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on Forecasting |
| By: | Bas Scheer (CPB Netherlands Bureau for Economic Policy Analysis) |
| Abstract: | In this paper we show that the prediction errors for unemployment vary over the business cycle. We initially use a macroeconomic data for the United States, because these data are available for a longer period than for the Netherlands. The dataset for the United States covers the 60s of the last century up to the COVID-crisis. We find that forecast errors are greatest in recession periods, but they are also relatively large in recovery periods. The forecasting errors are smaller in the periods between. All forecasting models show this error pattern, but interestingly, they don't all show it to the same extent. Some models perform relatively well during recessions, others during recovery periods, and still others during the periods in between. As a result, the choice of the best model depends on the weight that is assigned to prediction errors in recovery and recession periods. These findings are relevant to the forecasting models used by the CPB that support the bureau-wide unemployment forecast. We carried out the same analysis on Dutch data, which provided comparable results. For the Dutch unemployment estimate, too, the choice of the best model depends on the weights for recovery and recession periods. |
| JEL: | L26 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpb:discus:434&r= |
| By: | Augustin TAPSOBA |
| Abstract: | Being able to assess conflict risk at local level is crucial for preventing political violence or mitigating its consequences. This paper develops a new approach for predicting the timing and location of conflict events from violence history data. It adapts the methodology developed in Tapsoba (2018) for measuring violence risk across space and time to conflict prediction. Violence is modeled as a stochastic process with an unknown underlying distribution. Each conflict event observed on the ground is interpreted as a random realization of this process and its underlying distribution is estimated using kernel density estimation methods in a three-dimensional space. The optimal smoothing parameters are estimated to maximize the likelihood of future conflict events. An illustration of the practical gains (in terms of out-of-sample forecasting performance) of this new methodology compared to standard space-time autoregressive models is shown using data from Côte d’Ivoire. |
| Keywords: | Côte d'Ivoire |
| JEL: | Q |
| Date: | 2022–03–17 |
| URL: | http://d.repec.org/n?u=RePEc:avg:wpaper:en13778&r= |
| By: | Arthur Thomas (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - IUML - FR 3473 Institut universitaire Mer et Littoral - UBS - Université de Bretagne Sud - UM - Le Mans Université - UA - Université d'Angers - CNRS - Centre National de la Recherche Scientifique - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer - UN - Université de Nantes - ECN - École Centrale de Nantes - IEMN-IAE Nantes - Institut d'Économie et de Management de Nantes - Institut d'Administration des Entreprises - Nantes - UN - Université de Nantes); Olivier Massol (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School, City University of London); Benoît Sévi (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - IUML - FR 3473 Institut universitaire Mer et Littoral - UBS - Université de Bretagne Sud - UM - Le Mans Université - UA - Université d'Angers - CNRS - Centre National de la Recherche Scientifique - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer - UN - Université de Nantes - ECN - École Centrale de Nantes - IEMN-IAE Nantes - Institut d'Économie et de Management de Nantes - Institut d'Administration des Entreprises - Nantes - UN - Université de Nantes) |
| Abstract: | The purpose of this paper is to investigate whether the next day's consumption of natural gas can be accurately forecast using a simple model that solely incorporates the information contained in dayahead market data. Hence, unlike standard models that use a number of meteorological variables, we only consider two predictors: the price of natural gas and the spark ratio measuring the relative price of electricity to gas. We develop a suitable modeling approach that captures the essential features of daily gas consumption and in particular the nonlinearities resulting from power dispatching. We use the case of France as an application as this is, as far as is known, the very first attempt to model and predict the country's daily gas demand. Our results document the existence of a long-run relation between demand and spot prices and provide estimates of the own- and cross-price elasticities. We also provide evidence of the pivotal role of the spark ratio which is found to have an asymmetric and highly nonlinear impact on demand variations. Lastly, we show that our simple model is sufficient to generate predictions that are considerably more accurate than the forecasts published by infrastructure operators. |
| Keywords: | Natural Gas Markets,Day-Ahead Prices,Demand Price Elasticity,Load Forecasting |
| Date: | 2022–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03521140&r= |
| By: | Fotios Petropoulos; Daniele Apiletti; Vassilios Assimakopoulos; Mohamed Zied Babai; Devon K. Barrow; Souhaib Ben Taieb; Christoph Bergmeir; Ricardo J. Bessa; Jakub Bijak; John E. Boylan; Jethro Browell; Claudio Carnevale; Jennifer L. Castle; Pasquale Cirillo; Michael P. Clements; Clara Cordeiro; Fernando Luiz Cyrino Oliveira; Shari De Baets; Alexander Dokumentov; Joanne Ellison; Piotr Fiszeder; Philip Hans Franses; David T. Frazier; Michael Gilliland; M. Sinan G\"on\"ul; Paul Goodwin; Luigi Grossi; Yael Grushka-Cockayne; Mariangela Guidolin; Massimo Guidolin; Ulrich Gunter; Xiaojia Guo; Renato Guseo; Nigel Harvey; David F. Hendry; Ross Hollyman; Tim Januschowski; Jooyoung Jeon; Victor Richmond R. Jose; Yanfei Kang; Anne B. Koehler; Stephan Kolassa; Nikolaos Kourentzes; Sonia Leva; Feng Li; Konstantia Litsiou; Spyros Makridakis; Gael M. Martin; Andrew B. Martinez; Sheik Meeran; Theodore Modis; Konstantinos Nikolopoulos; Dilek \"Onkal; Alessia Paccagnini; Anastasios Panagiotelis; Ioannis Panapakidis; Jose M. Pav\'ia; Manuela Pedio; Diego J. Pedregal; Pierre Pinson; Patr\'icia Ramos; David E. Rapach; J. James Reade; Bahman Rostami-Tabar; Micha{\l} Rubaszek; Georgios Sermpinis; Han Lin Shang; Evangelos Spiliotis; Aris A. Syntetos; Priyanga Dilini Talagala; Thiyanga S. Talagala; Len Tashman; Dimitrios Thomakos; Thordis Thorarinsdottir; Ezio Todini; Juan Ram\'on Trapero Arenas; Xiaoqian Wang; Robert L. Winkler; Alisa Yusupova; Florian Ziel |
| Abstract: | Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.03854&r= |