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Non-Stationary Dynamic Factor Models for Large Datasets

Listed author(s):
  • Barigozzi, Matteo
  • Lippi, Marco
  • Luciani, Matteo

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.

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File URL: https://www.federalreserve.gov/econresdata/feds/2016/files/2016024r1pap.pdf
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File URL: https://www.federalreserve.gov/econresdata/feds/2016/files/2016024pap.pdf
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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2016-024.

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Length: 66 pages
Date of creation: 04 Mar 2016
Date of revision: 18 Jul 2017
Handle: RePEc:fip:fedgfe:2016-24
DOI: 10.17016/FEDS.2016.024r1
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