Prediction markets - markets used to forecast future events - have been used to accurately forecast the outcome of political contests, sporting events, and, occasionally, economic outcomes. This chapter summarizes the latest research on prediction markets in order to further their utilization by...

Quarterly GDP figures usually are published with a delay of some weeks. A common way to generate GDP series of higher frequency, i.e. to nowcast GDP, is to use available indicators to calculate a single index by means of a common factor derived from a dynamic factor model (DFM). This paper deals...

This paper documents the existence of Political Forecast Cycles. In a theoretical model of political selection, we show that governments release overly optimistic GDP growth forecasts ahead of elections to increase the reelection probability. The bias arises from lack of commitment if voters are...

We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of...

This paper argues that probability forecasts convey information on the uncertainties that surround macroeconomic forecasts in a manner which is straightforward and which is preferable to other alternatives, including the use of confidence intervals. Probability forecasts relating to UK output...

We apply generalized beta and triangular distributions to histograms from the Survey of Professional Forecasters (SPF) to estimate forecast uncertainty, shocks and discord using information framework, and compare these with moment-based estimates. We find these two approaches to produce...

This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying...

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique weekly and monthly electricity consumption data for the second-largest German state, the Free State of Bavaria, we conduct a pseudo out-of-sample forecasting experiment for...

Using neural networks, the present study replicates previous results on the prediction of student dropout obtained with decision trees and logistic regressions. For this purpose, multilayer perceptrons are trained on the same data as in the initial study. It is shown that neural networks lead to...

We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure...