Loss-based approach to two-piece location-scale distributions with applications to dependent data

TL;DR

This paper introduces an objective Bayesian method for estimating tail parameters in two-piece location-scale distributions, improving modeling of asymmetric data in time series and regression contexts, with applications to electricity price forecasting.

Contribution

It develops a novel Bayesian approach for tail parameter estimation in skewed distributions, enhancing predictive accuracy in dependent data models.

Findings

Improved density forecast accuracy in electricity markets

Effective Bayesian estimation for asymmetric distribution tails

Enhanced modeling of dependent data with skewed distributions

Abstract

Two-piece location-scale models are used for modeling data presenting departures from symmetry. In this paper, we propose an objective Bayesian methodology for the tail parameter of two particular distributions of the above family: the skewed exponential power distribution and the skewed generalised logistic distribution. We apply the proposed objective approach to time series models and linear regression models where the error terms follow the distributions object of study. The performance of the proposed approach is illustrated through simulation experiments and real data analysis. The methodology yields improvements in density forecasts, as shown by the analysis we carry out on the electricity prices in Nordpool markets.