Report NEP-FOR-2017-05-21

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:

• Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
• Gilles Mourre & Caterina Astarita & Anamaria Maftei, 2016. "Measuring the Uncertainty in Predicting Public Revenue," European Economy - Discussion Papers 039, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
• Asai, M. & McAleer, M.J., 2017. "Forecasting the Volatility of Nikkei 225 Futures," Econometric Institute Research Papers TI 2017-017/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Jorge A Chan-Lau, 2017. "Lasso Regressions and Forecasting Models in Applied Stress Testing," IMF Working Papers 17/108, International Monetary Fund.
• MIYAKAWA Daisuke & MIYAUCHI Yuhei & Christian PEREZ, 2017. "Forecasting Firm Performance with Machine Learning: Evidence from Japanese firm-level data," Discussion papers 17068, Research Institute of Economy, Trade and Industry (RIETI).