Entrepreneurial Investment Dynamics and the Wealth Distribution (Job Market Paper) [Link to most recent draft]
“Capital-Reallocation Frictions and Trade Shocks”, joint with Andrea Lanteri and Pamela Medina [Link to most recent draft]
Abstract: What are the short- and medium-run aggregate effects of an international-trade shock that increases competition for domestic manufacturing industries? In this paper, we address this question by combining detailed firm-level investment data from several manufacturing industries in Peru, data on the import penetration of Chinese manufacturing goods in Peru, and a quantitative general-equilibrium model of trade with heterogeneous firms subject to idiosyncratic shocks. We find evidence of large frictions in disinvestment (i.e., capital reallocation), that induce high persistence of low returns from capital at the firm level, and dampen the empirical response of reallocation to Chinese import competition. In our calibrated model, these frictions play a key role in the dynamic response of the economy to an import-competition shock, inducing slow transitional dynamics and several years of low aggregate productivity, while the distribution of firm-level capital and productivity adjusts to the new steady state.
“Uncertainty Shocks and Entrepreneurship” [Link to most recent draft]
Work in Progress
“Illiquidity Risk, Partial Risk Sharing, Entrepreneurship, and the Wealth Distribution”
“A Fast and Low Computational Memory Algorithm for Computing Distributions in Heterogeneous Agent Models” [Link to paper]
Heterogeneous agent models in macroeconomics generally require numerical computation of the cross-sectional distribution of agents, either in steady state or across time. One standard approach is to utilize “non-stochastic simulation”, as described in Young (2010). This method requires an approximation of the Markov kernel that iterates the distribution forward in time. The textbook method calls for a full approximation of the Markov kernel as a Markov transition matrix, which can be costly in terms of computational time and memory when the state space is large. This paper proposes an alternative simple method that requires much less computational memory, and is substantially faster than the standard method in the case of models with large state spaces.