Publication: Estimating non-stationary common factors : implications for risk sharing
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2020-01-01
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Springer Nature
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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.
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Consumption smoothing, Kalman filter, Non-stationary dynamic factor models, Principal components, Risk sharing
Bibliographic citation
Corona, F., Poncela, P., & Ruiz, E. (2020). Estimating Non-stationary Common Factors: Implications for Risk Sharing. Computational Economics, 55(1), 37-60.