TITLE:
Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks
AUTHORS:
Claudio Morana
KEYWORDS:
Long and Short Memory, Structural Breaks, Common Factors, Principal Components Analysis, Fractionally Integrated Heteroskedastic Factor Vector Autoregressive Model
JOURNAL NAME:
Open Journal of Statistics,
Vol.4 No.4,
June
20,
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.