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Identification in Macro-Finance: Recent Advances

Paper Session

Saturday, Jan. 4, 2020 8:00 AM - 10:00 AM (PST)

Marriott Marquis, Marina Ballroom F
Hosted By: American Economic Association
  • Chair: Emi Nakamura, University of California-Berkeley

What Do We Learn from Cross-Sectional Empirical Estimates in Macroeconomics?

Adam Guren
,
Boston University
Alisdair McKay
,
Federal Reserve Bank of Minneapolis
Emi Nakamura
,
University of California-Berkeley
Jon Steinsson
,
University of California-Berkeley

Abstract

Recent empirical work uses variation across cities or regions to identify the effects of economic shocks of interest to macroeconomists. The interpretation of such estimates is complicated by the fact that they reflect both partial equilibrium and local general equilibrium effects of the shocks. We present a simple method for recovering estimates of partial equilibrium effects from these cross-sectional empirical estimates. This approach makes use of structural assumptions and additional regression estimates to assess the strength of local general equilibrium effects. We apply our method to recent estimates of housing wealth effects based on city-level variation. For this case, we derive conditions under which the partial equilibrium effect of changes in house prices on consumption is equal to the city-level estimate divided by an estimate of the local fiscal multiplier, which measures the amplification from local general equilibrium. We evaluate the accuracy of this calculation in a multi-region, dynamic model of consumption and housing. The paper also reconciles the positive cross-sectional correlation between house price growth and construction with the notion that cities with larger price volatility have lower housing supply elasticities using a model in which housing supply elasticities are more dispersed in the long run than in the short run.

Shock Restricted Structural Vector Autoregressions

Sydney Ludvigson
,
New York University
Sai Ma
,
Federal Reserve Board
Serena Ng
,
Columbia University

Abstract

Identifying assumptions need to be imposed on dynamic models before they can be used to analyze the dynamic effects of economically interesting shocks. Often, the assumptions are only rich enough to identify a set of solutions. This paper considers two types of restrictions on the structural shocks that can help reduce the number of plausible solutions. The first is imposed on the sign and magnitude of the shocks during unusual episodes in history. The second restricts the correlation between the shocks and components of variables external to the model. These non-linear inequality constraints can be used in conjunction with zero and sign restrictions that are already widely used in the literature. The effectiveness of our constraints are illustrated using two applications of the oil market and Monte Carlo experiments calibrated to study the role of uncertainty
shocks in economic fluctuations.

Bartik Instruments: What, When, and Why and How?

Paul Goldsmith-Pinkham
,
Yale University
Isaac Sorkin
,
Stanford University
Henry Swift
,
Independent Researcher

Abstract

The Bartik instrument is formed by interacting local industry shares and national industry growth rates. We show that the Bartik instrument is numerically equivalent to using local industry shares as instruments in a GMM estimator and discuss how different asymptotics imply different identifying assumptions. We argue that in most applications the identifying assumption is in terms of industry shares. Finally, we show how to decompose the Bartik instrument into the weighted sum of the just-identified instrumental variables estimators. These weights measure how sensitive the parameter estimate is to each instrument. We illustrate our results through four applications: estimating the inverse elasticity of labor supply, estimating local labor market effects of Chinese imports, estimating the fiscal multiplier using defense spending shocks, and using simulated instruments to study the effects of Medicaid expansions.

Granular Instrumental Variables

Xavier Gabaix
,
Harvard University
Ralph Koijen
,
University of Chicago

Abstract

In many settings, there is a dearth of instruments, which hampers economists’ ability to investigate causal relations. We propose a quite general way to construct instruments: “granular instrumental variables” (GIVs). In the economies we study, a few large firms or countries account for a large share of economic activity. As they are large, their idiosyncratic shocks affect aggregate outcomes. This makes those idiosyncratic shocks valid instruments for aggregate shocks. We provide a methodology to extract idiosyncratic shocks from the data, this way creating GIVs. Those GIVs allow us to then estimate parameters of interest, including causal elasticities.
We first illustrate the idea in a basic supply and demand framework: we achieve a novel identification of supply and demand elasticities, based on idiosyncratic shocks to supply or demand. We then show how the procedure can be adapted to handle many enrichments. We provide initial illustrations of the procedure with two applications. First, we measure how shocks to domestic banks causally affect sovereign yields. We document how negative shocks to Italian banks adversely affect Italian government bond yields, and vice-versa. This gives the first causal measure of the “doom loop” between banks and sovereign yields. Second, we estimate short-term supply and demand elasticities in the oil market. Our estimates match well existing estimates that use much more complex and labor-intensive (e.g., narrative) methods.
We sketch how GIVs could be useful to estimate a host of other causal parameters in economics, particularly in aggregate macro-finance contexts where instruments are usually very rare.
Discussant(s)
Adrien Auclert
,
Stanford University
Harald Uhlig
,
University of Chicago
Rodrigo Adao
,
University of Chicago
Guido Imbens
,
Stanford University
JEL Classifications
  • E0 - General
  • G0 - General