Stochastic optimization is an umbrella term that includes over a dozen fragmented
communities, using a patchwork of sometimes overlapping notational systems with …

Differentiated products demand systems are a workhorse for understanding the price effects
of mergers, the value of new goods, and the contribution of products to seller networks …

Agent-based modeling (ABM) is a novel computational methodology for representing the
behavior of individuals in order to study social phenomena. Its use is rapidly growing in …

Recognized as an efficient approach to reduce fossil fuel consumption and alleviate
environment crisis, the adoption of electric vehicles (EVs) in urban transportation system is …

Dynamic stochastic general equilibrium (DSGE) models have become one of the
workhorses of modern macroeconomics and are extensively used for academic research as …

Uncertainty about future economic and climate conditions substantially affects the choice of
policies for managing interactions between the climate and the economy. We develop a …

We introduce a unified deep learning method that solves dynamic economic models by
casting them into nonlinear regression equations. We derive such equations for three …

The toolkit adapts a first-order perturbation approach and applies it in a piecewise fashion to
solve dynamic models with occasionally binding constraints. Our examples include a real …

Uncertainty appears to jump up after major shocks like the Cuban Missile crisis, the
assassination of JFK, the OPEC I oil‐price shock, and the 9/11 terrorist attacks. This paper …

We show how size-contingent laws can be used to identify the equilibrium and welfare
effects of labor regulation. Our framework incorporates such regulations into the Lucas …