Non-Stationary Dynamic Factor Models for Large Datasets

Barigozzi, Matteo; Lippi, Marco; Luciani, Matteo

doi:10.17016/FEDS.2016.024r1

Working Paper

Non-Stationary Dynamic Factor Models for Large Datasets

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;

https://doi.org/10.17016/FEDS.2016.024r1