Evan M. Munro
Graduate School of Business
655 Knight Way
Stanford, CA 94305
Institutional Affiliation: Stanford University
NBER Working Papers and Publications
|May 2020||Latent Dirichlet Analysis of Categorical Survey Expectations|
with : w27182
Beliefs are important determinants of an individual's choices and economic outcomes, so understanding how they differ across individuals is of considerable interest. Researchers often rely on surveys that report individual expectations as qualitative data. We propose using a Bayesian hierarchical latent class model to summarize and interpret observed heterogeneity in categorical expectations data. We show that the statistical model corresponds to an economic structural model of information acquisition, which guides interpretation and estimation of the model parameters. An algorithm based on stochastic optimization is proposed to estimate a model for repeated surveys when beliefs follow a dynamic structure and conjugate priors are not appropriate. Guidance on selecting the number of belief...
|December 2019||Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations|
with , , : w26566
When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the freedom the researcher has in choosing the design. In recent years a new class of generative models emerged in the machine learning literature, termed Generative Adversarial Networks (GANs) that can be used to systematically generate artificial data that closely mimics real economic datasets, while limiting the degrees of freedom for the researcher and optionally satisfying privacy guarantees with respect to their training data. In addition if an applied researcher is concerned with the performance of a particular statistical method on a specific...