Horseshoe Priors for Time-Varying AR and GARCH Processes

TL;DR

This paper introduces a Bayesian model with horseshoe priors for analyzing time-varying AR and GARCH processes, aiming to improve understanding of yield stability in grassland ecosystems under environmental stress.

Contribution

It develops a novel Bayesian framework incorporating horseshoe priors for time-varying AR and GARCH models, applied to ecological yield stability data.

Findings

Water stress and warmer temperatures reduce yield stability.

The model captures temporal changes in yield variability.

Results suggest environmental factors significantly impact grassland stability.

Abstract

Grassland ecosystems support a wide range of species and provide key services including food production, carbon storage, biodiversity support, and flood mitigation. However, yield stability in these grassland systems is not yet well understood, with recent evidence suggesting water stress throughout summer and warmer temperatures in late summer reduce yield stability. In this study we investigate how grassland yield stability of the Park Grass Experiment, UK, has changed over time by developing a Bayesian time-varying Autoregressive and time-varying Generalised Autoregressive Conditional Heterogeneity model using the variance-parameterised Gamma likelihood function.