nep-for New Economics Papers
on Forecasting
Issue of 2021‒04‒05
nine papers chosen by
Rob J Hyndman
Monash University

  1. Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO By Arkadiusz Jedrzejewski; Grzegorz Marcjasz; Rafal Weron
  2. Global Financial Cycle and the Predictability of Oil Market Volatility: Evidence from a GARCH-MIDAS Model By Afees A. Salisu; Rangan Gupta; Riza Demirer
  3. Forecasting Imminent Deaths By Rizzi, Silvia; Vaupel, James W
  4. The Hard Problem of Prediction for Conflict Prevention By Hannes Mueller; Christopher Rauh
  5. How are Day-Ahead Prices Informative for Predicting the Next Day’s Consumption of Natural Gas ? By Arthur Thomas; Olivier Massol; Benoît Sévi
  6. Forecasting inflation with twitter By J. Daniel Aromí; Martín Llada
  7. Forecasting open-high-low-close data contained in candlestick chart By Huiwen Wang; Wenyang Huang; Shanshan Wang
  8. Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models By Bhaghoe, Sailesh; Ooft, Gavin
  9. Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data By Zhaoxing Gao; Ruey S. Tsay

  1. By: Arkadiusz Jedrzejewski; Grzegorz Marcjasz; Rafal Weron
    Abstract: Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can be achieved also in the case of parameter-rich models estimated via the \textit{least absolute shrinkage and selection operator} (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes based on different approaches to modeling the long-term seasonal component.
    Keywords: Electricity price forecasting; Day-ahead market; LASSO; Long-term seasonal component; Variance stabilizing transformation; Forecast averaging
    JEL: C22 C32 C51 C53 Q41 Q47
    Date: 2021–03–28
  2. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA)
    Abstract: This study examines the predictive power of the global financial cycle (GFCy) over oil market volatility using the GARCH-MIDAS framework. The GARCH-MIDAS model provides an appropriate setting to forecast high frequency oil market volatility using global predictors that are only available at low frequency. We show that the global financial cycle carries significant predictive information over both oil market volatility proxies, both in- and out-of-sample. The predictive relationship is found to be positive, more strongly during the pre-GFC period, suggesting that rising global asset prices coupled with improved cross-border capital flows are associated with rising volatility in the oil market. While the GARCH-MIDAS model incorporating GFCy or any other proxy of global financial/economic conditions yields economic gains compared to the conventional GARCH-MIDAS-RV specification, especially in the pre-GFC period; the stance is found to be robust to risk aversion and leverage ratio. The economic gains observed from the GFCy-based model particularly during the pre-GFC period when world markets experienced a steady rise in global asset prices and cross-border capital flows underline the potential role of risk appetite (or behavioural factors) in forecasting applications. Overall, our results suggest that incorporating low frequency proxies of global asset market conditions can provide significant forecasting gains for energy market models, with significant implications for both investors and policymakers.
    Keywords: Global Financial Cycle, Oil Volatility, Predictability, MIDAS models
    JEL: C32 C53 G15 Q02
    Date: 2021–03
  3. By: Rizzi, Silvia; Vaupel, James W
    Abstract: We introduce a new method for making short-term mortality forecasts of a few months, illustrating it by estimating how many deaths might have happened if some major shock had not occurred. We apply the method to assess excess mortality from March to June 2020 in Denmark and Sweden as a result of the first wave of the coronavirus pandemic, associated policy interventions and behavioral, healthcare, social and economic changes. We chose to compare Denmark and Sweden because reliable data were available and because the two countries are similar but chose different responses to covid-19: Denmark imposed a rather severe lockdown; Sweden did not. We make forecasts by age and sex to predict expected deaths if covid-19 had not struck. Subtracting these forecasts from observed deaths gives the excess death count. Excess deaths were lower in Denmark than Sweden during the first wave of the pandemic. The later/earlier ratio we propose for shortcasting is easy to understand, requires less data than more elaborate approaches, and may be useful in many countries in making both predictions about the future and the past to study the impact on mortality of coronavirus and other epidemics. In the application to Denmark and Sweden, prediction intervals are narrower and bias is less than when forecasts are based on averages of the last five years, as is often done. More generally, later/earlier ratios may prove useful in short-term forecasting of illnesses and births as well as economic and other activity that varies seasonally or periodically.
    Date: 2021–02–03
  4. By: Hannes Mueller; Christopher Rauh
    Abstract: There is a growing interest in prevention in several policy areas and this provides a strong motivation for an improved integration of forecasting with machine learning into models of decision making. In this article we propose a framework to tackle conflict prevention. A key problem of conflict forecasting for prevention is that predicting the start of conflict in previously peaceful countries needs to overcome a low baseline risk. To make progress in this hard problem this project combines a newspaper-text corpus of more than 4 million articles with unsupervised and supervised machine learning. The output of the forecast model is then integrated into a simple static framework in which a decision maker decides on the optimal number of interventions to minimize the total cost of conflict and intervention. This exercise highlights the potential cost savings of prevention for which reliable forecasts are a prerequisite.
    Keywords: armed conflict, forecasting, machine learning, newspaper text, random forest, topic models
    JEL: O11 O43
    Date: 2021–03
  5. 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 - UM - Le Mans Université - UA - Université d'Angers - UN - Université de Nantes - ECN - École Centrale de Nantes - UBS - Université de Bretagne Sud - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer - CNRS - Centre National de la Recherche Scientifique - 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, University of London [London]); Benoît Sévi (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - IUML - FR 3473 Institut universitaire Mer et Littoral - UM - Le Mans Université - UA - Université d'Angers - UN - Université de Nantes - ECN - École Centrale de Nantes - UBS - Université de Bretagne Sud - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer - CNRS - Centre National de la Recherche Scientifique - 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, for the first time, 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 and apply it to the case of France. Our results document the existence of a long-run relation between demand and spot prices and provide estimates of the marginal impacts that these price variables have on observed demand levels. We also provide evidence of the pivotal role of the spark ratio in the short run 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,load forecasting
    Date: 2020–12
  6. By: J. Daniel Aromí; Martín Llada
    Abstract: We use Twitter content to generate an indicator of attention allocated to inflation. The analysis corresponds to Argentina for the period 2012-2019. The attention index provides valuable information regarding future levels of inflation. A one standard deviation increment in the index is followed by an increment of approximately 0.4% in expected inflation in the consecutive month. Out-of-sample exercises confirm that social media content allows for gains in forecast accuracy. Beyond point forecasts, the index provides valuable information regarding inflation uncertainty. The proposed indicator compares favorably with other indicators such as media content, media tweets, google search intensity and consumer surveys.
    JEL: E31 C53
    Date: 2020–11
  7. By: Huiwen Wang; Wenyang Huang; Shanshan Wang
    Abstract: Forecasting the (open-high-low-close)OHLC data contained in candlestick chart is of great practical importance, as exemplified by applications in the field of finance. Typically, the existence of the inherent constraints in OHLC data poses great challenge to its prediction, e.g., forecasting models may yield unrealistic values if these constraints are ignored. To address it, a novel transformation approach is proposed to relax these constraints along with its explicit inverse transformation, which ensures the forecasting models obtain meaningful openhigh-low-close values. A flexible and efficient framework for forecasting the OHLC data is also provided. As an example, the detailed procedure of modelling the OHLC data via the vector auto-regression (VAR) model and vector error correction (VEC) model is given. The new approach has high practical utility on account of its flexibility, simple implementation and straightforward interpretation. Extensive simulation studies are performed to assess the effectiveness and stability of the proposed approach. Three financial data sets of the Kweichow Moutai, CSI 100 index and 50 ETF of Chinese stock market are employed to document the empirical effect of the proposed methodology.
    Date: 2021–03
  8. By: Bhaghoe, Sailesh (The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise); Ooft, Gavin (The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise)
    Abstract: We apply Factor-MIDAS (FaMIDAS) and Mixed-Frequency Vector Autoregression (MF-VAR and MF-Bayesian VAR) to nowcast quarterly GDP growth of Suriname. For this purpose, we use a set of 44 microeconomic indices over the sample period 2012Q1 - 2020Q2. In the target equation, we regress GDP growth upon its first lag and a beta coefficient. In the explanatory equations the first set of monthly regressors explain the variation of growth without lags while the second set of regressors are fitted with two-month lags. We apply three set of samples for model estimations: 2012Q1 – 2019Q3, 2012Q1 – 2020Q1 and 2012Q1 – 2020Q2. Model nowcast accuracy is benchmarked against GDP growth of 2019 and economic activity growth estimated by the monthly GDP indicator of March and June 2020. The models provide mixed results as compared to the benchmark indicators. We select the models with the lowest Root Mean Squared Error (RMSE) and based on own Judgment to nowcast. As the forecast horizon increases from 2019Q4 to 2020Q2, so do the RMSE. To hedge against high biases and variances, we combine the best nowcasts to produce a single nowcast. Furthermore, it appeared that the FaMIDAS and the MF-VAR models deliver adequate results for two nowcast horizons.
    Keywords: FaMIDAS; MF-VAR; MF-BVAR; Nowcasting
    JEL: C22 C53 E37
    Date: 2021–03
  9. By: Zhaoxing Gao; Ruey S. Tsay
    Abstract: This paper proposes a hierarchical approximate-factor approach to analyzing high-dimensional, large-scale heterogeneous time series data using distributed computing. The new method employs a multiple-fold dimension reduction procedure using Principal Component Analysis (PCA) and shows great promises for modeling large-scale data that cannot be stored nor analyzed by a single machine. Each computer at the basic level performs a PCA to extract common factors among the time series assigned to it and transfers those factors to one and only one node of the second level. Each 2nd-level computer collects the common factors from its subordinates and performs another PCA to select the 2nd-level common factors. This process is repeated until the central server is reached, which collects common factors from its direct subordinates and performs a final PCA to select the global common factors. The noise terms of the 2nd-level approximate factor model are the unique common factors of the 1st-level clusters. We focus on the case of 2 levels in our theoretical derivations, but the idea can easily be generalized to any finite number of hierarchies. We discuss some clustering methods when the group memberships are unknown and introduce a new diffusion index approach to forecasting. We further extend the analysis to unit-root nonstationary time series. Asymptotic properties of the proposed method are derived for the diverging dimension of the data in each computing unit and the sample size $T$. We use both simulated data and real examples to assess the performance of the proposed method in finite samples, and compare our method with the commonly used ones in the literature concerning the forecastability of extracted factors.
    Date: 2021–03

This nep-for issue is ©2021 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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