Using penalized likelihood to select parameters in a random coefficients multinomial logit model

Joel L. Horowitz () and Lars Nesheim
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Joel L. Horowitz: Institute for Fiscal Studies and Northwestern University

No CWP50/19, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: 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.

Date: 2019-10-15
New Economics Papers: this item is included in nep-dcm
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Related works:
Working Paper: Using penalized likelihood to select parameters in a random coefficients multinomial logit model (2018)
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