Inducing sparsity and shrinkage in time-varying parameter models

Arbeitspapier

Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to remove this uncertainty and improve forecasts. In this paper, we develop computationally simple methods which both shrink and sparsify TVP models. In a simulated data exercise we show the benefits of our shrink-then-sparsify approach in a variety of sparse and dense TVP regressions. In a macroeconomic forecast exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Papers in Economics ; No. 2019-02

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Multiple or Simultaneous Equation Models; Multiple Variables: General ; Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data) ; Personal Income, Wealth, and Their Distributions
• Subject: Sparsity ; shrinkage ; hierarchical priors ; time varying parameter regression

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Huber, Florian ; Koop, Gary ; Onorante, Luca
• Event: Veröffentlichung
• (who): University of Salzburg, Department of Social Sciences and Economics
• (where): Salzburg
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/201679
• Last update: 10.03.2025, 11:44 AM CET