nep-for New Economics Papers
on Forecasting
Issue of 2021‒08‒09
five papers chosen by
Rob J Hyndman
Monash University

  1. Testing the predictive accuracy of COVID-19 forecasts By Laura Coroneo; Fabrizio Iacone; Alessia Paccagnini; Paulo Santos Monteiro
  2. Superior Predictability of American Factors of the Won/Dollar Real Exchange Rate By Sarthak Behera; Hyeongwoo Kim; Soohyon Kim
  3. GDP Forecast of the Biggest GCC Economies Using ARIMA By Youssef, Jamile; Ishker, Nermeen; Fakhreddine, Nour
  4. Inference and forecasting for continuous-time integer-valued trawl processes and their use in financial economics By Mikkel Bennedsen; Asger Lunde; Neil Shephard; Almut E.D. Veraart
  5. COVID-19 Tourism Recovery in the ASEAN and East Asia Region: Asymmetric Patterns and Implications By Stathis Polyzos; Anestis Fotiadis; Aristeidis Samitas

  1. By: Laura Coroneo; Fabrizio Iacone; Alessia Paccagnini; Paulo Santos Monteiro
    Abstract: We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention during the first and second waves of the epidemic in the United States. We find three main results. First, at the short horizon (1-week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3- and 4-week ahead) forecasters are more successful and sometimes outperform the benchmark, in particular during the first wave of the epidemic. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior approach for health authorities, rather than relying on a small number of forecasts.
    Keywords: Forecast evaluation, Forecasting tests, Epidemic
    JEL: C12 C53 I18
    Date: 2021–07
  2. By: Sarthak Behera; Hyeongwoo Kim; Soohyon Kim
    Abstract: Utilizing an array of data dimensionality reduction methods, we estimate latent common factors of the Won/Dollar real exchange rate from a large panel of economic predictors of the U.S. and South Korea. We demonstrate superior out-of-sample predictability of our factor augmented forecasting models relative to conventional models when we utilize factors obtained from U.S. economic variables, while Korean factors fail to enhance predictability. Our models perform better at longer horizons when American real activity factors are employed, whereas American nominal/financial market factors help improve short-run prediction accuracy. UIP fundamental factors with the dollar as numeraire overall perform well, while PPP and RIRP factors play a limited role in forecasting the Won/Dollar exchange rate.
    Keywords: Won/Dollar Real Exchange Rate; Principal Component Analysis; Partial Least Squares; LASSO; Out-of-Sample Forecast
    JEL: C38 C53 C55 F31 G17
    Date: 2021–07
  3. By: Youssef, Jamile; Ishker, Nermeen; Fakhreddine, Nour
    Abstract: Gulf Cooperation Council (GCC) members are considered one of the fastest growing economies. This paper aims to empirically forecast the economic activity of the vastest GCC countries: Qatar, Saudi Arabia, and the United Arab Emirates. An Auto-Regressive Moving Average (ARIMA) model for the three countries Gross Domestic Product is obtained using the Box-Jenkins methodology during the 1980 - 2020 period. The appropriate models for the three economies are of ARIMA (0,2,1), the forecasts are at a 95% confidence level and predicts a growth in the three countries for the upcoming five years.
    Keywords: ARIMA Model; GDP; forecasting; GCC
    JEL: C22 C53 O1 O53
    Date: 2021–06–17
  4. By: Mikkel Bennedsen (Department of Economics and Business Economics, Aarhus University and CREATES); Asger Lunde (Copenhagen Economics and CREATES); Neil Shephard (Department of Economics and Department of Statistics, Harvard University); Almut E.D. Veraart (Imperial College London and CREATES)
    Abstract: This paper develops likelihood-based methods for estimation, inference, model selection, and forecasting of continuous-time integer-valued trawl processes. The full likelihood of integer-valued trawl processes is, in general, highly intractable, motivating the use of composite likelihood methods, where we consider the pairwise likelihood in lieu of the full likelihood. Maximizing the pairwise likelihood of the data yields an estimator of the parameter vector of the model, and we prove consistency and asymptotic normality of this estimator. The same methods allow us to develop probabilistic forecasting methods, which can be used to construct the predictive distribution of integer-valued time series. In a simulation study, we document good finite sample performance of the likelihood-based estimator and the associated model selection procedure. Lastly, the methods are illustrated in an application to modelling and forecasting financial bid-ask spread data, where we find that it is beneficial to carefully model both the marginal distribution and the autocorrelation structure of the data. We argue that integer-valued trawl processes are especially well-suited in such situations.
    Keywords: Integer valued trawl process, Lévy basis, composite likelihood, pairwise likelihood, estimation, model selection, forecasting
    JEL: C01 C13 C22 C51 C53 G17
    Date: 2021–07–27
  5. By: Stathis Polyzos; Anestis Fotiadis; Aristeidis Samitas (College of Business, Zayed University, Abu Dhabi, UAE)
    Abstract: The aim of this paper is to produce forecasts for tourism flows and tourism revenue for ASEAN and East Asian countries after the end of the COVID-19 pandemic. By implementing two different machine-learning methodologies (the Long Short Term Memory neural network and the Generalised Additive Model) and using different training data sets, we aim to forecast the recovery patterns for these data series for the first 12 months after the end of crisis. We thus produce a baseline forecast, based on the averages of our different models, as well as a worst- and best-case scenario. We show that recovery is asymmetric across the group of countries in the ASEAN and East Asian region and that recovery in tourism revenue is generally slower than in tourist arrivals. We show significant losses of approximately 48%, persistent after 12 months, for some countries, while others display increases of approximately 40% when compared to pre-crisis levels. Our work aims to quantify the projected drop in tourist arrivals and tourism revenue for ASEAN and East Asian countries over the coming months. The results of the proposed research can be used by policymakers as they determine recovery plans, where tourism will undoubtedly play a very important role.
    Keywords: COVID-19, tourism, deep learning, ASEAN, East Asia
    JEL: H12 P46 Z32
    Date: 2021–06–08

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.