Working Paper: NBER ID: w17283
Authors: Jeremy T. Fox; Kyoo Il Kim
Abstract: We explore a nonparametric mixtures estimator for recovering the joint distribution of random coefficients in economic models. The estimator is based on linear regression subject to linear inequality constraints and is computationally attractive compared to alternative, nonparametric estimators. We provide conditions under which the estimated distribution function converges to the true distribution in the weak topology on the space of distributions. We verify the consistency conditions for discrete choice, continuous outcome and selection models.
Keywords: No keywords provided
JEL Codes: C14; L0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Proposed nonparametric estimator (C51) | Consistent under standard conditions (C29) |
| Sample size increases (C83) | Estimator converges to true distribution of random coefficients (C51) |
| Underlying economic model's characteristics (E13) | Estimator's convergence (C51) |
| Choice of grid points for estimation (C51) | Estimator's convergence (C51) |
| Estimator (C51) | Computationally simpler than traditional nonparametric methods (C14) |
| Method can be applied effectively to various economic models (C51) | Shares computational advantages with parametric methods (C59) |