Efficient Dimension Reduction for Multivariate Time Series Using Partial Envelopes

Music City Center 

Overparameterization poses a significant challenge for standard vector autoregressive (VAR) models, particularly in high-dimensional time series, as it restricts the number of variables and lags that can be effectively incorporated. To address this, we introduce partial envelope models designed for efficient dimension reduction in multivariate time series. Our approach provides a parsimonious framework by selectively focusing on key lag variables, leading to substantial efficiency gains in coefficient estimation compared to standard VAR models. By concentrating on a subset of relevant lags, our models enhance estimation efficiency while maintaining predictive accuracy. We demonstrate these efficiency improvements through simulated experiments and a real-data analysis, highlighting the advantages of our proposed partial envelope methodology.

VAR

Dimension reduction

Envelopes 

Business and Economic Statistics Section