Common Factors, Trends, and Cycles in Large Datasets
Matteo Barigozzi and
No 2017-111, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)
This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic properties of the estimator are discussed. Then, we show how to separate trends and cycles in the factors by mean of eigenanalysis of the estimated non-stationary factors. Finally, we employ our methodology on a panel of US quarterly macroeconomic indicators to estimate aggregate real output, or Gross Domestic Output, and the output gap.
Keywords: EM Algorithm; Gross Domestic Output; Kalman Smoother; Non-stationary Approximate Dynamic Factor Model; Output Gap; Quasi Maximum Likelihood; Trend-Cycle Decomposition (search for similar items in EconPapers)
JEL-codes: C32 C38 C55 E00 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2017-111
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