We develop methodology for estimation and inference using machine learning to enrich economic models. Our framework takes a standard economic model and recasts the parameters as fully flexible nonparametric functions, to capture the rich heterogeneity based on potentially high dimensional or...

This article proposes a distributed Markov chain Monte Carlo (MCMC) algorithm for estimating Bayesian hierarchical models when the number of cross-sectional units is very large and the objects of interest are the unit-level parameters. The two-stage algorithm is asymptotically exact, retains the...

Choice environments in practice are often more complicated than the well studied case of choice between perfect substitutes. Consumers choosing from menus or configuring products face choice sets that consist of substitutes, complements and independent items, and the utility maximizing choice...

We propose a new likelihood-based estimation method for the sequential search model. By allowing search costs to be heterogeneous across consumers and products, we can directly compute the joint probability of the search sequence and the purchase decision when consumers are searching for the...

About 10% of the US labor force is employed in selling related occupations and the expenditures on selling activities total close to 5% of the US GDP. Without question, selling occupies a prominent role in our economy. This chapter offers a discussion on the construct of selling, its role in...

We study complementarities between brands in the context of collaborations across museums. Over the course of our sample, one major museum with a highly recognized brand closed temporarily and sequentially collaborated with two established local museums. With individual panel data on museum...

Abstract We study the welfare implications of personalized pricing, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. We conduct a randomized controlled pricing field experiment to train a demand model and to conduct inferences...

This paper proposes a post-model selection inference procedure, called targeted undersmoothing, designed to construct uniformly valid confidence sets for functionals of sparse high-dimensional models, including dense functionals that may depend on many or all elements of the high-dimensional...

This study provides evidence on the determinants of contract duration using a large sample of franchise contracts. We find that the term of the contract systematically increases with the franchisee's physical and human capital investments, measures of recontracting costs, and the franchisor's...