Estimating non-stationary common factors: Implications for risk sharing
Francisco Corona () and
Esther Ruiz Ortega
DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística
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, unless the idiosyncratic noise is non-stationary, procedures based on extracting the factors using the nonstationary original series work better than those based on differenced variables. The results are illustrated in an empirical application fitting non-stationary DFM to aggregate GDP and consumption of the set of 21 OECD industrialized countries. The goal is to check international risk sharing is a short or long-run issue.
Keywords: Consumption; smoothing; Long-run/Short-run; estimation; Non-stationary; Dynamic; Factor; Models; Kalman; filter; Principal; components; Resilience; Risk; sharing (search for similar items in EconPapers)
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Journal Article: Estimating Non-stationary Common Factors: Implications for Risk Sharing (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:24585
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