Abstract
In this paper, we analyze and compare the finite sample properties of alternative factor extraction procedures in the context of non-stationary Dynamic Factor Models (DFMs). On top of considering procedures already available in the literature, we extend the hybrid method based on the combination of principal components and Kalman filter and smoothing algorithms to non-stationary models. We show that if the idiosyncratic noises are stationary, procedures based on extracting the factors using the non-stationary original series work better than those based on differenced variables. We apply the methodology to the analysis of cross-border risk sharing by fitting non-stationary DFM to aggregate Gross Domestic Product and consumption of a set of 21 industrialized countries from the Organization for Economic Co-operation and Development (OECD). The goal is to check if international risk sharing is a short- or long-run issue.
Highlights
Dynamic Factor Models (DFMs) were first introduced in economics by Geweke (1977) and Sargent and Sims (1977) with the aim of extracting the underlying common factors in a system of time series
Bai and Ng (2004) carry out a Monte Carlo analysis to evaluate and compare the finite sample properties of implementing principal components (PC) procedures to data in levels or to their first differences and show that the non-stationary common factors can be properly recovered by both approaches when the idiosyncratic components are stationary
Barigozzi et al (2016, 2017) point out that stationarity of the idiosyncratic components would produce an amount of cointegration relations for the observed system that it is not consistent with that found in the systems that are standard in the DFMs literature as, for example, those of Stock and Watson (2002) and Forni et al (2009)
Summary
Dynamic Factor Models (DFMs) were first introduced in economics by Geweke (1977) and Sargent and Sims (1977) with the aim of extracting the underlying common factors in a system of time series. Bai and Ng (2004) carry out a Monte Carlo analysis to evaluate and compare the finite sample properties of implementing PC procedures to data in levels or to their first differences and show that the non-stationary common factors can be properly recovered by both approaches when the idiosyncratic components are stationary. The second contribution of this paper is an empirical application in which we extract common factors from a non-stationary system of aggregate output and consumption variables of a set of 21 industrialized countries of the Organization for Economic Co-operation and Development (OECD). The use of possible non-stationary DFMs allows to distinguish between longrun and short-run issues in consumption smoothing through international risk sharing.
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