Upsampled Vector Autoregressive Processes

Emilio Ruiz-Moreno, Luis Miguel Lopez-Ramos, Baltasar Beferull-Lozano

Abstract: Vector autoregressive (VAR) processes are simple yet remarkably versatile discrete statistical models used to characterize the dynamics of a collection of variables. Although implicitly, any given VAR process assumes the highest rate, or resolution, at which those variables may vary. Hence, dynamic variations at finer rates are out of the explainability of such models by design. This paper proposes a new method to overcome this drawback. Specifically, it describes how to increase the resolution associated with any VAR process such that the process mean is unchanged and the forecast estimates at the rates given by the original resolution, and the long-run associated credible intervals, are closely preserved. Our experiments confirm the viability of the proposed method for multivariate time series analysis.