nep-for New Economics Papers
on Forecasting
Issue of 2020‒09‒28
five papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Low Frequency Macroeconomic Events with High Frequency Data By Ana B. Galvão; Michael T. Owyang
  2. How to Estimate a VAR after March 2020 By Michele Lenza; Giorgio E. Primiceri
  3. A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and application in predicting total COVID-19 cases By NGO, Hoang Anh; HOANG, Thai Nam
  4. Use of Unofficial Newspaper Data for COVID-19 Death Surveillance By Ahamad, Mazbahul G; Ahmed, Monir U.; Talukder, Byomkesh; Tanin, Fahian
  5. Are We More Accurate? Revisiting the European Commission’s Macroeconomic Forecasts By Andras Chabin; Sébastien Lamproye; Milan Výškrabka

  1. By: Ana B. Galvão; Michael T. Owyang
    Abstract: High-frequency financial and economic activity indicators are usually time aggregated before forecasts of low-frequency macroeconomic events, such as recessions, are computed. We propose a mixed-frequency modelling alternative that delivers high-frequency probability forecasts (including their confidence bands) for these low-frequency events. The new approach is compared with single-frequency alternatives using loss functions adequate to rare event forecasting. We provide evidence that: (i) weekly-sampled spread improves over monthly-sampled to predict NBER recessions, (ii) the predictive content of the spread and the Chicago Fed Financial Condition Index (NFCI) is supplementary to economic activity for one-year-ahead forecasts of contractions, and (iii) a weekly activity index can date the 2020 business cycle peak two months in advance using a mixed-frequency filtering.
    Keywords: mixed frequency models; recession; financial indicators; weekly activity index; event probability forecasting
    JEL: C25 C53 E32
    Date: 2020–09
  2. By: Michele Lenza; Giorgio E. Primiceri
    Abstract: This paper illustrates how to handle a sequence of extreme observations—such as those recorded during the COVID-19 pandemic—when estimating a Vector Autoregression, which is the most popular time-series model in macroeconomics. Our results show that the ad-hoc strategy of dropping these observations may be acceptable for the purpose of parameter estimation. However, disregarding these recent data is inappropriate for forecasting the future evolution of the economy, because it vastly underestimates uncertainty.
    JEL: C11 C32 E32 E37
    Date: 2020–09
  3. By: NGO, Hoang Anh; HOANG, Thai Nam
    Abstract: The Nonlinear Grey Bernoulli Model NGBM(1, 1) is a recently developed grey model which has various applications in different fields, mainly due to its accuracy in handling small time-series datasets with nonlinear variations. In this paper, to fully improve the accuracy of this model, a novel model is proposed, namely Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1, 1). This model combines the rolling mechanism with the simultaneous optimization of all model parameters (exponential, background value and initial condition). The accuracy of this new model has significantly been proven through forecasting Vietnam’s GDP from 2013 to 2018, before it is applied to predict the total COVID-19 infected cases globally by day.
    Date: 2020–05–14
  4. By: Ahamad, Mazbahul G (University of Nebraska–Lincoln); Ahmed, Monir U.; Talukder, Byomkesh; Tanin, Fahian
    Abstract: Objective. To highlight the critical importance of unofficially reported newspaper-based deaths from coronavirus disease 2019 (COVID-19)–like illness (CLI) together with officially confirmed death counts to support improvements in COVID-19 death surveillance. Methods. Both hospital-based official COVID-19 and unofficial CLI death counts were collected from daily newspapers between March 8 and August 22, 2020. We performed both exploratory and time-series analyses to understand the influence of combining newspaper-based CLI death counts with confirmed hospital death counts on the trends and forecasting of COVID-19 death counts. An autoregressive integrated moving average–based approach was used to forecast the number of weekly death counts for six weeks ahead. Results. Between March 8 and August 22, 2020, 2,156 CLI deaths were recorded based on newspaper reporting for a count that was 55% of the officially confirmed death count (n = 3,907). This shows that newspaper reports tend to cover a significant number of COVID-19 related deaths. Our forecast also indicates an approximate total of 406 CLI expected for the six weeks ahead, which could contribute to a total of 2,413 deaths including 2,007 confirmed deaths expected from August 23 to October 3, 2020. Conclusions. Analyzing existing trends in and forecasting the expected number of newspaper-based CLI deaths indicates yet-unreported COVID-19 death counts, which could be a critical source to estimate provisional COVID-19 death counts and mortality surveillance. Public Health Implications. Considering unofficial newspaper-based CLI death counts is essential to identify COVID-19 death severity and surveillance needs to advance public health research efforts to prepare appropriate response strategies for low- and middle-income countries.
    Date: 2020–09–11
  5. By: Andras Chabin; Sébastien Lamproye; Milan Výškrabka
    Abstract: In this paper, we present the results of the comprehensive assessment of the accuracy of European Economic Forecasts. High-quality macroeconomic forecasts are a prerequisite for economic surveillance of the European Commission. We evaluate forecasts for three key variables – GDP growth, consumer price inflation and the general government budget balance – on two forecast horizons – current year and oneyear-ahead – over the period 2000-2017. Pointing to some improvement in the accuracy recently, the forecasts continue to show a satisfactory track record which does not differ much from the forecast track records of other international institutions. The Commission’s forecasts present largely an unbiased outlook for near term economic developments, accurately foresee an acceleration and deceleration in the underlying variables and mostly contain information beyond a naïve forecast. There is room for improvement, however. The forecasts appear to be prone to repeating errors, which to some extent seems to be related to an overly conservative assessment of the business cycle dynamics and to a lesser extent to errors in technical assumptions.
    JEL: C1 E60 E66
    Date: 2020–07

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