Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks

Posted: 7 Feb 2011 Last revised: 28 May 2014

See all articles by Claudio Morana

Claudio Morana

Università di Milano Bicocca; Università degli Studi di Milano-Bicocca - Department of Economics, Management and Statistics (DEMS); Università degli Studi di Milano-Bicocca - Center for European Studies (CefES); Center for Economic Research on Pensions and Welfare Policies (CeRP); University of Bologna - Rimini Center for Economic Analysis (RCEA)

Date Written: May 28, 2014

Abstract

In the paper a general framework for large scale modeling of macroeconomic and financial time series is introduced. The proposed approach is characterized by simplicity of implementation, performing well independently of persistence and heteroskedasticity properties, accounting for common deterministic and stochastic factors. Monte Carlo results strongly support the proposed methodology, validating its use also for relatively small cross-sectional and temporal samples.

Keywords: long and short memory, structural breaks, common factors, principal components analysis, fractionally integrated heteroskedastic factor vector autoregressive model

JEL Classification: C22

Suggested Citation

Morana, Claudio, Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks (May 28, 2014). Available at SSRN: https://ssrn.com/abstract=1756376 or http://dx.doi.org/10.2139/ssrn.1756376

Claudio Morana (Contact Author)

Università di Milano Bicocca ( email )

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Università degli Studi di Milano-Bicocca - Department of Economics, Management and Statistics (DEMS) ( email )

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Università degli Studi di Milano-Bicocca - Center for European Studies (CefES) ( email )

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Center for Economic Research on Pensions and Welfare Policies (CeRP) ( email )

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