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

DOI10.1016/j.jeconom.2019.11.008zbMath1471.62418OpenAlexW3123911497MaRDI QIDQ2658770

Joel L. Horowitz, Lars P. Nesheim

Publication date: 24 March 2021

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10419/189739

zbMATH Keywords

random coefficientsadaptive Lassologit modelpenalized estimation

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (1)

A new autoregressive process driven by explanatory variables and past observations: an application to PM 2.5

Uses Software

• SNOPT
• nwSpGr

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• Likelihood approximation by numerical integration on sparse grids
• Statistics for high-dimensional data. Methods, theory and applications.
• The positive semidefiniteness of partitioned matrices
• Simulation of multivariate normal rectangle probabilities and their derivatives. Theoretical and computational results
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Discrete Choice Methods with Simulation
• Simulation and the Asymptotics of Optimization Estimators
• Applied Welfare Economics with Discrete Choice Models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Penalized estimation of high-dimensional models under a generalized sparsity cindition
• Tuning parameter selectors for the smoothly clipped absolute deviation method

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