SAS/IIF Grant to Promote Research on Forecasting
For the twelfth year, the IIF, in collaboration with SAS®, is proud to announce financial support for research on how to improve forecasting methods and business forecasting practice. The award for this year was (2) $5,000 grants. The recipients for the grant year 2014-2015 have been announced! See below for the winning research, click on 2014-2015.
Applications must include:
- Description of the project (max. 4 pages)
- Letter of support from the home institution where the researcher is based.
- Brief (max. 4 page) c.v.
- Budget and work-plan for the project.
For more information, click here. All applications or inquiries should be sent to .
The IIF-SAS grant was created in 2002 by the IIF, with financial support from the SAS® Institute, in order to promote research on forecasting principles and practice. The fund provided amounts of US $10,000 per year, which is divided to support research in the two basic aspects of forecasting: development of theoretical results and new methods, and practical applications with real-world comparisons.
Grant awarded to David Ardia, University Laval, Canada; Lennart F. Hoogerheide, Vrije Universiteit Amsterdam; Jeremy Kolly, University Laval, Canada and Fribourg University, Switzerland, for the project proposal in the category of Methodology “Bayesian Prediction of Market Risk using Regime-Switching GARCH Models.”
Grant awarded to Yongchen (Herbert) Zhao, University at Albany, USA, for the project proposal in the category of Methodology “Robust Real-Time Automated Forecast Combination in SAS: Development of a SAS Procedure and a Comprehensive Evaluation of Recently Developed Combination Methods.”
Grant awarded to Elena-Ivona Dumitrescu, Janine Christine Balter, and Peter Reinhard Hansen, European University Institute, Italy, for the project proposal in the category of Methodology “Forecasting Exchange Rate Volatility: Multivariate Realized GARCH Framework.”
Grant awarded to Yorghos Tripodis, Boston University, USA, for the project proposal in the category of Business Applications “Forecasting the Cognitive Status in an Aging Population.”
Grant awarded to Siddharth Arora and James Taylor, University of Oxford, UK, for the project proposal in the category of Business Applications “Short-term Load Forecasting Using Rule-based Seasonal Exponential Smoothing Incorporating Special Day Effects.”
Grant awarded to Juan Trapero, Universidad de Castilla-La Mancha, Spain; Nikolaos Kourentzes, Lancaster University, UK; and Diego Pedregal, Universidad de Castilla-La Mancha, Spain, for the project proposal in the category of Methodology “Minimizing the gap between judgmental and statistical forecasting in the presence of promotions.”
Grant awarded to Bryan Routledge and Noah Smith, Carnegie Mellon University, USA, for the project proposal in the category of Methodology “Text-Driven Forecasting of Mergers: Identifying Targets and Acquirers.“
Grant awarded to David Dickey and Melinda Thielbar, North Carolina State University, USA, for the project proposal in the category of Methodology “Neural Networks for Time Series Prediction: Practical Implications of Theoretical Results.”
Grant awarded to Gloria González-Rivera, Department of Economics, University of California – Riverside, for her project proposal “Evaluation of Multidimensional Predictive Densities.”
Grant awarded to Barbara Rossi, Duke University, “New Methods for Forecasting and Model Evaluation.”
Grant awarded to Pilar Poncela, Universidad Autónoma de Madrid, Spain, and Eva Senra, Universidad de Alcala, Spain “Combining Forecasts Through Factor Models: Assessing Consensus and Uncertainty.”
Grant awarded to Ting Yu, University of Technology at Sydney, Australia, “Incorporating Prior Domain Knowledge in Machine Learning.”
Grant awarded to Brajendra Sutradhar, Memorial University of Newfoundland, Canada, “Best Practice Recommendation for Forecasting Counts.”
Grant awarded to Min Qi, Kent State University, USA, and G. Peter Zhang, Georgia State University, USA, “Trend Time Series Modeling and Forecasting with Neural Networks.”