|
on Forecasting |
| By: | Tony Chernis; Niko Hauzenberger; Florian Huber; Gary Koop; James Mitchell |
| Abstract: | Bayesian predictive synthesis (BPS) is a method of combining predictive distributions based on agent opinion analysis theory, which encompasses many common approaches to combining density forecasts. The key ingredient in BPS is a synthesis function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area’s Survey of Professional Forecasters. The second combines density forecasts of US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits—in terms of improved forecast accuracy and interpretability—of modeling the synthesis function nonparametrically. |
| Keywords: | Econometric and Statistical Methods |
| JEL: | C11 C32 C53 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:23-61&r=for |
| By: | Niklas Valentin Lehmann |
| Abstract: | Open online crowd-prediction platforms are increasingly used to forecast trends and complex events. Despite the large body of research on crowd-prediction and forecasting tournaments, online crowd-prediction platforms have never been directly compared to other forecasting methods. In this analysis, exchange rate crowd-predictions made on Metaculus are compared to predictions made by the random-walk, a statistical model considered extremely hard-to-beat. The random-walk provides less erroneous forecasts, but the crowd-prediction does very well. By using the random-walk as a benchmark, this analysis provides a rare glimpse into the forecasting skill displayed on open online crowd-prediction platforms. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.09081&r=for |
| By: | Gianluca Cubadda (CEIS & DEF, University of Rome "Tor Vergata"); Stefano Grassi (DEF, University of Rome "Tor Vergata"); Barbara Guardabascio (University of Perugia) |
| Abstract: | Many economic variables are characterized by changes in their conditional mean and volatility, and time-varying Vector Autoregressive Models are often used to handle such complexity. Unfortunately, as the number of series grows, they present increasing estimation and interpretation issues. This paper tries to address this problem by proposing a Multivariate Autoregressive Index model that features time-varying mean and volatility. Technically, we develop a new estimation methodology that mixes switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows one to select or weigh the number of common components, and other data features, in real-time without additional computational costs. Using US macroeconomic data, we provide a forecast exercise that shows the feasibility and usefulness of this model. |
| Keywords: | Large Vector Autoregressive Models, Multivariate Autoregressive Index Models, Time-Varying Parameter Models, Bayesian Vector Autoregressive Models. |
| Date: | 2024–01–10 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:571&r=for |