nep-for New Economics Papers
on Forecasting
Issue of 2020‒10‒12
twelve papers chosen by
Rob J Hyndman
Monash University

  1. Optimal probabilistic forecasts: When do they work? By Ruben Loaiza-Maya; Gael M. Martin; David T. Frazier; Worapree Maneesoonthorn; Andres Ramirez Hassan
  2. The New Benchmark for Forecasts of the Real Price of Crude Oil By Amor Aniss Benmoussa; Reinhard Ellwanger; Stephen Snudden
  3. Investors' Uncertainty and Forecasting Stock Market Volatility By Ruipeng Liu; Rangan Gupta
  4. Development of a methodology for medium-term forecasting of the socio-economic development of the Russian Federation in the territorial context By Grishina, Irina (Гришина, Ирина); Polynev, Andrey (Полынев, Андрей); Shkuropat, Anna (Шкуропат, Анна); Kotov, Alexander (Котов, Александр)
  5. Real Time Forecasting of Covid-19 Intensive Care Units demand By Berta, Paolo; Lovaglio, Pietro Giorgio; Paruolo, Paolo; Verzillo, Stefano
  6. Forecasting the Old-Age Dependency Ratio to Determine a Sustainable Pension Age By Rob J Hyndman; Yijun Zeng; Han Lin Shang
  7. Modeling and Forecasting Economic Growth in Sub-Saharan Africa in the Post-Covid Era By Van, Germinal G.
  8. Forecasting impacts of Agricultural Production on Global Maize Price By Rotem Zelingher; David Makowski; Thierry Brunelle
  9. Forecasting recovery rates on non-performing loans with machine learning By Bellotti, Anthony; Brigo, Damiano; Gambetti, Paolo; Vrins, Frédéric
  10. Forecasting for Social Good By Bahman Rostami-Tabar; Mohammad M Ali; Tao Hong; Rob J Hyndman; Michael D Porter; Aris Syntetos
  11. Scenario-Based Forecast for Post-Conflict’s Growth in Syria By Mouyad Alsamara; Zouhair Mrabet; Ahmad Shikh Ebid
  12. The COVID-19 Shock and Equity Shortfall: Firm-level Evidence from Italy By Elena Carletti; Tommaso Oliviero; Marco Pagano; Loriana Pelizzon

  1. By: Ruben Loaiza-Maya; Gael M. Martin; David T. Frazier; Worapree Maneesoonthorn; Andres Ramirez Hassan
    Abstract: Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Herein, we reinvestigate the practice of using proper scoring rules to produce probabilistic forecasts that are 'optimal' according to a given score, and assess when their out-of-sample accuracy is superior to alternative forecasts, according to that score. Particular attention is paid to relative predictive performance under misspecification of the predictive model. Using numerical illustrations, we document several novel findings within this paradigm that highlight the important interplay between the true data generating process, the assumed predictive model and the scoring rule. Notably, we show that only when a predictive model is sufficiently compatible with the true process to allow a particular score criterion to reward what it is designed to reward, will this approach to forecasting reap benefits. Subject to this compatibility however, the superiority of the optimal forecast will be greater, the greater is the degree of misspecification. We explore these issues under a range of different scenarios, and using both artificially simulated and empirical data.
    Keywords: coherent predictions, linear predictive pools, predictive distributions, proper scoring rules, stochastic volatility with jumps, testing equal predictive ability
    JEL: C18 C53 C58
    Date: 2020
  2. By: Amor Aniss Benmoussa; Reinhard Ellwanger; Stephen Snudden
    Abstract: We propose a new no-change benchmark to evaluate forecasts of series that are temporally aggregated. The new benchmark is the last high-frequency observation and reflects the null hypothesis that the underlying series, rather than the aggregated series, is unpredictable. Under the random walk null hypothesis, using the last high-frequency observation improves the mean squared prediction errors of the no-change forecast constructed from average monthly or quarterly data by up to 45 percent. We apply this insight to forecasts of the real price of crude oil and show that a new benchmark that relies on monthly closing prices dominates the conventional no-change forecast in terms of forecast accuracy. Although model-based forecasts also improve when models are estimated using closing prices, only the futures-based forecast significantly outperforms the new benchmark. Introducing a more suitable benchmark changes the assessments of different forecasting approaches and of the general predictability of real oil prices.
    Keywords: Econometric and statistical methods, International topics
    JEL: C53 Q47
    Date: 2020–09
  3. By: Ruipeng Liu (Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: This paper examines if incorporating investors' uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using the Markov-switching multifractal (MSM) model, we find that investors' uncertainty can substantially increase the accuracy of the forecasts of stock market volatility according to the forecast encompassing test. We further provide evidence that the MSM outperforms the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model.
    Keywords: Investors' uncertainty, Stock market risk, MSM, Volatility forecasting
    Date: 2020–09
  4. By: Grishina, Irina (Гришина, Ирина) (The Russian Presidential Academy of National Economy and Public Administration); Polynev, Andrey (Полынев, Андрей) (The Russian Presidential Academy of National Economy and Public Administration); Shkuropat, Anna (Шкуропат, Анна) (The Russian Presidential Academy of National Economy and Public Administration); Kotov, Alexander (Котов, Александр) (The Russian Presidential Academy of National Economy and Public Administration)
    Abstract: Development of a methodology for medium-term forecasting of socio-economic development in the context of the constituent entities of the Russian Federation, taking into account the forecast of the main macroeconomic parameters and territorial factors that determine the interregional differentiation of the socio-economic development of Russia. To achieve the above goal, the following tasks were solved: - the analysis of domestic and foreign experience in forecasting the socio-economic development of regions and methods of decomposition of predicted macroeconomic indicators at the regional level was carried out; - the analysis of the territorial proportions of the socio-economic development of the Russian Federation for the retrospective period was carried out, the identification of the main factors of their formation and trends of change; - methodological approaches to forecasting indicators of socio-economic development of constituent entities of the Russian Federation for the medium term have been developed using the version of forecasting macroeconomic indicators of the development of the Russian Federation; - formed the initial statistical base for forecasting indicators of socio-economic development of the constituent entities of the Russian Federation for the period up to 2022; - options for a preliminary forecast of the main indicators of the socio-economic development of the constituent entities of the Russian Federation for the period 2020 - 2024 have been developed.
    Date: 2020–05
  5. By: Berta, Paolo (University of Milan-Bicocca); Lovaglio, Pietro Giorgio (University of Milan-Bicocca); Paruolo, Paolo (European Commission); Verzillo, Stefano (European Commission)
    Abstract: Response management to the SARS-CoV-2 outbreak requires to answer several forecasting tasks. For hospital managers, a major one is to anticipate the likely needs of beds in intensive care in a given catchment area one or two weeks ahead, starting as early as possible in the evolution of the epidemic. This paper proposes to use a bivariate Error Correction model to forecast the needs of beds in intensive care, jointly with the number of patients hospitalised with Covid-19 symptoms. Error Correction models are found to provide reliable forecasts that are tailored to the local characteristics both of epidemic dynamics and of hospital practice for various regions in Europe in Italy, France and Scotland, both at the onset and at later stages of the spread of the disease. This reasonable forecast performance suggests that the present approach may be useful also beyond the set of analysed regions.
    Keywords: SARS-CoV-2, Covid-19, Intensive Care Units, Cointegration, Error correction models, Health forecasting, Multivariate time series, Vector Autoregression Models
    JEL: C53 C32
    Date: 2020–09
  6. By: Rob J Hyndman; Yijun Zeng; Han Lin Shang
    Abstract: We forecast the old-age dependency ratio for Australia under various pension age proposals, and estimate a pension age scheme that will provide a stable old-age dependency ratio at a specified level. Our approach involves a stochastic population forecasting method based on coherent functional data models for mortality, fertility and net migration, which we use to simulate the future age-structure of the population. Our results suggest that the Australian pension age should be increased to 68 by 2030, 69 by 2036, and 70 by 2050, in order to maintain the old-age dependency ratio at 23%, just above the 2018 level. Our general approach can easily be extended to other target levels of the old-aged dependency ratio and to other countries.
    Keywords: coherent forecasts, demographic components, functional time series, pension age
    JEL: J11 J14 C22
    Date: 2020
  7. By: Van, Germinal G.
    Abstract: The coronavirus has deleteriously affected a great majority of countries in the world. Developed societies such as the United States and the majority of Western countries have had the highest rates of mortality because of the pandemic. Sub-Saharan Africa, on the other hand, has been the continent where the pandemic has not done excessive damages. Africa’s GDP growth did not significantly decrease compared with the other continents. Consequently, the purpose of this paper is to model and forecast economic growth in sub-Saharan Africa in the post-COVID era and to examine the factors that are part of the growth process of the continent. To appropriately develop an econometric model of the economic growth of Sub-Saharan Africa in the post-COVID era, we decided to use the time-series data. This time-series data will be the dataset used to develop the statistical model that will enable us to forecast the economic growth of the continent in the post-COVID era.
    Keywords: Econometrics, Macroeconomics, Mathematical Modeling, Time-Series Analysis, Autoregressive Model, Statistical Modeling
    JEL: C01 C02 C15 C22 C53 O11
    Date: 2020–09–27
  8. By: Rotem Zelingher (ECO-PUB - Economie Publique - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); David Makowski; Thierry Brunelle (CIRED - Centre International de Recherche sur l'Environnement et le Développement - CNRS - Centre National de la Recherche Scientifique - ENPC - École des Ponts ParisTech - EHESS - École des hautes études en sciences sociales - AgroParisTech - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement)
    Abstract: Agricultural price shocks strongly affect farmers' income and food security. It is therefore important to understand the origin of these shocks and anticipate their occurrence. In this study, we explore the possibility of predicting global prices of one of the world main agricultural commodity-maize-based on variations in regional production. We examine the performances of several machine-learning (ML) methods and compare them with a powerful time series model (TBATS) trained with 56 years of price data. Our results show that, out of nineteen regions, global maize prices are mostly influenced by Northern America. More specifically, small positive production changes relative to the previous year in Northern America negatively impact the world price while production of other regions have weak or no influence. We find that TBATS is the most accurate method for a forecast horizon of three months or less. For longer forecasting horizons, ML techniques based on bagging and gradient boosting perform better but require yearly input data on regional maize productions. Our results highlight the interest of ML for predicting global prices of major commodities and reveal the strong sensitivity of global maize price to small variations of maize production in Northern America.
    Keywords: Food-security,Maize,Agricultural commodity prices,Regional production,Machine learning
    Date: 2020–09–22
  9. By: Bellotti, Anthony; Brigo, Damiano; Gambetti, Paolo; Vrins, Frédéric
    Keywords: loss given default ; credit risk ; defaulted loans ; debt collection ; superior set of models
    Date: 2020–01–01
  10. By: Bahman Rostami-Tabar; Mohammad M Ali; Tao Hong; Rob J Hyndman; Michael D Porter; Aris Syntetos
    Abstract: Forecasting plays a critical role in the development of organisational business strategies. Despite a considerable body of research in the area of forecasting, the focus has largely been on the financial and economic outcomes of the forecasting process as opposed to societal benefits. Our motivation in this study is to promote the latter, with a view to using the forecasting process to advance social and environmental objectives such as equality, social justice and sustainability. We refer to such forecasting practices as Forecasting for Social Good (FSG) where the benefits to society and the environment take precedence over economic and financial outcomes. We conceptualise FSG and discuss its scope and boundaries in the context of the "Doughnut theory". We present some key attributes that qualify a forecasting process as FSG: it is concerned with a real problem, it is focused on advancing social and environmental goals and prioritises these over conventional measures of economic success, and it has a broad societal impact. We also position FSG in the wider literature on forecasting and social good practices. We propose an FSG maturity framework as the means to engage academics and practitioners with research in this area. Finally, we highlight that FSG: (i) cannot be distilled to a prescriptive set of guidelines, (ii) is scalable, and (iii) has the potential to make significant contributions to advancing social objectives.
    Keywords: forecasting, social good, social foundation, ecological ceiling, sustainability
    Date: 2020
  11. By: Mouyad Alsamara (Qatar University); Zouhair Mrabet (Qatar University); Ahmad Shikh Ebid (UN ESCWA)
    Abstract: This paper investigates the relationship between the main macroeconomic indicators, namely real GDP, consumer prices and parallel market exchange rate in the Syrian economy during the period 1990-2017. We provide a comprehensive analysis for the macroeconomic policies and performance in the pre-conflict and during the conflict periods. For this purpose, we employ two advanced estimation approaches, namely, nonlinear ARDL and Structural VAR. these techniques are very useful to estimate how real GDP has reacted to shocks stemming from three major macroeconomic variables namely, money supply, consumer prices, and parallel exchange rate market. The empirical results indicate that the responses of real GDP to negative shocks in money supply are greater than its responses to positive shocks in money supply during the conflict period. Moreover, we distinguish four different scenario for money supply as possible views of rebuilding scenarios. The achievement of this scenario depends on the political settlement agreement and the size of capital inflow into the economy.
    Date: 2020–04–20
  12. By: Elena Carletti; Tommaso Oliviero; Marco Pagano; Loriana Pelizzon
    Abstract: We forecast the drop in profits and the equity shortfall triggered by the COVID-19 lockdown, using a representative sample of 80,972 Italian firms. A 3-month lockdown entails an aggregate yearly drop in profits of about 10% of GDP and results in financial distress for 17% of the sample firms, employing 8.8% of the sample employees. Distress is more frequent for small and medium-sized enterprises, for firms with high pre-COVID-19 leverage, and those belonging to the Manufacturing and Wholesale Trading sectors. Listed companies are less likely to enter distress, while there is no clear correlation between distress rates and family firm ownership.
    Keywords: COVID-19, pandemics, losses, distress, equity, recapitalization.
    JEL: G01 G32 G33
    Date: 2020–10

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