in time through the posterior probability. Our findings show that (1) DMA improves forecasts compared to other frameworks … posteriori-Wahrscheinlichkeit, welche Prädiktoren zu verschiedenen Zeitpunkten für die Prognose relevant waren. Unsere Ergebnisse … zeigen, dass (1) DMA die Prognose im Vergleich zu anderen Verfahren verbessert und (2) dass die Goldpreisprädiktoren sich …

forecast levels. A country's proximity to the US, importance to the firm, and visibility, as well as availability of more … precise information about foreign country exposures, contribute to consensus forecast efficiency. We identify a dimension of … — and show that it contributes to forecast efficiency, accuracy, and informativeness and that it helps the analyst achieve …

election outcomes. Prediction market prices have been shown to be consistently accurate forecasts of these outcomes, but we don … incentives to acquire information about the outcome, thus tend to have better forecasts. Moreover, their trades have larger …

machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs … that are available to all investors. We measure disagreement as dispersion in forecasts across investor-models. Our measure … aligns with extant measures of disagreement (e.g., analyst forecast dispersion), but is a significantly stronger predictor of …

standard GARCH specification, the non-linear models generally lead to better forecasts in terms of both smaller forecast errors … price indexes. Forecasts produced by each non-linear GARCH model and each index are evaluated using a common set of … classical criteria, as well as forecast combination techniques with constant and non-constant weights. With respect to the …