NBER Publications by Stephen P. Ryan

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.

The random coefficients, multinomial choice logit model has been widely used in empirical choice analysis for the last 30 years. We are the first to prove that the distribution of random coefficients in this model is nonparametrically identified. Our approach exploits the structure of the logit model, and so requires no monotonicity assumptions and requires variation in product characteristics within only an infinitesimally small open set. Our identification argument is constructive and may be applied to other choice models with random coefficients.

The US mobile phone service industry has dramatically consolidated over the last two decades. One justification for consolidation is that merged firms can provide consumers with larger coverage areas at lower costs. We estimate the willingness to pay for national coverage to evaluate this motivation for past consolidation. As market level quantity data is not publicly available, we devise an econometric procedure that allows us to estimate the willingness to pay using market share ranks collected from a popular online retailer, Amazon. Our semiparametric maximum score estimator controls for consumers%u2019 heterogeneous preferences for carriers, handsets and minutes of calling time. We find that national coverage is strongly valued by consumers, providing an efficiency justification for ac...