3 papers
econ.EM2026
From dense grids to valid inference: Accounting for regularization bias in nonparametric random coefficient models
Lingwei Kong, Maximilian Osterhaus, Michael Pen
This paper develops an inference procedure for average functionals of random-coefficient distributions, such as mean willingness-to-pay and average elasticities, when the distribut…
econ.EM2024
Deep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models
Stephan Hetzenecker, Maximilian Osterhaus
This paper studies the finite sample performance of the flexible estimation approach of Farrell, Liang, and Misra (2021a), who propose to use deep learning for the estimation of he…
econ.EM2024
A Sparse Grid Approach for the Nonparametric Estimation of High-Dimensional Random Coefficient Models
Maximilian Osterhaus
A severe limitation of many nonparametric estimators for random coefficient models is the exponential increase of the number of parameters in the number of random coefficients incl…