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  3. 2026
  4. Preliminary Program
  5. Causal Inference in Macroeconometrics
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Causal Inference in Macroeconometrics

Paper Session

Saturday, Jan. 3, 2026 8:00 AM - 10:00 AM (EST)

Philadelphia Marriott Downtown
Hosted By: Econometric Society
  • Chair: Mikkel Plagborg-Møller, Princeton University

Micro Responses to Macro Shocks

Martin Almuzara
,
Federal Reserve Bank of New York
Víctor Sancibrián
,
Bocconi University

Abstract

We study panel data regression models when the shocks of interest are aggregate and possibly small relative to idiosyncratic noise. This speaks to a large empirical literature that targets impulse responses via panel local projections. We show how to interpret the estimated coefficients when units have heterogeneous responses and how to obtain valid standard errors and confidence intervals. A simple recipe leads to robust inference: including lags as controls and then clustering at the time level. This strategy is valid under general error dynamics and uniformly over the degree of signal-to-noise of macro shocks.

Unified Bayesian-Frequentist Inference for SVARs

Timothy Christensen
,
Yale University
Juan Rubio-Ramirez
,
Emory University

Abstract

There is a widely held view that asymptotic equivalence between Bayesian and frequentist inference necessarily breaks down under partial identification. We show that for inference on impulse response functions (IRFs) in structural vector autoregressions (SVARs), one can construct Bayesian credible sets that are also valid frequentist confidence sets. Specifically, the highest posterior density credible sets for the vector of structural IRFs are valid in a frequentist sense. Moreover, dual Bayesian and frequentist validity for inference on subsets of structural IRFs can be achieved through profiling. We provide computationally attractive algorithms for constructing these sets and demonstrate their use with several applications.

Semiparametric Inference for Impulse Response Functions Using Double/Debiased Machine Learning

Daniele Ballinari
,
Swiss National Bank
Alexander Wehrli
,
Swiss National Bank

Abstract

We introduce a double/debiased machine learning (DML) estimator for the impulse response function (IRF) in settings where a time series of interest is subjected to multiple discrete treatments, assigned over time, which can have a causal effect on future outcomes. The proposed estimator can rely on fully nonparametric relations between treatment and outcome variables, opening up the possibility to use flexible machine learning approaches to estimate IRFs. To this end, we extend the theory of DML from an i.i.d. to a time series setting and show that the proposed DML estimator for the IRF is consistent and asymptotically normally distributed at the parametric rate, allowing for semiparametric inference for dynamic effects in a time series setting. The properties of the estimator are validated numerically in finite samples by applying it to learn the IRF in the presence of serial dependence in both the confounder and observation innovation processes. We also illustrate the methodology empirically by applying it to the estimation of the effects of macroeconomic shocks.

Discussant(s)
Laura Liu
,
University of Pittsburgh
Toru Kitagawa
,
Brown University
Ashesh Rambachan
,
Massachusetts Institute of Technology
JEL Classifications
  • C2 - Single Equation Models; Single Variables
  • E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook