Board of Governors of the Federal Reserve System (U.S.)
Finance and Economics Discussion Series
Common Factors, Trends, and Cycles in Large Datasets
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.
Cite this item
Matteo Barigozzi & Matteo Luciani, Common Factors, Trends, and Cycles in Large Datasets, Board of Governors of the Federal Reserve System (U.S.), Finance and Economics Discussion Series 2017-111, 13 Nov 2017.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- E00 - Macroeconomics and Monetary Economics - - General - - - General
Keywords: EM Algorithm ; Gross Domestic Output ; Kalman Smoother ; Non-stationary Approximate Dynamic Factor Model ; Output Gap ; Quasi Maximum Likelihood ; Trend-Cycle Decomposition
This item with handle RePEc:fip:fedgfe:2017-111
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