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Estimating the Link Function in Multinomial Response Models under Endogeneity and Quadratic Loss

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Author Info
George Judge (University of California, Berkeley and Giannini Foundation)
Ron Mittelhammer (Washington State University)

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Abstract

This paper considers estimation and inference for the multinomial response model in the case where endogenous variables are arguments of the unknown link function. Semiparametric estimators are proposed that avoid the parametric assumptions underlying the likelihood approach as well as the loss of precision when using nonparametric estimation. A data based shrinkage estimator that seeks an optimal combination of estimators and results in superior risk performance under quadratic loss is also developed.

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File URL: http://repositories.cdlib.org/cgi/viewcontent.cgi?article=1049&context=are_ucb
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Publisher Info
Paper provided by Department of Agricultural & Resource Economics, UC Berkeley in its series Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series with number 970.

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Date of creation: 03 Feb 2004
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Handle: RePEc:cdl:agrebk:970

Note: oai:cdlib1:are_ucb-1049
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Related research
Keywords: multinomial process endogeneity empirical likelihood procedures quadratic loss semiparametric estimation and inference data dependent shrinkage asymptotic and finite sample risk

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Hong, Han & Tamer, Elie, 2003. "Endogenous binary choice model with median restrictions," Economics Letters, Elsevier, vol. 80(2), pages 219-225, August. [Downloadable!] (restricted)
  2. Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2, September. [Downloadable!]
  3. Ron Mittelhammer & George Judge & Ron Schoenberg, 2003. "Empirical Evidence Concerning the Finite Sample Performance of EL-Type Structural Equation Estimation and Inference Methods," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series 945, Department of Agricultural & Resource Economics, UC Berkeley. [Downloadable!]
  4. George Judge & Ron Mittelhammer, 2003. "A Semi-Parametric Basis for Combining Estimation Problems Under Quadratic Loss," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series 948, Department of Agricultural & Resource Economics, UC Berkeley. [Downloadable!]
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  5. Ichimura, H., 1991. "Semiparametric Least Squares (sls) and Weighted SLS Estimation of Single- Index Models," Papers 264, Minnesota - Center for Economic Research.
  6. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July. [Downloadable!] (restricted)
  7. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March. [Downloadable!] (restricted)
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