Unified ensemble mean forecasting of tropical cyclones based on the feature-oriented mean method

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged.We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Simulating the Long-Term Impacts of the COVID-19 Pandemic on the Sustainability of the Population-Economy-Environment Nexus

The COVID19 pandemic has created a massive shock, unexpectedly increasing mortality levels and generating economic recessions all around the world. In recent years, several efforts have been made to develop models that link the environment, population and the economy which may be used to estimate potential longer term effects of the pandemic. Unfortunately, many of the parameters used in these models lack appropriate empirical identification. In this study, first I estimate the parameters of “Wonderland”, a system dynamics model of the population-economy-environment nexus, and posteriorly, add external GDP and mortality shocks to the model. The estimated parameters are able to closely match world data, while future simulations point, on average and regardless of the COVID19 pandemic, to a world reaching dangerous environmental levels in the following decades, in line with consensus forecasts. On the other hand, the effects of the pandemic on the economy are highly uncertain and may last for several decades.

Interest rate forecasts in Latin America

PurposeThis paper aims to assess the quality of interest rate forecasts for the money markets in Argentina, Brazil, Chile, Mexico and Venezuela for the period between 2001 and 2019. Future interest rate trends are of key significance for many business-related decisions. Thus, reliable interest rate forecasts are essential, for example, for banks that make profits by carrying out maturity transformations.Design/methodology/approachThe data that we analyze were collected by Consensus Economics through a monthly survey with over 120 renowned economists and were published between 2001 and 2019 in the journal Latin American Consensus Forecasts. The authors use the Diebold-Mariano test, the sign accuracy test, the TOTA coefficient and the unbiasedness test to determine the precision and biasedness of the forecasts.FindingsThe research reveals that the forecasting work carried out in Brazil, Chile and Mexico is remarkably successful. The quality of forecasts from Argentina and Venezuela, on the other hand, is significantly poorer.Originality/valueOver 50 studies have already been published with regard to the accuracy of interest rate forecasts, emphasizing the importance of the topic. However, interest rate forecasts for Latin American money markets have hardly been considered thus far. The paper closes this research gap. Overall, the analyzed database amounts to a total of 209 forecast time series with 28,451 individual interest rate forecasts. This study is thus far more comprehensive than all previous studies.

Climatic features of the 2020/2021 winter season and the air temperature and precipitation outlook for the summer of 2021 over Northern Eurasia

Climatic features of the 2020/2021 winter season and the air temperature and precipitation outlook for the summer of 2021 over Northern Eurasia / Khan V.M., Vilfand R.M., Emelina E.V., Kaverina E.S., Kulikova I.A., Sumerova K.A., Tischenko V.A. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 6-19. The main features of the Northern Hemisphere large-scale atmospheric circulation are analyzed for the past 2020/2021 winter. The accuracy of consensus forecasts of air temperature and precipitation compiled during the work of the 19th session of the North Eurasian Climate Outlook Forum (NEACOF-19) is presented, with the skill scores of consensus forecasts for Northern Eurasia. The main features of the thermal state of the ocean and large-scale atmospheric circulation for the coming summer of 2021 are considered and analyzed. A forecast of surface air temperature and precipitation anomalies for the summer of 2021 agreed with the NEACOF-20 experts is formulated. Keywords: North Eurasian Climate Outlook Forum, North Eurasian Climate Center, consensus forecast, air temperature, precipitation, large-scale atmospheric circulation, hydrodynamic models, sea surface temperature

Modularity and composite diversity affect the collective gathering of information online

Many modern interactions happen in a digital space, where automated recommendations and homophily can shape the composition of groups interacting together and the knowledge that groups are able to tap into when operating online. Digital interactions are also characterized by different scales, from small interest groups to large online communities. Here, we manipulate the composition of groups based on a large multi-trait profiling space (including demographic, professional, psychological and relational variables) to explore the causal link between group composition and performance as a function of group size. We asked volunteers to search news online under time pressure and measured individual and group performance in forecasting real geo-political events. Our manipulation affected the correlation of forecasts made by people after online searches. Group composition interacted with group size so that composite diversity benefited individual and group performance proportionally to group size. Aggregating opinions of modular crowds composed of small independent groups achieved better forecasts than aggregating a similar number of forecasts from non-modular ones. Finally, we show differences existing among groups in terms of disagreement, speed of convergence to consensus forecasts and within-group variability in performance. The present work sheds light on the mechanisms underlying effective online information gathering in digital environments.

Does litigation change managers’ beliefs about the value of voluntarily disclosing bad news?

Research suggests that earnings-disclosure-related litigation causes managers to reduce subsequent disclosure, perhaps stemming from a belief that even their good faith disclosures will cause them trouble. This paper considers unexplored dimensions of disclosure and alternative channels of disclosure to provide additional evidence that speaks to how litigation shapes managers’ disclosure strategies. Consistent with Skinner (1994)’s classic legal liability hypothesis, we find that, while managers reduce and delay forecasts of positive earnings news following litigation, they increase the frequency and timeliness of their bad news forecasts. Moreover, many managers who were nonguiders prior to facing legal scrutiny begin guiding following litigation. Managers also maintain (if not increase) the information they provide via press releases and during conference calls following litigation. Supporting the notion that managers use disclosure to walk down expectations, additional analyses document an increase in the likelihood that lawsuit firms report earnings that beat consensus forecasts in the post-lawsuit period. Collectively, our evidence suggests that following litigation managers continue to view disclosure as a valuable tool that shapes their firms’ information environments and reduces expected legal costs. In so doing, it supports an important alternative viewpoint of how firms respond to litigation as well as the effectiveness of litigation as a disciplining mechanism.

Bitcoin Returns and the Frequency of Daily Abnormal Returns

This paper investigates the relationship between Bitcoin returns and the frequency of daily abnormal returns over the period from June 2013 to February 2020 using a number of regression techniques and model specifications including standard OLS, weighted least squares (WLS), ARMA and ARMAX models, quantile regressions, Logit and Probit regressions, piecewise linear regressions, and non-linear regressions. Both the in sample and out-of-sample performance of the various models are compared by means of appropriate selection criteria and statistical tests. These suggest that, on the whole, the piecewise linear models are the best, but in terms of forecasting accuracy they are outperformed by a model that combines the top five to produce “consensus” forecasts. The finding that there exist price patterns that can be exploited to predict future price movements and design profitable trading strategies is of interest both to academics (since it represents evidence against the EMH) and to practitioners (who can use this information for their investment decisions).

Application of Multimodel Superensemble Technique on the TIGGE Suite of Operational Models

One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance) for many variables such as rainfall, winds, geopotential height, temperature, and relative humidity. These weather forecasting centers have delivered near-real-time (with a delay of 48 hours) ensemble prediction system data to three TIGGE data archives since October 2006. This study is based on six years (2008–2013) of daily rainfall data by utilizing output from six centers, namely the European Centre for Medium-Range Weather Forecasts, the National Centre for Environmental Prediction, the Center for Weather Forecast and Climatic Studies, the China Meteorological Agency, the Canadian Meteorological Centre, and the United Kingdom Meteorological Office, and make consensus forecasts of up to 10 days lead time by utilizing the multimodal multilinear regression technique. The prediction is made over the Indian subcontinent, including the Indian Ocean. TRMM3B42 daily rainfall is used as the benchmark to construct the multimodel superensemble (SE) rainfall forecasts. Based on statistical ability ratings, the SE offers a better near-real-time forecast than any single model. On the one hand, the model from the European Centre for Medium-Range Weather Forecasting and the UK Met Office does this more reliably over the Indian domain. In a case of Indian monsoon onset, 05 June 2014, SE carries the lowest RMSE of 8.5 mm and highest correlation of 0.49 among six member models. Overall, the performance of SE remains better than any individual member model from day 1 to day 10.

Methodical approaches to forecasting and early detection of economic cyclicity

The analysis and generalization of theoretical preconditions of formation of the advancing economic indicators which at different times in different countries of the world are used as indicators of change of a phase of an economic cycle is carried out. Based on the analysis of literature sources, it is established that the methodology of economic forecasting of cyclicality in the scientific literature includes two main areas, namely: a) direct forecasting of the dynamics of key macroeconomic indicators based on extrapolation, correlation and regression analysis, expert surveys (consensus forecasts) and surveys of economic entities; b) the use of leading economic indicators, which by their nature can be pro-cyclical, counter-cyclical and acyclical, and in relation to the phase of the economic cycle - ahead, late and synchronous. The most common leading indicators are considered, among which the Index of leading economic indicators of the USA, Composite leading index of the Organization for Economic Cooperation and Development (OECD), Indicator of business confidence by types of economic activity, consumer confidence, business climate and economic sentiment in accordance with the Special Data Dissemination Standard of the International Monetary Fund. Since 2006, the National Bank of Ukraine has been conducting a survey on the status and prospects of business activity, and similar quarterly indicators have been developed by the State Statistics Service of Ukraine and implemented since 2013. At the same time, a separate indicator is proposed for forecasting business activity in construction - the indicator of business confidence in construction (IDVB). However, the feasibility of using these indicators has yet to be proven in practice. To increase the accuracy of forecasting changes in the phases of economic cycles, it is proposed to develop separate indicators at the sectoral and regional levels. Theoretical generalization and substantiation of causes, consequences, methods of counteracting economic cyclicality should become the basis for further applied research. This approach is a theoretical basis for the development of tools for countercyclical management of enterprises, sectors of the economy and national economies in conditions of fluctuations in business activity.

Overreaction in Macroeconomic Expectations

We study the rationality of individual and consensus forecasts of macroeconomic and financial variables using the methodology of Coibion and Gorodnichenko (2015), who examine predictability of forecast errors from forecast revisions. We find that individual forecasters typically overreact to news, while consensus forecasts under-react relative to full-information rational expectations. We reconcile these findings within a diagnostic expectations version of a dispersed information learning model. Structural estimation indicates that departures from Bayesian updating in the form of diagnostic overreaction capture important variation in forecast biases across different series, yielding a belief distortion parameter similar to estimates obtained in other settings. (JEL C53, D83, D84, E13, E17, E27, E47)