Data and Code for: SVAR Identification From Higher Moments: Has the Simultaneous Causality Problem Been Solved?

Principal Investigator(s): View help for Principal Investigator(s) José Luis Montiel Olea, Columbia University; Mikkel Plagborg-Møller, Princeton University; Eric Qian, Princeton University

Version: View help for Version V1

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README.md	text/x-web-markdown	2.3 KB	04/12/2022 06:27:AM
README.txt	text/plain	2 KB	04/12/2022 06:27:AM

Project Citation:

Montiel Olea, José Luis, Plagborg-Møller, Mikkel, and Qian, Eric. Data and Code for: SVAR Identification From Higher Moments: Has the Simultaneous Causality Problem Been Solved? Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-04-27. https://doi.org/10.3886/E167401V1

Project Description

Summary: Two recent strands of the SVAR literature use higher moments for identification, either exploiting non-Gaussianity or heteroskedasticity. These approaches achieve point identification without exclusion or sign restrictions. We review this work critically, and contrast its goals with the separate research program that has pushed for macroeconometrics to rely more heavily on credible economic restrictions. Identification from higher moments imposes stronger assumptions on the shock process than second-order methods do. We recommend that these assumptions be tested. Since inference from higher moments places high demands on a finite sample, weak identification issues should be given priority by applied users.

Funding Sources: National Science Foundation (1851665)

Scope of Project

Subject Terms: Structural vector autoregression; non-gaussian identification; stochastic volatility

JEL Classification:
C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

Geographic Coverage: U.S.

Time Period(s): 10/1/1959 – 12/31/2019

Data Type(s): aggregate data; observational data; program source code

Methodology

Data Source: Federal Reserve Economic Data

Unit(s) of Observation: country

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Published Versions

V1 [2022-04-27]

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