Identification in Macroeconomics

By Emi Nakamura, Jón Steinsson. Journal of Economic Perspectives, 2017

379 citations recorded by the source corpus.

This paper discusses empirical approaches macroeconomists use to answer questions like: What does monetary policy do? How large are the effects of fiscal stimulus? What caused the Great Recession? Why do some countries grow faster than others? Identification of causal effects plays two roles in this process. In certain cases, progress can be made using the direct approach of identifying plausibly exogenous variation in a policy and using this variation to assess the effect of the policy. However, external validity concerns limit what can be learned in this way. Carefully identified causal effects estimates can also be used as moments in a structural moment matching exercise. We use the term “identified moments” as a short-hand for “estimates of responses to identified structural shocks,” or what applied microeconomists would call “causal effects.” We argue that such identified moments are often powerful diagnostic tools for distinguishing between important classes of models (and thereby learning about the effects of policy). To illustrate these notions we discuss the growing use of cross-sectional evidence in macroeconomics and consider what the best existing evidence is on the effects of monetary policy.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/958b92a668db7ff7ea8ef952d90db003de4a387c

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Citations in the corpus

The Long-Run Effects of Monetary Policy (2020, methodology): “1), Nakamura and Steinsson (2018) report the multiplier of monthly industrial production to monetary shocks over 36 months to lie between 1 and 2 using the local projections specification for VAR shocks and Romer-Romer shocks.”
The Costs of Economic Nationalism: Evidence from the Brexit Experiment* (2019, methodology): “In this regard natural experiments differ from controlled experiments, “the holy grail of empirical science” (Nakamura and Steinsson, 2018).”
Local Projections vs. VARs: Lessons From Thousands of DGPs (2021, background): “Our findings provide a novel perspective on recent work emphasizing the potential dangers of VAR model mis-specification (Ramey, 2016; Nakamura & Steinsson, 2018).”
Demographic Origins of the Startup Deficit (2019, neutral): “25See the discussion of cross-sectional elasticities in Nakamura and Steinsson (2018) and references therein.”
Monetary Policy, Corporate Finance and Investment (2018, positive): “We isolate a time series of monetary policy shocks by exploiting the high frequency surprises in interest rate futures contracts within a 30 minute window around policy announcements, following Gurkaynak et al. (2005), Gertler and Karadi (2015) and Nakamura and Steinsson (2018a).”
Causal inference (2011, background): “1Curiously, several contributors to the causation literature appear to have confused the question of whether a variable is external with the unrelated question of whether it causes an internal variable via multiple paths (see, for example, Nakamura-Steinsson [24], p.”
Minimum Wages and Racial Inequality* (2020, background): “If this is the case, and if such general equilibrium effects are present, they are not captured in our cross-industry design (Nakamura and Steinsson, 2018).”
Local Projection Inference Is Simpler and More Robust Than You Think (2020, methodology): “…of local projections has allowed the methods of causal analysis in macroeconomics to connect with the rich toolkit for program evaluation in applied microeconomics; see for example Angrist et al. (2018), Nakamura and Steinsson (2018), Stock and Watson (2018), and Rambachan and Shephard (2019).”