Common Factors, Trends, and Cycles in Large Datasets

53 Pages Posted: 16 Nov 2017

See all articles by Matteo Barigozzi

Matteo Barigozzi

Université Libre de Bruxelles (ULB) - European Center for Advanced Research in Economics and Statistics (ECARES); London School of Economics and Political Science; University of Bologna

Matteo Luciani

Board of Governors of the Federal Reserve System

Date Written: 2017-11-13

Abstract

This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic properties of the estimator are discussed. Then, we show how to separate trends and cycles in the factors by mean of eigenanalysis of the estimated non-stationary factors. Finally, we employ our methodology on a panel of US quarterly macroeconomic indicators to estimate aggregate real output, or Gross Domestic Output, and the output gap.

Keywords: EM Algorithm, Gross Domestic Output, Kalman Smoother, Non-stationary Approximate Dynamic Factor Model, Output Gap, Quasi Maximum Likelihood, Trend-Cycle Decomposition

JEL Classification: C32, C38, C55, E00

Suggested Citation

Barigozzi, Matteo and Barigozzi, Matteo and Barigozzi, Matteo and Luciani, Matteo, Common Factors, Trends, and Cycles in Large Datasets (2017-11-13). FEDS Working Paper No. 2017-111, Available at SSRN: https://ssrn.com/abstract=3072264 or http://dx.doi.org/10.17016/FEDS.2017.111

Matteo Barigozzi (Contact Author)

Université Libre de Bruxelles (ULB) - European Center for Advanced Research in Economics and Statistics (ECARES) ( email )

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HOME PAGE: http://www.barigozzi.eu/research.html

London School of Economics and Political Science ( email )

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University of Bologna ( email )

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Matteo Luciani

Board of Governors of the Federal Reserve System ( email )

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Washington, DC 20551
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