Early Warning with Calibrated and Sharper Probabilistic Forecasts

TL;DR

This paper explores how combining calibrated probabilistic forecasts with unconditional densities improves early warning systems across disciplines like economics and meteorology, using UK inflation forecasts as a case study.

Contribution

It introduces a method for enhancing density forecasts by mixing them with unconditional densities to counteract model mis-specification effects.

Findings

Combining forecasts improves density accuracy.

Enhanced early warning capabilities across disciplines.

Application demonstrated on UK inflation data.

Abstract

Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In order to enhance the quality of the density forecasts, one can mix them with the unconditional density. This paper examines the value of combining conditional density forecasts with the unconditional density. The findings have positive implications for issuing early warnings in different disciplines including economics and meteorology, but UK inflation forecasts are considered as an example.