolution with an application to earnings dynamics. Review of Economic Studies 77(2), 491533.Google Scholar
Carrasco, M. & Florens, J.-P. (2011) A spectral method for deconvolving a density. Econometric Theory 27, 546581.Google Scholar
Devroye, L. (1989) Consistent deconvolution in density estimation. Canadian Journal of Statistics 17, 235239.Google Scholar
Evdokimov, K. (2008) Identification and Estimation of a Nonparametric Panel Data Model with Unobserved Heterogeneity. Working paper, Yale University.Google Scholar
Evdokimov, K. (2010) Nonparametric Identification of a Nonlinear Panel Model with Application to Duration Analysis with Multiple Spells. Working paper, Princeton University.Google Scholar
Kennan, J. & Walker, J.R. (2011) The effect of expected income on individual migration decisions. Econometrica 79(1), 211251.Google Scholar
Kotlarski, I. (1967) On characterizing the gamma and the normal distribution. Pacific Journal Of Mathematics 20(1), 6976.10.2140/pjm.1967.20.69Google Scholar
Krasnokutskaya, E. (2011) Identification and estimation of auction models with unobserved heterogeneity. The Review of Economic Studies 78(1), 293327.Google Scholar
Li, T., Perrigne, I., & Vuong, Q. (2000) Conditionally independent private information in OCS wildcat auctions. Journal of Econometrics 98(1), 129161.CrossRefGoogle Scholar
Li, T. & Vuong, Q. (1998) Nonparametric estimation of the measurement error model using multiple indicators. Journal of Multivariate Analysis 65, 139165.CrossRefGoogle Scholar
Paley, R. & Wiener, N. (1934) Fourier Transforms in the Complex Domain. Colloquium Publications. American Mathematical Society.Google Scholar
Schennach, S.M. (2004) Estimation of nonlinear models with measurement error. Econometrica 72(1), 3375.Google Scholar
Schennach, S.M. (2007) Instrumental variable estimation of nonlinear errors-in-variables models. Econometrica 75(1), 201239.Google Scholar
Zinde-Walsh, V. (2010) Measurement error and convolution in generalized functions spaces. ArXiv e-prints.Google Scholar