Linear Estimation of Aggregate Dynamic Discrete Demand for Durable Goods: Overcoming the Curse of Dimensionality.

We develop a new approach using market-level data to model, identify, and estimate a dynamic discrete choice demand model for durable goods with continuous unobserved product-specific state variables. They are specified as serially correlated and correlated with the observed product characteristics, particularly price. We provide a method to estimate all model primitives, including the consumer's discount factor and the state transition distributions of unobserved product characteristics without the need to reduce the dimension of the state space or by other approximation techniques, such as discretizing state variables. We prove the identification of model primitives and provide an estimation algorithm in which the most computationally demanding step is a linear regression. Finally, we show how it can be implemented in an application in which we estimate the demand for smartphones.