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  3. 2022
  4. Preliminary Program
  5. Macro Identification Using High-Frequency and Micro Data
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Macro Identification Using High-Frequency and Micro Data

Paper Session

Friday, Jan. 7, 2022 3:45 PM - 5:45 PM (EST)

Hosted By: Society for Computational Economics
  • Chair: Christiane Baumeister, University of Notre Dame

Granular Instrumental Variables

Xavier Gabaix
,
Harvard University
Ralph S.J. Koijen
,
University of Chicago

Abstract

We propose a new way to construct instruments in a broad class of economic environments: “granular instrumental variables” (GIVs). In the economies we study, a few large firms, industries or countries account for an important share of economic activity. As the idiosyncratic shocks from these large players affect aggregate outcomes, they are valid and often powerful instruments. We provide a methodology to extract idiosyncratic shocks from the data in order to create GIVs, which are size-weighted sums of idiosyncratic shocks. These GIVs allow us to then estimate parameters of interest, including causal elasticities and multipliers. We first illustrate the idea in a basic supply and demand framework: we achieve a novel identification of both supply and demand elasticities based on idiosyncratic shocks to either supply or demand. We then show how the procedure can be enriched to work in many situations. We provide illustrations of the procedure with two applications. First, we measure how “sovereign yield shocks” transmit across countries in the Eurozone. Second, we estimate short-term supply and demand multipliers and elasticities in the oil market. Our estimates match existing ones that use 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.

Identifying Sectoral Shocks with a Modicum of Economic Theory: From the 1970s to the Pandemic

Ferre De Graeve
,
KU Leuven
Jan David Schneider
,
KU Leuven

Abstract

This paper proposes a new empirical approach to understand better the contribution of sectoral shocks in explaining aggregate fluctuations. The method exploits cross-sectional identification restrictions consistent with broad implications of production network (input-output) theories, without relying on dense DSGE assumptions and parametrisations, which most previous evidence is based on. Imposing these restrictions in a factor-augmented VAR for the U.S., the method identifies sectoral shocks. We present four main results:
(i) Sector-specific shocks generally explain a large part of aggregate fluctuations without relying on a particular theoretical model. This paper further shows that a pronounced shift has occurred recently: sectoral shocks have a more subdued role during the pandemic compared to previous U.S. recessions.
(ii) The identified sectoral shocks and their role in historical recessions accord well with the typical macro narrative. For instance, our estimates view the 2001 recession as a tech-related boom gone bust, or the Great Recession’s origins relate to construction.
(iii) While specific sectoral shocks are important for understanding certain periods, no single sector dominates in terms of determining aggregate fluctuations all of the time.
(iv) Sectoral shocks reach aggregate consequences largely through network amplification. This aggregate amplification can be easily two to three times larger than the contribution that only the originating sector has on aggregate activity.

Announcement-Specific Decompositions of Unconventional Monetary Policy Shocks & Their Macroeconomic Effects

Daniel J. Lewis
,
Federal Reserve Bank of New York and the University of Pennsylvania

Abstract

I propose to identify announcement-specific decompositions of asset price changes into monetary policy shocks exploiting heteroskedasticity in intraday data. This approach accommodates both changes in the nature of shocks and the state of the economy across announcements, allowing me to explicitly compare shocks across announcements. I compute decompositions with respect to Fed Funds, forward guidance, asset purchase, and Fed information shocks for 2007-2019. Only a handful of announcements spark significant shocks. Both forward guidance and asset purchase shocks lower yields and uncertainty and raise corporate spreads and equities; Fed information shocks raise yields and lower uncertainty. However, only asset purchase shocks significantly stimulate the macroeconomy, raising inflation and industrial production and lowering the unemployment rate.

The Unequal Economic Consequences of Carbon Pricing

Diego R. Kaenzig
,
London Business School

Abstract

This paper studies how carbon pricing affects emissions, economic aggregates and inequality. Exploiting institutional features of the European carbon market and high-frequency data, I identify a carbon policy shock. I find that a tighter carbon pricing regime leads to a significant increase in energy prices, a persistent fall in emissions and an uptick in green innovation. This comes at the cost of a temporary fall in economic activity, which is not borne equally across society: poorer households lower their consumption significantly while richer households are less affected. Not only are the poor more exposed because of their higher energy share, they also experience a larger fall in their income. These indirect effects account for over 80 percent of the aggregate effect on consumption. A climate-economy model with heterogeneity in households' energy shares, income incidence and marginal propensities to consume is able to account for these facts.

Discussant(s)
James D. Hamilton
,
University of California-San Diego
Christian Matthes
,
Indiana University
Eric T. Swanson
,
University of California-Irvine
James H. Stock
,
Harvard University
JEL Classifications
  • C3 - Multiple or Simultaneous Equation Models; Multiple Variables
  • E3 - Prices, Business Fluctuations, and Cycles