Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation / Jackson Bunting, Paul Diegert, Arnaud Maurel.

Format:

Book

Author/Creator:

Bunting, Jackson.

Contributor:

National Bureau of Economic Research.

Diegert, Paul.

Maurel, Arnaud.

Series:

Working Paper Series (National Bureau of Economic Research) no. w32164.

NBER working paper series no. w32164

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2024.

Summary:

We provide semiparametric identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: i) known heterogeneity, ii) initially unknown heterogeneity that may be revealed over time, and iii) transitory uncertainty. We consider a common environment where the researcher only has access to a short panel on choices and realized outcomes. We establish identification of the outcome equation parameters and the distribution of the three types of unobservables, under the standard assumption that unknown heterogeneity and uncertainty are normally distributed. We also show that, absent known heterogeneity, the model is identified without making any distributional assumption. We then derive the asymptotic properties of a sieve MLE estimator for the model parameters, and devise a tractable profile likelihood based estimation procedure. Monte Carlo simulation results indicate that our estimator exhibits good finite-sample properties.

Notes:

Print version record

February 2024.