Printed from https://ideas.repec.org/n/nep-for/2021-06-14.html
Report NEP-FOR-2021-06-14
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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-FOR
The following items were announced in this report:
- Le Ha Thu & Roberto Leon-Gonzalez, 2021, "Forecasting Macroeconomic Variables in Emerging Economies: An Application to Vietnam," GRIPS Discussion Papers, National Graduate Institute for Policy Studies, number 21-03, Jun.
- Jonas E. Arias & Juan F. Rubio-Ramirez & Minchul Shin, 2021, "Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models," Working Papers, Federal Reserve Bank of Philadelphia, number 21-21, Jun, DOI: 10.21799/frbp.wp.2021.21.
- Nikoleta Anesti & Eleni Kalamara & George Kapetanios, 2021, "Forecasting UK GDP growth with large survey panels," Bank of England working papers, Bank of England, number 923, May.
- James Mitchell & Martin Weale, 2021, "Censored Density Forecasts: Production and Evaluation," Working Papers, Federal Reserve Bank of Cleveland, number 21-12R, May, revised 16 Aug 2022, DOI: 10.26509/frbc-wp-202112r.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021, "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Bank of Israel Working Papers, Bank of Israel, number 2021.06, Mar.
- Ewa Batyra & Tiziana Leone & Mikko Myrskylä, 2021, "Forecasting of cohort fertility by educational level in countries with limited data availability: the case of Brazil," MPIDR Working Papers, Max Planck Institute for Demographic Research, Rostock, Germany, number WP-2021-011, DOI: 10.4054/MPIDR-WP-2021-011.
- Marcelo A. T. Aragão, 2021, "Blurred Crystal Ball: investigating the forecasting challenges after a great exogenous shock," Working Papers Series, Central Bank of Brazil, Research Department, number 549, May.
- Mateusz Buczyński & Marcin Chlebus, 2021, "GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-08.