Non-Stationary Dynamic Factor Models for Large Datasets

By Matteo Barigozzi, Marco Lippi, Matteo Luciani. 2016

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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.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/694f30d6a44ef3c632cd6ce54056fc6e33046ff1

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In Economics and Econometrics

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Macroeconomic forecast accuracy in a data‐rich environment (2019, background): “The logarithm of the real series (INDPRO and EMP) and (7)Bernanke et al. (2005) keep inflation, interest and unemployment rates in levels. Choosing (SW) or (BBE) transformations has effects on correlation patterns in Xt. Under (BBE), the group of interest rates is highly correlated as well as the in”