Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks — Oak Academic Publishing
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Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks
Department of Economics, Management and Statistics, University of Milan-Bicocca, Milan, Italy; Center for Research on Pensions and Welfare Policies, Collegio Carlo Alberto, Moncalieri, Italy
1 Department of Economics, Management and Statistics, University of Milan-Bicocca, Milan, Italy; Center for Research on Pensions and Welfare Policies, Collegio Carlo Alberto, Moncalieri, Italy
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.
KeywordsLong and Short MemoryStructural BreaksCommon FactorsPrincipal Components AnalysisFractionally Integrated Heteroskedastic Factor Vector Autoregressive Model
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