We analyze the properties of multiperiod forecasts which are formulated by a number of companies for a fixed horizon ahead which moves each month one period closer and are collected and diffused each month by some polling agency. Some descriptive evidence and a formal model suggest that knowing...

We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson. The second is based on...

Study attempted to estimate the predictive capacity of India's international trade of India's 170 trade partners for the period of 1991 to 2017. Predictive capacity of traditional and augmented equation measured with respect to pooled, fixed and random effect to measure the accuracy of the model...

The present paper develops three models that help predict the success rate and attainable investment levels of online crowdfunding ventures. This is done by applying standard economic theory and machine learning techniques from computer science to the novel sector of on-line crowd-based micro-...

This paper provides a flexible multi-factor framework to address some ongoing challenges in mortality modeling, with a special focus on the mortality curvature and possible mortality plateau for extremely old ages. We extend the Gompertz law Gompertz (1825) by proposing a multi-factor...

Mathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes....

We consider 1927 borrowers from 54 countries who had a credit rating by both Moody's and S&P at the end of 1998, and their subsequent default history up to the end of 2002. Viewing bond ratings as predicted probabilities of default, we consider partial orderings among competing probability...

This paper proposes a new way of displaying and analyzing macroeconomic time series to form recession forecasts. The proposed data displays contain the last three years of each expansion. These allow observers to see for themselves what is different about the last year before recession. Based on...

I extend traditional projection methods by using neural networks as a function approximator to solve continuous-time models. The method is well-suited for high-dimensional settings such as those that arise in the presence of many assets, and can capture the strong non-linearities that occur for...