Measuring the Output Gap Using Large Datasets

44 Pages Posted: 8 Aug 2018 Last revised: 23 Oct 2019

See all articles by Matteo Barigozzi

Matteo Barigozzi

University of Bologna

Matteo Luciani

Federal Reserve Board

Date Written: October 23, 2019

Abstract

We decompose US aggregate output into potential output and the output gap by means of a non-stationary dynamic factor model estimated on a large dataset of macroeconomic indicators, combined with a non-parametric trend-cycle decomposition of the factors. We find that: (1) from the mid-90s to 2008 the US economy operated above its potential; (2) as of 2018:Q4 the labor market was tighter than the goods and services market; and (3) our output gap measure revises modestly in real-time. Due to its purely data driven nature, our measure is a natural complementary tool to the theoretical models used in policy institutions.

Keywords: Output Gap; Non-stationary Approximate Dynamic Factor Model; Trend-Cycle Decomposition

JEL Classification: C32, C38, C55, E0

Suggested Citation

Barigozzi, Matteo and Luciani, Matteo, Measuring the Output Gap Using Large Datasets (October 23, 2019). Available at SSRN: https://ssrn.com/abstract=3217816 or http://dx.doi.org/10.2139/ssrn.3217816

Matteo Barigozzi (Contact Author)

University of Bologna ( email )

Piazza Scaravilli 2
Bologna, 40100
Italy

Matteo Luciani

Federal Reserve Board ( email )

20th and C Streets, NW
Washington, DC 20551
United States

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