Factor Extraction In Dynamic Factor Models
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Factor Extraction in Dynamic Factor Models
Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components surveys the literature on factor extraction in the context of Dynamic Factor Models (DFMs) fitted to multivariate systems of economic and financial variables. Many of the most popular factor extraction procedures often used in empirical applications are based on either Principal Components (PC) or Kalman filter and smoothing (KFS) techniques. First, the authors show that the KFS factors are a weighted average of the contemporaneous information (PC factors) and the past information and that the weights of the latter are negligible unless the factors are closed to the non-stationarity boundary and/or their loadings are pretty small when compared with the variance-covariance matrix of the idiosyncratic components. Second, the authors survey how PC and KFS deal with several issues often faced in the context of extracting factors from real data systems. In particular, they describe PC and KFS procedures to deal with mixed frequencies and missing observations, structural breaks, non-stationarity, Markov-switching parameters or multi-level factor structures. In general, KFS is very flexible to deal with these issues.
Factor Extraction in Dynamic Factor Models
Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components surveys the literature on factor extraction in the context of Dynamic Factor Models (DFMs) fitted to multivariate systems of economic and financial variables. Many of the most popular factor extraction procedures often used in empirical applications are based on either Principal Components (PC) or Kalman filter and smoothing (KFS) techniques. First, the authors show that the KFS factors are a weighted average of the contemporaneous information (PC factors) and the past information and that the weights of the latter are negligible unless the factors are closed to the non-stationarity boundary and/or their loadings are pretty small when compared with the variance-covariance matrix of the idiosyncratic components. Second, the authors survey how PC and KFS deal with several issues often faced in the context of extracting factors from real data systems. In particular, they describe PC and KFS procedures to deal with mixed frequencies and missing observations, structural breaks, non-stationarity, Markov-switching parameters or multi-level factor structures. In general, KFS is very flexible to deal with these issues.
The Stacked Leading Indicators Dynamic Factor Model
The paper introduces an approximate dynamic factor model based on the extraction of principal components from a very large number of leading indicators stacked at various lags. The model is designed to produce short-term forecasts that are computed with the EM algorithm implemented with the first few eigenvectors ordered by descending eigenvalues. A cross-sectional bootstrap experiment is used to shed light on the sensitivity of the factor model to factor selection and to sampling uncertainty. The empirical number of factors seems more appropriately set through an analysis of eigenvalues, bootstrapped eigenvalues or the BIC than with more sophisticated information criteria. Confidence intervals derived from bootstrapped forecasts show the extent to which the data composition can support the hypothesis of business cycle co-movements and the selected factors can account for those shocks. Pseudo real-time out-of-sample forecast experiments conducted with a dataset of about two thousand series covering the euro area business cycle show that the SLID factor model outperforms benchmark models (AR models, leading indicators equations) for one-, two- and three- quarters-ahead forecasts of GDP growth. The accuracy of coincident forecasts compared to final estimates is not significantly different from Eurostat Flash or first estimates and is slightly superior to that of CEPR Eurocoin.