Report NEP-FOR-2022-03-07

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:

• Afees A. Salisu & Rangan Gupta & Elie Bouri, 2022. "Testing the Forecasting Power of Global Economic Conditions for the Volatility of International REITs using a GARCH-MIDAS Approach," Working Papers 202211, University of Pretoria, Department of Economics.
• Alexander Gleim & Nazarii Salish, 2022. "Forecasting Environmental Data: An example to ground-level ozone concentration surfaces," Papers 2202.03332, arXiv.org.
• Carmina Fjellstrom, 2022. "Long Short-Term Memory Neural Network for Financial Time Series," Papers 2201.08218, arXiv.org.
• Davydenko, Andrey & Goodwin, Paul, 2021. "Assessing Point Forecast Bias Across Multiple Time Series: Measures and Visual Tools," SocArXiv jhtzw, Center for Open Science.
• Matthew F. Tomlinson & David Greenwood & Marcin Mucha-Kruczynski, 2022. "2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log-returns: out-of-sample comparison of conditional EVT models," Papers 2202.01043, arXiv.org, revised Oct 2022.
• Effat Ara Easmin Lucky & Md. Mahadi Hasan Sany & Mumenunnesa Keya & Md. Moshiur Rahaman & Umme Habiba Happy & Sharun Akter Khushbu & Md. Arid Hasan, 2022. "Simulating Using Deep Learning The World Trade Forecasting of Export-Import Exchange Rate Convergence Factor During COVID-19," Papers 2201.12291, arXiv.org.
• Ina Hajdini, 2022. "Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model," Working Papers 22-03R, Federal Reserve Bank of Cleveland, revised 06 Mar 2023.
• Taylan Kabbani & Fatih Enes Usta, 2022. "Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark," Papers 2201.12283, arXiv.org.
• Modis, Theodore, 2022. "Strengths and weaknesses of the logistic function used in forecasting," OSF Preprints mrwu3, Center for Open Science.
• Hwai-Chung Ho, 2022. "Forecasting the distribution of long-horizon returns with time-varying volatility," Papers 2201.07457, arXiv.org.
• Thackway, William & Ng, Matthew Kok Ming & Lee, Chyi Lin & Pettit, Christopher, 2021. "Building a predictive machine learning model of gentrification in Sydney," SocArXiv hkc96, Center for Open Science.