Efficient Neural Vector Autoregressive Models for Multivariate Time Series

Thomas M. Menino Convention & Exhibition Center 

Multivariate time series in economics and finance are often moderately to highly dimensional and may display nonlinear dynamics, limiting the effectiveness of classical vector autoregressive (VAR) models. We propose neural VAR models with built-in dimension reduction that first map the series into a low-dimensional dynamic subspace and then fit a VAR structure augmented with a feed-forward neural component, yielding parsimonious yet flexible representations. Simulation studies show that the proposed method achieves smaller estimation error and better forecast accuracy across a range of data-generating processes. An application to a system of macroeconomic indicators demonstrates that the model delivers more accurate forecasts and a more interpretable dependence structure than competing VAR models.

Dimension reduction

Neural networks

Multivariate time series 

Business and Economic Statistics Section