Testing the Value of Probability Forecasts for Calibrated Combining
Abstract
We combine the probability forecasts of a real GDP decline from the U.S. Survey of Professional Forecasters, after trimming the forecasts that do not have "value", as measured by the Kuiper Skill Score and in the sense of Merton (1981). For this purpose, we use a simple test to evaluate the probability forecasts. The proposed test does not require the probabilities to be converted to binary forecasts before testing, and it accommodates serial correlation and skewness in the forecasts. We find that the number of forecasters making valuable forecasts decreases sharply as the horizon increases. The beta-transformed linear pool combination scheme, based on the valuable individual forecasts, is shown to outperform the simple average for all horizons on a number of performance measures, including calibration and sharpness. The test helps to identify the good forecasters ex ante, and therefore contributes to the accuracy of the combined forecasts.
Read the paper
Related studies
- Combining in silico and in vitro approaches to identification of potent inhibitor against phospholipase A2 (PLA2)
- In silico and in vitro studies: investigating the chemical composition, DFT, molecular docking, and dynamic simulation of Satureja candidissima (Munby) Briq essential oil as a potential antibacterial agent
- Quercetin combined with ciprofloxacin and gentamicin inhibits biofilm formation and virulence in Staphylococcus aureus
- In silico study of the mechanism of action, pharmacokinetic and toxicological properties of some N-methylanthranilates and their analogs
- Anticonvulsant Effects of Citronellal, Daidzin, and Phytol, Possibly Through GABAA Receptor and Voltage-Gated Sodium Channel Interaction Pathways: In Vivo and In Silico Studies
- 2-Phenylcyclopropylmethylamine (PCPMA) Derivatives as D3R-Selective Ligands for 3D-QSAR, Docking and Molecular Dynamics Simulation Studies
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.