Working Paper: NBER ID: w15210
Authors: Patrick Bajari; Jeremy T. Fox; Kyoo Il Kim; Stephen P. Ryan
Abstract: We propose a simple nonparametric mixtures estimator for recovering the joint distribution of parameter heterogeneity in economic models, such as the random coefficients logit. The estimator is based on linear regression subject to linear inequality constraints, and is robust, easy to program and computationally attractive compared to alternative estimators for random coefficient models. We prove consistency and provide the rate of convergence under deterministic and stochastic choices for the sieve approximating space. We present a Monte Carlo study and an empirical application to dynamic programming discrete choice with a serially-correlated unobserved state variable.
Keywords: nonparametric estimator; random coefficients; econometrics; discrete choice; dynamic programming
JEL Codes: C01; C14; C25; C31; C35; I21; I28; L0; O1; O15
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| nonparametric mixtures estimator (C14) | flexibility in modeling (C52) |
| nonparametric mixtures estimator (C14) | unique global optimum (C61) |
| random coefficients (C39) | choice probabilities (C25) |
| nonparametric mixtures estimator (C14) | joint distribution of parameter heterogeneity (C46) |
| nonparametric mixtures estimator (C14) | estimation of random coefficients (C51) |