March 2016 (Revised August 2017)

Non-Stationary Dynamic Factor Models for Large Datasets

Matteo Barigozzi, Marco Lippi, and Matteo Luciani


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 < 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.

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Keywords: Dynamic Factor models, unit root processes, cointegration, common trends, impulse response functions


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Last Update: June 19, 2020