This paper leverages the interface Google Insights for Search to 'nowcast' Belgian macro-economic indicators such as retail consumer spending and unemployment. Using a general ECM framework, the analysis demonstrates that economic fluctuations correlate with Google search intensity. In...

The present work attempts to evaluate the advantages inherent to the use of exogenous variables highly correlated to the electric load, for the forecast of future demand. Here we utilize time series models of the auto-regressive moving average types incorporating seasonal treatment and exogenous...

This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident...

Predicting the performance of planned organ transplantation has proved to be a critical problem to solve. The purpose of this study is to present a data mining-based model for variable filtering and selection in order to predict the performance of thoracic transplantation via the graft...

We investigate predictive abilities of nonlinear models for stock returns when density forecasts are evaluated and compared instead of the conditional mean point forecasts. The aim of this paper is to show whether the in-sample evidence of strong nonlinearity in mean may be exploited for...

The Conference Board's Leading Economic Indicators Index suffers from construction flaws, which reduce its predictive power as well as one's ability to interpret its signals. This paper develops a vector autoregression model to address these problems. The model's out-of-sample GDP forecasts,...

In previous studies, high-frequency data has been used to improve portfolio allocation by estimating the full realized covariance matrix. In this paper, we show that strategies using high-frequency data for measuring and forecasting univariate realized volatility alone can already generate...

This paper proposes a predictive maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for...

We analyze the role of hedge fund, swap dealer and arbitrageur activity in a Markov regime-switching model between high volatility bear markets and low volatility bull markets for crude oil, corn and Mini-S&P500 index futures. We find that these institutional positions reflect fundamental...