Bayesian group-shrinkage based estimation for panel vector autoregressive models with mixed frequency data
Nilanjana Chakraborty, Kshitij Khare, George Michailidis
The Annals of Applied Statistics · 2025 · Volume 19 · Issue 4
Abstract
Panel vector autoregressive (VAR) models are effective tools for capturing temporal relationships between a set of variables (e.g., macroeconomic indicators of an economy) while accounting for interdependencies between a set of entities (e.g., market sectors or whole economies). For modeling macroeconomic data, this challenge is often further accentuated by the presence of variables observed at different frequencies. Existing Bayesian approaches that link entity-specific VAR models often impose strict fusion of VAR coefficients to a common value across entities. This paper develops a balanced and less stringent Bayesian approach for mixed frequency panel VAR models that employ group shrinkage prior distributions to borrow strength across entities while allowing for entity-specific idiosyncrasies. A key novel feature is the ability to incorporate and learn the interdependence structure between entities through an interentity covariance (matrix) parameter. The proposed methodology is evaluated both on synthetic data and on two economic applications: employment indices across neighboring U.S. states and macroeconomic indicators of tightly integrated European economies. Finally, we establish the theoretical properties of the proposed approach.
Keywords
Bayesian estimation; group shrinkage prior distributions; high dimensional; mixed frequency data; Panel VAR;
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