TY  - GEN
UR  - https://doi.org/10.1920/wp.cem.2019.5019
TI  - Using penalized likelihood to select parameters in a random coefficients multinomial logit model
KW  - random coefficients
KW  -  logit
KW  -  penalized likelihood
KW  -  LASSO
N1  - This version is the version of record. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10060499
AV  - restricted
EP  - 26
N2  - 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.
CY  - London, UK
T3  - cemmap Working Paper
PB  - Centre for microdata methods and practice (cemmap)
A1  - Horowitz, JL
A1  - Nesheim, L
Y1  - 2019/09//
ER  -