Report NEP-FOR-2019-04-08

This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Rob J Hyndman issued this report. It is usually issued weekly.

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Other reports in NEP-FOR

The following items were announced in this report:

• Tingting Cheng & Jiti Gao & Oliver Linton, 2019. "Nonparametric Predictive Regressions for Stock Return Prediction," Monash Econometrics and Business Statistics Working Papers 4/19, Monash University, Department of Econometrics and Business Statistics.
• Novy Ann M. Etac & Roel F. Ceballos, 2019. "Forecasting the Volatilities of Philippine Stock Exchange Composite Index Using the Generalized Autoregressive Conditional Heteroskedasticity Modeling," Papers 1904.00749, arXiv.org.
• Skreta, Vasiliki & Giacomini, Raffaella & Gaglianone, Wagner & Issler, Joao, 2019. "Incentive-driven Inattention," CEPR Discussion Papers 13619, C.E.P.R. Discussion Papers.
• Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
• Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2019. "Higher Moment Constraints for Predictive Density Combinations," Working Papers BAWP-2019-01, University of Sydney Business School, Discipline of Business Analytics.
• Fokin, Nikita (Фокин, Никита), 2019. "VAR-LASSO model for the Russian economy on a large data set [Var-Lasso Модель Для Российской Экономики На Большом Массиве Данных]," Working Papers 031951, Russian Presidential Academy of National Economy and Public Administration.
• Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Working Papers 201925, University of Pretoria, Department of Economics.
• Chan, Felix & Pauwels, Laurent, 2019. "Equivalence of optimal forecast combinations under affine constraints," Working Papers BAWP-2019-02, University of Sydney Business School, Discipline of Business Analytics.