nep-for New Economics Papers
on Forecasting
Issue of 2021‒03‒29
eight papers chosen by
Rob J Hyndman
Monash University

  1. Gauging the Effect of Influential Observations on Measures of Relative Forecast Accuracy in a Post-COVID-19 Era: Application to Nowcasting Euro Area GDP Growth By Boriss Siliverstovs
  2. Forecasting the U.S. Dollar in the 21st Century By Charles Engel; Steve Pak Yeung Wu
  3. The power of text-based indicators in forecasting the Italian economic activity By Valentina Aprigliano; Simone Emiliozzi; Gabriele Guaitoli; Andrea Luciani; Juri Marcucci; Libero Monteforte
  4. Online Learning with Radial Basis Function Networks By Gabriel Borrageiro; Nick Firoozye; Paolo Barucca
  5. Modeling and forecasting macroeconomic downside risk By Delle Monache, Davide; De Polis, Andrea; Petrella, Ivan
  6. Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data By Min Seong Kim
  7. Forecasting the Logistics Demand of Guangxi Beibu Gulf Port By Guoyou Yue
  8. Tail Forecasting with Multivariate Bayesian Additive Regression Trees By ; Todd E. Clark; Florian Huber; Gary Koop; Massimiliano Marcellino

  1. By: Boriss Siliverstovs (Latvijas Banka)
    Abstract: The previous research already emphasised the importance of investigating the predictive ability of econometric models separately during expansions and recessions (Chauvet and Potter (2013), Siliverstovs (2020), Siliverstovs and Wochner (2020)). Using the data for the pre-COVID period, it has been shown that ignoring asymmetries in a model's forecasting accuracy across the business cycle phases typically leads to a biased judgement of the model's predictive ability in each phase. In this study, we discuss the implications of data challenges posed by the COVID-19 pandemic on econometric model estimates and forecasts. Given the dramatic swings in GDP growth rates across a wide range of countries during the coronavirus pandemic, one can expect that the asymmetries in the models' predictive ability observed during the pre-COVID period will be further exacerbated in the post-COVID era. In such situations, recursive measures that dissect the models' forecasting ability observation by observation allow to gain detailed insights into the underlying causes of one model's domination over the others. In this paper, we suggest a novel metric referred to as the recursive relative mean squared forecast error (based on rearranged observations) or R2MSFE(+R). We show how this new metric paired with the cumulated sum of squared forecast error difference (CSSFED) of Welch and Goyal (2008) highlights significant differences in the relative forecasting ability of the dynamic factor model and naive univariate benchmark models in expansions and recessions that are typically concealed when only point estimates of relative forecast accuracy are reported.
    Keywords: COVID-19, nowcasting, GDP, euro area
    JEL: C22 C52 C53
    Date: 2021–02–02
  2. By: Charles Engel; Steve Pak Yeung Wu
    Abstract: The level of the (log of) the exchange rate seems to have strong forecasting power for dollar exchange rates against major currencies post-2000 at medium- to long-run horizons of 12-, 36- and 60-months. We find that this is true using conventional asymptotic statistics correcting for serial correlation biases. But correcting for small-sample bias using simulation methods, we find little evidence to reject a random walk. This small sample bias arises because of near-spurious correlation when the predictor variable is persistent and the horizon for exchange rate forecasts is long. Similar problems of spurious correlation may arise when other persistent variables are used to forecast changes in the exchange rate. We find, in fact, using asymptotic statistics, the level of the exchange rate provides better forecasts than economic measures of “global risk”, and the measures of global risk do not improve the (possibly spurious) forecasting power of the level of the exchange rate.
    JEL: F31 F37 G15
    Date: 2021–02
  3. By: Valentina Aprigliano (Bank of Italy); Simone Emiliozzi (Bank of Italy); Gabriele Guaitoli (University of Warwick); Andrea Luciani (Bank of Italy); Juri Marcucci (Bank of Italy); Libero Monteforte (Ufficio Parlamentare di Bilancio, Bank of Italy)
    Abstract: Can we use newspaper articles to forecast economic activity? Our answer is yes and, to this end, we propose a brand new economic dictionary in Italian with valence shifters, and we apply it to a corpus of about two million articles from four popular newspapers. We produce a set of high-frequency text-based sentiment and policy uncertainty indicators (TESI and TEPU respectively), which are constantly updated, not revised and computed both for the whole economy and for specific sectors or economic topics. To test the predictive power of our text-based indicators, we propose two forecasting exercises. First, by using Bayesian Model Averaging (BMA) techniques, we show that our monthly text-based indicators greatly reduce the uncertainty surrounding the short-term forecasts of the main macroeconomic aggregates, especially during recessions. Secondly, we employ these indices in a weekly GDP growth tracker, achieving sizeable gains in forecasting accuracy in both normal and turbulent times.
    Keywords: Forecasting, Text Mining, Sentiment, Economic Policy Uncertainty, Big data, BMA.
    JEL: C11 C32 C43 C52 C55 E52 E58
    Date: 2021–03
  4. By: Gabriel Borrageiro; Nick Firoozye; Paolo Barucca
    Abstract: We investigate the benefits of feature selection, nonlinear modelling and online learning with forecasting in financial time series. We consider the sequential and continual learning sub-genres of online learning. Through empirical experimentation, which involves long term forecasting in daily sampled cross-asset futures, and short term forecasting in minutely sampled cash currency pairs, we find that the online learning techniques outperform the offline learning ones. We also find that, in the subset of models we use, sequential learning in time with online Ridge regression, provides the best next step ahead forecasts, and continual learning with an online radial basis function network, provides the best multi-step ahead forecasts. We combine the benefits of both in a precision weighted ensemble of the forecast errors and find superior forecast performance overall.
    Date: 2021–03
  5. By: Delle Monache, Davide (Bank of Italy); De Polis, Andrea (Univeristy of Warwick); Petrella, Ivan (Univeristy of Warwick)
    Abstract: We document a substantial increase in downside risk to US economic growth over the last 30 years. By modelling secular trends and cyclical changes of the predictive density of GDP growth, we find an accelerating decline in the skewness of the conditional distributions, with significant, procyclical variations. Decreasing trend-skewness, which turned negative in the aftermath of the Great Recession, is associated with the long-run growth slowdown started in the early 2000s. Short-run skewness fluctuations imply negatively skewed predictive densities ahead of and during recessions, often anticipated by deteriorating financial conditions, while positively skewed distributions characterize expansions. The model delivers competitive out-of-sample (point, density and tail) forecasts, improving upon standard benchmarks, due to the strong signals of increasing downside risk provided by current financial conditions.
    Keywords: business cycle, financial conditions, downside risk, skewness, score driven models.
    JEL: C12 C22 C51 C53 E37 E44
    Date: 2021–03
  6. By: Min Seong Kim (University of Connecticut)
    Abstract: In this paper, we propose the time-series average of spatial HAC estimators for the variance of the estimated common factors under the approximate factor structure. Based on this, we provide the con dence interval for the conditional mean of the dffusion-index forecasting model with cross-sectional heteroskedasticity and dependence of the idiosyncratic errors. We establish the asymptotics under very mild conditions, and no prior information about the dependence structure is needed to implement our procedure. We employ a bootstrap to select the bandwidth parameter. Simulation studies show that our procedure performs well in nite samples. We apply the proposed con dence interval to the problem of forecasting the unemployment rate using data by Ludvigson and Ng (2010).
    Keywords: Approximate factor structure, Bandwidth selection, Di¤usion index forecast, Ro-bust inference, Spatial HAC estimator
    JEL: C12 C31 C38
    Date: 2021–03
  7. By: Guoyou Yue (Guangxi Key Laboratory of Cross-border E-commerce Intelligent Information Processing, Guangxi University of Finance and Economics, China. Author-2-Name: Author-2-Workplace-Name: Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)
    Abstract: Objective - The objective of this paper is to establish the forecasting models of port cargo throughput and container throughput in Guangxi Beibu Gulf Port in the next 5 years, and to put forward the countermeasures of port logistics development in Guangxi Beibu Gulf Port according to the forecast results. Methodology/Technique - The data of cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2009-2020 are collected through the data of Guangxi Statistical Yearbook and Guangxi Statistical Bulletin. Based on 2019 and 2020, the forecasting models of cargo throughput and container throughput in Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou are establishe using a weighted moving average forecasting method. The cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2020/2021-2025 are predicted. Findings - The forecast results show that by 2025, the cargo throughput of Guangxi Beibu Gulf Port is expected to exceed 400 million tons, and the container throughput is expected to exceed 10 million TEU. According to the fitting diagram of forecast results and actual data, it can be seen that the accuracy of the forecast results is very high. Novelty - It is innovative to select 2 base years in 2019 and 2020 to establish forecasting model. Based on the comparative analysis of the forecast results, this paper puts forward various measures to promote the development of port logistics of Guangxi Beibu Gulf port, such as strengthening the construction of port self-condition, strengthening the co-ordinated development of port and economic hinterland, speeding up the construction of port collection and distribution system, training and introducing all kinds of high-quality port logistics talents.
    Keywords: Logistics Demand Forecast; Cargo Throughput Forecast; Container Throughput Forecast; Weighted Moving Average Forecasting Method; Guangxi Beibu Gulf Port
    JEL: C53 R41
    Date: 2021–03–31
  8. By: ; Todd E. Clark; Florian Huber; Gary Koop; Massimiliano Marcellino
    Abstract: We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a novel nonparametric specification. Estimation is carried out using scalable Markov chain Monte Carlo estimation algorithms for each specification. We evaluate the real-time density and tail forecasting performance of the various models for a set of US macroeconomic and financial indicators. Our results suggest that using nonparametric models generally leads to improved forecast accuracy. In particular, when interest centers on the tails of the posterior predictive, flexible models improve upon standard VAR models with SV. Another key finding is that if we allow for nonlinearities in the conditional mean, allowing for heteroskedasticity becomes less important. A scenario analysis reveals highly nonlinear relations between the predictive distribution and financial conditions.
    Keywords: nonparametric VAR; regression trees; macroeconomic forecasting
    JEL: C11 C32 C53
    Date: 2021–03–22

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