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

• 作者: Claudio Morana
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Long and SHORT Memory Structural BREAKS Common Factors Principal Components Analysis Fractionally Integrated Heteroskedastic Factor Vector AUTOREGRESSIVE Model
• 年，卷（期）: 2014,（4）
• 页码范围: 292-312
• 页数: 21页
• 分类号: R73

摘要

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.