nep-for New Economics Papers
on Forecasting
Issue of 2021‒10‒04
twelve papers chosen by
Rob J Hyndman
Monash University

  1. Predicting the COVID-19 epidemic: is a regional approach preferable? By Laura Coroneo,; Fabrizio Iacone,; Giancarlo Manzi,; Silvia Salini
  2. Forecasting Electricity Prices with Expert, Linear and Non-Linear Models By Anna Gloria Billé; Angelica Gianfreda; Filippo Del Grosso; Francesco Ravazzolo
  3. Quantifying time-varying forecast uncertainty and risk for the real price of oil By Knut Are Aastveit; Jamie L. Cross; Herman K. van Dijk
  4. No-Regret Forecasting with Egalitarian Committees By Jiun-Hua Su
  5. Modélisation et prévision du nombre d’infections au coronavirus au Togo: une approche par un modèle ARIMA avec le logiciel R By Kadanga, Mayo Takémsi Norris; Togbenu, Fo-Kossi Edem
  6. The Bias and Efficiency of the ECB Inflation Projections: a State Dependent Analysis By Eleonora Granziera; Pirkka Jalasjoki; Maritta Paloviita
  7. Macroeconomic forecasting with LSTM and mixed frequency time series data By Sarun Kamolthip
  8. Forecasting the vaccine uptake rate: An infodemiological study in the US By Xingzuo Zhou; Yiang Li
  9. Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility By Eduardo Ramos-P\'erez; Pablo J. Alonso-Gonz\'alez; Jos\'e Javier N\'u\~nez-Vel\'azquez
  10. Estimating the Variance of a Combined Forecast: Bootstrap-Based Approach By Ulrich Hounyo; Kajal Lahiri
  11. Stock Price Prediction Under Anomalous Circumstances By Jinlong Ruan; Wei Wu; Jiebo Luo
  12. Advancing the Science of Travel Demand Forecasting By Walker, Joan L.; Chatman, Daniel; Daziano, Ricardo; Erhardt, Gregory; Gao, Song; Mahmassani, Hani; Ory, David; Sall, Elizabeth; Bhat, Chandra; Chim, Nicholas; Daniels, Clint; Gardner, Brian; Kressner, Josephine; Miller, Eric; Pereira, Francisco; Picado, Rosella; Hess, Stephane; Axhausen, Kay; Bareinboim, Elias; Ben-Akiva, Moshe; Brathwaite, Timothy; Charlton, Billy; Chen, Siyu; Circella, Giovanni; El Zarwi, Feras; Gonzalez, Marta; Harb, Mustapha; Mahmassani, Amine; McFadden, Daniel; Moekel, Rolf; Pozdnukhov, Alexei; Sheehan, Maddie; Sivakumar, Aruna; Weeks, Jennifer; Zhao, Jinhua

  1. By: Laura Coroneo,; Fabrizio Iacone,; Giancarlo Manzi,; Silvia Salini
    Abstract: We use a SIRD model to predict the dynamics of the COVID-19 epidemic in the Italian regions at 1 to 4 weeks ahead. Out of sample forecasting results indicate that national forecasts obtained by aggregating regional forecasts are more accurate than predictions from a national model. These results suggest that national health authorities should take into account the level of heterogeneity across regions when predicting the spread of a national epidemic.
    Keywords: Forecasting, Aggregation, Forecast evaluation, Epidemic.
    JEL: C12 C53 I18
    Date: 2021–09
  2. By: Anna Gloria Billé (Department of Statistical Sciences, University of Padua, Italy); Angelica Gianfreda (Department of Economics, University of Modena and Reggio Emilia, Italy; Energy Markets Group, London Business School, UK); Filippo Del Grosso (Faculty of Economics and Management, Free University of Bozen, Italy); Francesco Ravazzolo (Faculty of Economics and Management, Free University of Bozen, Italy; BI Norwegian Business School; Rimini Centre for Economic Analysis)
    Abstract: This paper provides an iterative model selection for forecasting day–ahead hourly electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources (RES), fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA–GARCH models hence including parsimonious autoregressive specifications (known as expert-type models). Results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass and waste, weighted imports and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting. Considering the northern Italian prices and using the Model Confidence Set, predictions indicate a strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and RES improves the forecast accuracy of electricity prices more than predictions freely available to researchers.
    Keywords: Demand, Wind, Solar, Biomass, Waste, Fossil Fuels (coal, natural gas, CO2), Weighted Inflows, Commercial and Public Forecasts
    JEL: C13 C22 C53 Q47
    Date: 2021–09
  3. By: Knut Are Aastveit; Jamie L. Cross; Herman K. van Dijk
    Abstract: We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.
    Keywords: oil price, forecast density combination, bayesian forecasting, instabilities, model uncertainty
    JEL: C11 C32 C53 Q43 Q47
    Date: 2021–06–01
  4. By: Jiun-Hua Su
    Abstract: The forecast combination puzzle is often found in literature: The equal-weight scheme tends to outperform sophisticated methods of combining individual forecasts. Exploiting this finding, we propose a hedge egalitarian committees algorithm (HECA), which can be implemented via mixed integer quadratic programming. Specifically, egalitarian committees are formed by the ridge regression with shrinkage toward equal weights; subsequently, the forecasts provided by these committees are averaged by the hedge algorithm. We establish the no-regret property of HECA. Using data collected from the ECB Survey of Professional Forecasters, we find the superiority of HECA relative to the equal-weight scheme during the COVID-19 recession.
    Date: 2021–09
  5. By: Kadanga, Mayo Takémsi Norris; Togbenu, Fo-Kossi Edem
    Abstract: In this paper, we attempt to propose a short-term prediction model of the number of new cases of coronavirus infections in Togo using the R software. From the original daily data, a new weekly database containing 80 observations was constructed. After splitting this new database into training and test samples in order to select the appropriate model, the database was then used to build our forecasting model, the ARIMA(2,1,2) model. This model was used to make forecasts for the next four weeks. The findings show that Togo can expect approximately 1200 infections in average every week if suitable measures are not adopted in order to stop the rapid spread of the virus in the country.
    Keywords: Coronavirus, COVID-19, Forecast, ARIMA
    JEL: C53
    Date: 2021–09–24
  6. By: Eleonora Granziera; Pirkka Jalasjoki; Maritta Paloviita
    Abstract: We test for bias and efficiency of the ECB inflation forecasts using a confidential dataset of ECB macroeconomic quarterly projections. We investigate whether the properties of the forecasts depend on the level of inflation, by distinguishing whether the inflation observed by the ECB at the time of forecasting is above or below the target. The forecasts are unbiased and efficient on average, however there is evidence of state dependence. In particular, the ECB tends to overpredict (underpredict) inflation at intermediate forecast horizons when inflation is below (above) target. The magnitude of the bias is larger when inflation is above the target. These results hold even after accounting for errors in the external assumptions. We also find evidence of inefficiency, in the form of underreaction to news, but only when inflation is above the target. Our findings bear important implications for the ECB forecasting process and ultimately for its communication strategy.
    Keywords: forecast evaluation, forecast eciency, ination forecasts, central bank communication
    JEL: C12 C22 C53 E31 E52
    Date: 2021–04–28
  7. By: Sarun Kamolthip
    Abstract: This paper demonstrates the potentials of the long short-term memory (LSTM) when applyingwith macroeconomic time series data sampled at different frequencies. We first present how theconventional LSTM model can be adapted to the time series observed at mixed frequencies when thesame mismatch ratio is applied for all pairs of low-frequency output and higher-frequency variable. Togeneralize the LSTM to the case of multiple mismatch ratios, we adopt the unrestricted Mixed DAtaSampling (U-MIDAS) scheme (Foroni et al., 2015) into the LSTM architecture. We assess via bothMonte Carlo simulations and empirical application the out-of-sample predictive performance. Ourproposed models outperform the restricted MIDAS model even in a set up favorable to the MIDASestimator. For real world application, we study forecasting a quarterly growth rate of Thai realGDP using a vast array of macroeconomic indicators both quarterly and monthly. Our LSTM withU-MIDAS scheme easily beats the simple benchmark AR(1) model at all horizons, but outperformsthe strong benchmark univariate LSTM only at one and six months ahead. Nonetheless, we find thatour proposed model could be very helpful in the period of large economic downturns for short-termforecast. Simulation and empirical results seem to support the use of our proposed LSTM withU-MIDAS scheme to nowcasting application.
    Date: 2021–09
  8. By: Xingzuo Zhou; Yiang Li
    Abstract: A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data-involving an autoregressive model with autoregressive integrated moving average (ARIMA)- and innovative web search queries-involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provided a more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.
    Date: 2021–09
  9. By: Eduardo Ramos-P\'erez; Pablo J. Alonso-Gonz\'alez; Jos\'e Javier N\'u\~nez-Vel\'azquez
    Abstract: Events such as the Financial Crisis of 2007-2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.
    Date: 2021–09
  10. By: Ulrich Hounyo (University at Albany and CREATES); Kajal Lahiri (University at Albany)
    Abstract: This paper considers bootstrap inference in model averaging for predictive regressions. We first consider two different types of bootstrap methods in predictive regressions: standard pairwise bootstrap and standard fixed-design residual-based bootstrap. We show that these procedures are not valid in the context of model averaging. These common bootstrap approaches induce a bias-related term in the bootstrap variance of averaging estimators. We then propose and justify a fixed-design residual-based bootstrap resampling approach for model averaging. In a local asymptotic framework, we show the validity of the bootstrap in estimating the variance of a combined forecast and the asymptotic covariance matrix of a combined parameter vector with fixed weights. Our proposed method preserves non-parametrically the cross-sectional dependence between different models and the time series dependence in the errors simultaneously. The finite sample performance of these methods are assessed via Monte Carlo simulations. We illustrate our approach using an empirical study of the Taylor rule equation with 24 alternative specifications.
    Keywords: Bootstrap, Local asymptotic theory, Model average estimators, Wild bootstrap, Variance of consensus forecast
    JEL: C33 C53 C80
    Date: 2021–09–28
  11. By: Jinlong Ruan; Wei Wu; Jiebo Luo
    Abstract: The stock market is volatile and complicated, especially in 2020. Because of a series of global and regional "black swans," such as the COVID-19 pandemic, the U.S. stock market triggered the circuit breaker three times within one week of March 9 to 16, which is unprecedented throughout history. Affected by the whole circumstance, the stock prices of individual corporations also plummeted by rates that were never predicted by any pre-developed forecasting models. It reveals that there was a lack of satisfactory models that could predict the changes in stocks prices when catastrophic, highly unlikely events occur. To fill the void of such models and to help prevent investors from heavy losses during uncertain times, this paper aims to capture the movement pattern of stock prices under anomalous circumstances. First, we detect outliers in sequential stock prices by fitting a standard ARIMA model and identifying the points where predictions deviate significantly from actual values. With the selected data points, we train ARIMA and LSTM models at the single-stock level, industry level, and general market level, respectively. Since the public moods affect the stock market tremendously, a sentiment analysis is also incorporated into the models in the form of sentiment scores, which are converted from comments about specific stocks on Reddit. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98% which can be used to optimize existing prediction methodologies.
    Date: 2021–09
  12. By: Walker, Joan L.; Chatman, Daniel; Daziano, Ricardo; Erhardt, Gregory; Gao, Song; Mahmassani, Hani; Ory, David; Sall, Elizabeth; Bhat, Chandra; Chim, Nicholas; Daniels, Clint; Gardner, Brian; Kressner, Josephine; Miller, Eric; Pereira, Francisco; Picado, Rosella; Hess, Stephane; Axhausen, Kay; Bareinboim, Elias; Ben-Akiva, Moshe; Brathwaite, Timothy; Charlton, Billy; Chen, Siyu; Circella, Giovanni; El Zarwi, Feras; Gonzalez, Marta; Harb, Mustapha; Mahmassani, Amine; McFadden, Daniel; Moekel, Rolf; Pozdnukhov, Alexei; Sheehan, Maddie; Sivakumar, Aruna; Weeks, Jennifer; Zhao, Jinhua
    Abstract: Travel demand forecasting models play an important role in guiding policy, planning, and design of transportation systems. There is no shortage of literature critiquing the accuracy of model forecasts (see, for example, Pickrell, 1989; Wachs, 1990; Pickrell, 1992; Flyvbjerg, Skamris Holm, and Buhl 2005; Richmond, 2005; Flyvbjerg, 2007; Bain, 2009; Parthasarathi and Levinson, 2010; Welde and Odeck, 2011; Hartgen, 2013; Nicolaisen and Driscoll, 2014; Schmitt, 2016; Odeck and Welde, 2017, and Voulgaris, 2019), not to mention several high-profile lawsuits (Saulwick 2014, Stacey 2015, Rubin 2018). Many researchers and practitioners feel more can be done to advance rigorous travel analysis methods for the public good (see, e.g., Motivated by these critiques, a two-day, NSF-funded workshop was held at UC Berkeley in the Spring of 2017 to engage in a fundamental review of the state of the art in travel demand modeling, to discuss the future of the field, and to propose new directions and processes for advancing the science. Travel demand forecasting is an inherently practical enterprise. While academics drive the fundamental research, the users of travel demand models and forecasts are typically government agencies and transport operators that use the models to inform long-range investment, funding, and planning decisions. Private firms play a key role in assisting the agencies in both development and application of the models, and, more recently, high-tech firms have entered the development fray. While all of these actors have important roles in advancing the science of the field, in this report we focus our attention primarily on the academic side of the enterprise, consistent with the orientation of the funding agency (NSF), and in order to make the task manageable. That said, other sectors are represented in various parts of this report as they interface with academics or play particularly central roles in our proposals for advancing the science.
    Keywords: Engineering
    Date: 2019–12–19

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.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.