%0 Generic
%A Horowitz, JL
%A Nesheim, L
%C London, UK
%D 2019
%F discovery:10060499
%I Centre for microdata methods and practice (cemmap)
%K random coefficients, logit, penalized likelihood, LASSO
%N CWP50/19
%T Using penalized likelihood to select parameters in a random coefficients multinomial logit model
%U https://discovery.ucl.ac.uk/id/eprint/10060499/
%X The multinomial logit model with random coefficients is widely used in applied research. This paper is  concerned with estimating a random coefficients logit model in which the distribution of each coefficient  is characterized by finitely many parameters. Some of these parameters may be zero. The paper gives  conditions under which with probability approaching 1 as the sample size approaches infinity, penalized  maximum likelihood (PML) estimation with the adaptive LASSO (AL) penalty function distinguishes  correctly between zero and non-zero parameters in a random coefficients logit model. If one or more  parameters are zero, then PML with the AL penalty function often reduces the asymptotic mean-square  estimation error of any continuously differentiable function of the model’s parameters, such as a market  share or an elasticity. The paper describes a method for computing the PML estimates of a random  coefficients logit model. It also presents the results of Monte Carlo experiments that illustrate the  numerical performance of the PML estimates. Finally, it 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.
%Z This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.