Non-Stationary Dynamic Factor Models for Large Datasets

67 Pages Posted: 31 Mar 2016 Last revised: 1 Sep 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

Marco Lippi

Dipartimento di Scienze Economiche (DiSSE); Einaudi Institute for Economics and Finance (EIEF)

Matteo Luciani

Board of Governors of the Federal Reserve System

Multiple version iconThere are 2 versions of this paper

Date Written: 2016-03-04

Abstract

We study a Large-Dimensional Non-Stationary Dynamic Factor Model where (1) the factors Ft are I (1) and singular, that is Ft has dimension r and is driven by q dynamic shocks with q less than r, (2) the idiosyncratic components are either I (0) or I (1). Under these assumption the factors Ft are cointegrated and modeled by a singular Error Correction Model. We provide conditions for consistent estimation, as both the cross-sectional size n, and the time dimension T, go to infinity, of the factors, the loadings, the shocks, the ECM coefficients and therefore the Impulse Response Functions. Finally, the numerical properties of our estimator are explored by means of a MonteCarlo exercise and of a real-data application, in which we study the effects of monetary policy and supply shocks on the US economy.

Keywords: Dynamic Factor models, Cointegration, Common trends, Impulse response functions, Unit root processes

JEL Classification: C00, C01, E00

Suggested Citation

Barigozzi, Matteo and Barigozzi, Matteo and Barigozzi, Matteo and Lippi, Marco and Luciani, Matteo, Non-Stationary Dynamic Factor Models for Large Datasets (2016-03-04). FEDS Working Paper No. 2016-024, Available at SSRN: https://ssrn.com/abstract=2756940 or http://dx.doi.org/10.17016/FEDS.2016.024r1

Matteo Barigozzi (Contact Author)

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

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University of Bologna ( email )

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Marco Lippi

Dipartimento di Scienze Economiche (DiSSE) ( email )

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Einaudi Institute for Economics and Finance (EIEF) ( email )

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Matteo Luciani

Board of Governors of the Federal Reserve System ( email )

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Washington, DC 20551
United States

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