Sargent Reading Group Notes

In 2013; Mian, Rao, Sufi used proprietary data on the US housing market (obtained from Core Logic) and personal consumption expenditures (obtained from master card) from 2006-2009 to estimate that the elasticity of consumption expenditures to changes in the housing share of household net worth. This paper replicates the main results from Mian, Rao, and Sufi using data that is more easily accessed by economists in academia. Data The authors proxy the housing data from Core Logic, using housing data from Zillow.

This paper aims to explain the low exchange rate pass-through for exporters. Exchange rate pass-through is the response in export prices to movement in the exchange rate. Model The theoretical model is not the focus of the authors’ analysis in this paper. They write down a fairly complicated model, derive some of the equilibrium conditions, then use the implications of the equilibrium conditions as testable predictions they take to the data.

A mostly empirical paper that examines the inputs used by Chinese firms to produce exported goods. DVAR The empirical analysis in this paper is centered around a variable named DVA, which stands for domestic value added in exports. To derive DVA, we start with total revenue. The authors break total revenue into the sum of profits labor costs capital costs materials from domestic sources materials from foreign sources Because intermediate good producers can get their inputs from China or foreign sources, the domestic and foreign materials sources are decomposed into Chinese and non-Chinese components.

An economist, a physicist, and two computer scientists walk into a bar… This is a computational paper that describes and algorithm featuring an adaptive sparse grid and discuss implementation details on a sophisticated HPC cluster. Model They provide examples of their algorithm and computation using a standard international real business cycle model. This is not the interesting part of the paper, so I will not focus on it here. Computation Adaptive Sparse Grids The first main contribution of this paper is to introduce economists to a specialized flavor of function approximation.

This paper builds on the approximate linear programming work of Farias and van Roy that I presented a few weeks ago and applies a version of that technique to a dynamic oligopoly model. The actual model is not novel to this research, so I will spend most of my time talking about the algorithm. Model The model is set in discrete time and multiple firms compete in a single good market over an infinite horizon.

This is a mostly empirical paper that tries to decompose exporter persistence into sunk cost and learning components. Model Let firms be indexed by i. Firms have two state variables: productivity zi, t that follows an AR(1) process and export experience Ai, t. In Ai, t is the number of consecutive periods a firm has exported coming into period t. Without microfoudations, Timoshenko assumes that per-period sales are the Melitz result, multiplied by a function g of export experience:

This paper documents facts about Brazilian manufacturing firms that switch their bundle of exported products over time. The author then builds a Melitz-style trade model that attempts to explain these facts. Empirics The author uses data product level data on Brazilian manufacturing firms to document several stylized facts. 72% of continuing exporters alter their product mix every year (add new exported products or drop existing exported products) 83% of all Brazilian exports come from these product switching firms The proportion of exporters who do product switching falls with age in export market The frequency with which exporters engage in product switching falls with age The exit rate of product switching firms is lower than aggregate exit rate for all ages of exporter.

Background When a Markov decision process (MDP) is formulated as a dynamic programming problem, the reinforcement learning literature proposes are two classic classes of algorithms to solve them. Let’s briefly review these types of algorithms and point out strengths and weaknesses of each. 1: Actor only methods We can think of an actor as a fictitious character that operates on a policy rule. When I talk about the performance of an actor, I mean the value of following a policy.

This paper provides an overview of various solution concepts that belong to the families of approximate dynamic programming and reinforcement learning. I will discuss an alternative representation of a dynamic programming that is often used in this literature and provide an overview of some solution methods that are common with this representation. Basics: Q-functions In economics, the standard representation of a dynamic programming problem is to define a recursive function V that maps from a state space into the real line.

This paper was published in Operations Research and as such they use a different notation and jargon than economists. I’ll present some of their main results, but in a language and notation that is familiar to me. Background Consider a typical dynamic programming problem faced by an economic agent, which we summarize by the following Bellman equation: $$V(x) = \underset{c \in \Gamma(x)}{\max} u(x, c) + \beta E V(x')$$ Now define the natural operator associated with the Bellman, which we call T: