@article{b0db0e9c18fb4686b08630561b7eb338,
title = "Using penalized likelihood to select parameters in a random coefficients multinomial logit model",
abstract = "This paper is about estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters, some of which may be zero. The paper gives conditions under which, with probability approaching 1 as the sample size increases, penalized maximum likelihood (PML) estimation with the adaptive LASSO (AL) penalty distinguishes correctly between zero and non-zero parameters. The paper also gives conditions under which PML reduces the asymptotic mean-square estimation error of any continuously differentiable function of the model's parameters. The paper describes a method for computing PML estimates and presents the results of Monte Carlo experiments that illustrate their performance. It also presents the results of PML estimation of a random coefficients logit model of choice among brands of butter and margarine in the British groceries market.",
keywords = "Adaptive LASSO, Logit model, Penalized estimation, Random coefficients",
author = "Horowitz, {Joel L.} and Lars Nesheim",
note = "Funding Information: Research carried out in part while Joel L. Horowitz was a visitor to the Department of Economics, University College London. We gratefully acknowledge financial support from the Economic and Social Research Council (ESRC), UK through the ESRC Centre for Microdata Methods and Practice (CeMMAP) grant number ES/1034021/1 and ESRC Large Research Grant ES/P008909/1. Data were provided by Kantar UK Ltd. The use of the data in this research does not imply endorsement by Kantar UK Ltd. of the interpretation or analysis of the data. Funding Information: Research carried out in part while Joel L. Horowitz was a visitor to the Department of Economics, University College London. We gratefully acknowledge financial support from the Economic and Social Research Council (ESRC), UK through the ESRC Centre for Microdata Methods and Practice (CeMMAP) grant number ES/1034021/1 and ESRC Large Research Grant ES/P008909/1 . Data were provided by Kantar UK Ltd. The use of the data in this research does not imply endorsement by Kantar UK Ltd. of the interpretation or analysis of the data. Publisher Copyright: {\textcopyright} 2020 Elsevier B.V.",
year = "2021",
month = may,
doi = "10.1016/j.jeconom.2019.11.008",
language = "English (US)",
volume = "222",
pages = "44--55",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "1",
}