A Simple Nonparametric Estimator for the Distribution of Random Coefficients
Kyoo il Kim () and
Stephen Ryan ()
No 15210, NBER Working Papers from National Bureau of Economic Research, Inc
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.
JEL-codes: C01 C14 C25 C31 C35 I21 I28 L0 O1 O15 (search for similar items in EconPapers)
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