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Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Uncertainty attribution in water supply forecasting is crucial to improve forecast skill and increase confidence in seasonal water management planning. We develop a framework to quantify fractional forecast uncertainty and partition it between (1) snowpack quantification methods, (2) variability in post‐forecast precipitation, and (3) runoff model errors. We demonstrate the uncertainty framework with statistical runoff models in the upper Tuolumne and Merced River basins (California, USA) using snow observations at two endmember spatial resolutions: a simple snow pillow index and full‐catchment snow water equivalent (SWE) maps at 50 m resolution from the Airborne Snow Observatories. Bayesian forecast simulations demonstrate a nonlinear decrease in the skill of statistical water supply forecasts during warm snow droughts, when a low fraction of winter precipitation remains as SWE. Forecast skill similarly decreases during dry snow droughts, when winter precipitation is low. During a shift away from snow‐dominance, the uncertainty of forecasts using snow pillow data increases about 1.9 times faster than analogous forecasts using full‐catchment SWE maps in the study area. Replacing the snow pillow index with full‐catchment SWE data reduces statistical forecast uncertainty by 39% on average across all tested climate conditions. Attributing water supply forecast uncertainty to reducible error sources reveals opportunities to improve forecast reliability in a warmer future climate. [ABSTRACT FROM AUTHOR]

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research on clinical versus statistical prediction has demonstrated that algorithms make more accurate predictions than humans in many domains. Geopolitical forecasting is an algorithm-unfriendly domain, with hard-to-quantify data and elusive reference classes that make predictive model-building difficult. Furthermore, the stakes can be high, with missed forecasts leading to mass-casualty consequences. For these reasons, geopolitical forecasting is typically done by humans, even though algorithms play important roles. They are essential as aggregators of crowd wisdom, as frameworks to partition human forecasting variance, and as inputs to hybrid forecasting models. Algorithms are extremely important in this domain. We doubt that humans will relinquish control to algorithms anytime soon—nor do we think they should. However, the accuracy of forecasts will greatly improve if humans are aided by algorithms. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study focused on examining the dynamic and causal relationships among selected world stock market indices. The growing importance of dynamic interactions among the stock indices have emerged as a focal point of research, driven by the recognition that financial markets are interconnected and interdependent. To achieve the above stated objective, multivariate vector autoregressive (VAR) approach is applied. For which the monthly time series data on the selected indices are obtained for the period 2010-2024. The required data was sourced from yahoofinance.com and the analysis was conducted using RStudio software version 2022.12.0+353 and MS Excel. We found that almost all the indices exhibit a positive and significant impact from their own past values on their future values and they are expected to increase over the next eight months. The impulse response function analysis reveals that only shocks in NSE are positively influenced by their own past values, along with the past values of other indices. Additionally, the FEVD test results indicate that most of the variance in each index is attributable to their own shocks, with the exception of TSX and DAX.

Romania faced the highest inflation rate in the European Union at the beginning of 2024, but progress has been made compared to that in 2023 due to the increasing interest rate. This inflation stemmed from a combination of global and domestic factors (global factors such as the Russia-Ukraine war, supply chain disruptions caused by the COVID-19 pandemic and war, rising commodity prices, domestic factors such as wage and pension increases, tax and charge hikes, and a strategy of gradual increase in the monetary policy interest rate). The National Bank of Romania (NBR) uses a combination of monetary policy instruments to target inflation and provides quarterly forecasts. However, under uncertain conditions, numerical forecasts are less reliable, and the inclusion of sentiment analysis in forecasts might lead to innovation in the field by improving the prediction accuracy. Sentiment analysis has become increasingly important in the field of economics, offering valuable insights and potentially improving economic forecasting and decision-making due to rapid technological progress. Sentiment analysis can identify potential changes in consumer behaviour and business decisions before they are translated into actual economic data, providing an early warning system for economic trends and potential crises. The methodological background relies on natural language processing to extract sentiment indices for large amounts of texts in Inflation Reports provided by NBR. Moreover, the sentiment indices calculated by IntelliDocker are incorporated into autoregressive distributed lag (ARDL) models to provide quarterly inflation forecasts. This type of econometric model has the advantage of addressing endogeneity. Moreover, the unemployment rate is considered an inflation predictor since tensions in the labour market might impact inflation. This paper contributes to empirical forecasting by proposing sentiment forecasts that are more accurate than NBR numerical forecasts corresponding to the 2006: Q1-2023: Q4 horizon. The new forecasting method might be used to make inflation predictions for the next quarters. More accurate forecasts would be valuable for businesses, the central bank, policymakers, and the general public. However, while sentiment analysis offers valuable insights, it is important to remember that human judgment and expertise remain essential for interpreting the data and making informed economic decisions.

Copyright of Materiały Budowlane is the property of Wydawnictwo SIGMA-NOT and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract This study examines the potential economic and labour market impacts of a hypothetical but plausible migration scenario of 250,000 new migrants inspired by Austria’s experience in 2015. Using the agent-based macroeconomic model developed by Poledna et al. (Eur Econ Rev, 151:104306, 2023. 10.1016/j.euroecorev.2022.104306, the study explores the detailed labour market outcomes for different groups in Austria’s population and the macroeconomic effects of the migration scenario. The analysis suggests that Austria’s economy and labour market have the potential to be resilient to the simulated migration influx. The results indicate a positive impact on GDP due to increased aggregate consumption and investment. The labour market experiences an increase in the unemployment rates of natives and previous migrants. In some industries, the increase in the unemployment rates is more significant, potentially indicating competition among different groups of migrants. This research provides insights for policymakers and stakeholders in Austria and other countries that may face the challenge of managing large-scale migration in the near future.

Scarce information and human capital may make it difficult for residents of developingcountries to produce accurate forecasts, limiting responses to uncertain future eventslike air pollution. We study two randomized interventions in Lahore, Pakistan: 1)provision of air pollution forecasts; 2) general training in forecasting. Both reducedsubjects’ own air pollution forecast errors; the training effect suggests that modesteducational interventions can durably improve forecasting skills. Forecast receiptincreased demand for protective masks and increased the responsiveness of outdoortime to pollution. Forecast recipients were willing to pay 60 percent of the cost ofmobile internet for continued access.

This work aims to define a set of representative weather patterns for South Africa that can be utilized to support impact‐based forecasting of heatwave events. Sets of weather patterns have been generated using k‐means clustering on daily ERA5 reanalysis data between 1979 and 2020. Different pattern sets were generated by varying the clustering atmospheric variable, the spatial domain and the number of weather patterns. These weather patterns are evaluated using the explained variation score to assess their ability to represent the variability of the maximum daily 2m temperature (Tmax,2m). The results indicate that a set of 30 weather patterns generated using mean sea‐level pressure, with a clustering domain in the range 15°–34°E and 21°–36°S, provides a reasonable representation of Tmax,2m variability across South Africa. The implementation of an appropriate weather pattern set into a medium‐range forecasting tool has the potential to extend the prediction of high‐impact weather events in South Africa, such as heatwaves, and also highlight specific impacts on the population, for example, food and water insecurity, heat exhaustion or energy and transport impacts. [ABSTRACT FROM AUTHOR]

Romania faced the highest inflation rate in the European Union at the beginning of 2024, but progress has been made compared to that in 2023 due to the increasing interest rate. This inflation stemmed from a combination of global and domestic factors (global factors such as the Russia-Ukraine war, supply chain disruptions caused by the COVID-19 pandemic and war, rising commodity prices, domestic factors such as wage and pension increases, tax and charge hikes, and a strategy of gradual increase in the monetary policy interest rate). The National Bank of Romania (NBR) uses a combination of monetary policy instruments to target inflation and provides quarterly forecasts. However, under uncertain conditions, numerical forecasts are less reliable, and the inclusion of sentiment analysis in forecasts might lead to innovation in the field by improving the prediction accuracy. Sentiment analysis has become increasingly important in the field of economics, offering valuable insights and potentially improving economic forecasting and decision-making due to rapid technological progress. Sentiment analysis can identify potential changes in consumer behaviour and business decisions before they are translated into actual economic data, providing an early warning system for economic trends and potential crises. The methodological background relies on natural language processing to extract sentiment indices for large amounts of texts in Inflation Reports provided by NBR. Moreover, the sentiment indices calculated by IntelliDocker are incorporated into autoregressive distributed lag (ARDL) models to provide quarterly inflation forecasts. This type of econometric model has the advantage of addressing endogeneity. Moreover, the unemployment rate is considered an inflation predictor since tensions in the labour market might impact inflation. This paper contributes to empirical forecasting by proposing sentiment forecasts that are more accurate than NBR numerical forecasts corresponding to the 2006: Q1-2023: Q4 horizon. The new forecasting method might be used to make inflation predictions for the next quarters. More accurate forecasts would be valuable for businesses, the central bank, policymakers, and the general public. However, while sentiment analysis offers valuable insights, it is important to remember that human judgment and expertise remain essential for interpreting the data and making informed economic decisions. [ABSTRACT FROM AUTHOR]

Purpose: This research interrogates how the construction of narratives and accounting forecasts contributes to managing the emotional state of actors involved in reporting meetings by promoting discourses of hope in their organization's future, mitigating their anxiety. This study shows how narratives are built from multiple antenarratives and accounting forecasts, which restore and strengthen organizational actors' commitment to their organizations. This study contributes to a better understanding of the role played by narratives and accounting documents in mitigating organizational members' anxiety. Design/methodology/approach: Over eight months, an interventionist research design method gave one of the authors the opportunity to record discussions held during reporting meetings in a business incubator. These recordings captured the production of narratives and forecasts in these meetings. Findings: This study shows how the production of multiple antenarratives and accounting forecasts helps organizational actors who attend reporting meetings mitigate the anxiety triggered by disappointing performance figures and restore collective discourses full of hope for the organization's future. This case highlights how personal antenarratives and successive versions of accounting forecasts contribute to restoring a collective commitment to a failing organization. Originality/value: This study refines current understanding of the under-explored links between accounting forecasts, narratives and anxiety management. The study provides insight into how accounting practices contribute to the production of narratives that successfully restore organizational members' commitment to working for a failing organization. The study also exemplifies the original insights gained from interventionist research protocols. [ABSTRACT FROM AUTHOR]

Motivated by research from the late 1990s and early 2000s, researchers often examine analyst performance as a function of individual analyst characteristics using variables representing analysts' experience, busyness, and resources. Studies make different choices for variable construction and fixed effects, sometimes with varying results, suggesting there is no standard approach to evaluating the effect of analyst characteristics. We reproduce analyses from two early studies (Clement (1999) and Clement and Tse (2003)) and extend these analyses to a more recent sample period and across different methodological approaches and alternative analyst performance measures. We provide robust evidence that analyst experience is positively associated with earnings forecast accuracy but that associations for analyst resources and busyness are sensitive to variable measurement and fixed effect choices. Our results can inform accounting and finance scholars examining analyst performance as they consider which analyst characteristics to include and how to test the robustness of their findings. [ABSTRACT FROM AUTHOR]

Copyright of Energy Policy Journal / Polityka Energetyczna is the property of Mineral & Energy Economy Research Institute of the Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examines the potential economic and labour market impacts of a hypothetical but plausible migration scenario of 250,000 new migrants inspired by Austria's experience in 2015. Using the agent-based macroeconomic model developed by Poledna et al. (Eur Econ Rev, 151:104306, 2023. 10.1016/j.euroecorev.2022.104306, the study explores the detailed labour market outcomes for different groups in Austria's population and the macroeconomic effects of the migration scenario. The analysis suggests that Austria's economy and labour market have the potential to be resilient to the simulated migration influx. The results indicate a positive impact on GDP due to increased aggregate consumption and investment. The labour market experiences an increase in the unemployment rates of natives and previous migrants. In some industries, the increase in the unemployment rates is more significant, potentially indicating competition among different groups of migrants. This research provides insights for policymakers and stakeholders in Austria and other countries that may face the challenge of managing large-scale migration in the near future. [ABSTRACT FROM AUTHOR]

Scoring rules measure the deviation between a forecast, which assigns degrees of confidence to various events, and reality. Strictly proper scoring rules have the property that for any forecast, the mathematical expectation of the score of a forecast p by the lights of p is strictly better than the mathematical expectation of any other forecast q by the lights of p. Forecasts need not satisfy the axioms of the probability calculus, but Predd et al. [9] have shown that given a finite sample space and any strictly proper additive and continuous scoring rule, the score for any forecast that does not satisfy the axioms of probability is strictly dominated by the score for some probabilistically consistent forecast. Recently, this result has been extended to non-additive continuous scoring rules. In this paper, a condition weaker than continuity is given that suffices for the result, and the condition is proved to be optimal. [ABSTRACT FROM AUTHOR]

The Italian territory is located at the heart of one of the global hot spots of climate change, where the implementation of climate‐smart land management practices is imperative to guarantee the present and future maintenance of ecosystem functions as well as the sustainability of human socioeconomic activities. The project HIGHLANDER (HIGH performance computing to support smart LAND sERvices) led by Cineca aims at building a comprehensive and multi‐sector framework for land‐management decision‐making in Italy. The project relies on high quality information on different components of the landscape, with a focus on climate‐driven processes, and state‐of‐the‐art computing infrastructures. The HIGHLANDER Data Portal maximizes the impact of HIGHLANDER results by providing access to data products and services. In this article, we describe the architectural features of the platform, as well as the available HIGHLANDER datasets and downstream applications. [ABSTRACT FROM AUTHOR]

We construct a distraction measure based on extreme industry returns to gauge whether analysts' attention is away from certain stocks under coverage. We find that temporarily distracted analysts make less accurate forecasts, revise forecasts less frequently, and publish less informative forecast revisions, relative to undistracted analysts. Further, at the firm level, analyst distraction carries real negative externalities by increasing information asymmetry for stocks that suffer from a larger extent of analyst distraction during a given quarter. Our findings thus augment our understanding of the determinants and effects of analyst effort allocation and broaden the literature on distraction and information spillover in financial markets. [ABSTRACT FROM AUTHOR]

Sweet potato is one of the important tropical tuber crops across the world. This review analyzes the studies on the climate resilience of sweet potato to various environmental parameters, the impact of climate change on its yield, and geographical suitability to exploit more about this crop and find out the knowledge gap in its existing research. Drought tolerance studies indicate the suitability of sweet potato in drought‐prone areas in terms of their survival rate, and their leaves can tolerate up to a wilting point of −1.3 MPa. The optimum night and daytime temperatures for sweet potato are 18°C and 28°C. There are 82 crop varieties available globally with heat‐stress tolerance without compromising the yield. The elevated atmospheric CO2 enhanced the growth and yield of sweet potato and increased the water use efficiency. The salinity tolerance of sweet potato is observed as 11 dS m−1, and it is superior to other major food crops. Sweet potato's ozone tolerance is higher than other crops (59 ppb). The impact studies on crop yield and geographical suitability also recommend its superiority to other major food crops with similar crop duration. As an outcome of this study, further research is needed to explore this crop as sweet potato can substitute for major food grain crops in developing countries regarding food, feed, medicine, and fuel. Research on value‐added products and their marketing is another area that needs immediate attention. Studies must exploit sweet potato leaves to ensure nutritional security in developing countries. The researchers also need to focus on developing climate‐resilient varieties to ensure a continuous supply of planting materials to needy people to ensure food security. This review also demands more studies on the modeling of sweet potato worldwide to issue crop yield forecasts and monitor the spatial distribution of its major pests worldwide. [ABSTRACT FROM AUTHOR]

This study investigates whether investors can reap economic benefits from analyzing differences in analyst quality. Although high-quality analysts' average forecast is more accurate than the consensus forecast for firms with a large analyst following, the benefits of using high-quality analysts' average forecasts are not economically significant. In contrast, the value of analyst quality differentiation exists in the second moment of forecasts. High-quality analysts' forecast dispersion gives investors an advantage in dealing with uncertainty by predicting return volatility and providing opportunities for economically significant returns using option straddle and post-earnings announcement drift investment strategies. This paper was accepted by Suraj Srinivassan, accounting. Funding: A. Rubin and A. Vedrashko thank the financial support of the Social Sciences and Humanities Research Council of Canada (SSHRC). Supplemental Material: The data are available at https://doi.org/10.1287/mnsc.2023.4699. [ABSTRACT FROM AUTHOR]

This paper explores the economic consequences of demographic changes on development in 208 countries through 2100. We employ panel fixed effects models to estimate long-run equilibrium relationships between capital stock, labor force, and economic growth. Cointegration analysis confirms the existence of these long-run relationships. We affirm that there is a steady, long-run relationship between GDP, capital stock, and population. We also present our findings in an interactive web application, allowing users to explore how population shifts might impact economies worldwide. Highlighting key trends, we offer an illustrative case study of East Asia, while emphasizing the tool's universal applicability. Thus, this work does not seek to engage in specific academic discourse, but rather endeavors to contribute a valuable resource for informed public dialogue concerning the global economic implications of anticipated demographic transformations. The results show that the "Comparative Global Weights Forecasts" section showcases economic clout, illustrating Japan's decline and Indonesia's rise in global GDP rankings. Lastly, the "Comparative Regional Forecasts" section contrasts regional dynamics, such as East Asia & the Pacific versus South Asia, highlighting population peaks, economic growth disparities, and shifts in global GDP shares.

In this paper, several financial indicators at S.C.D.A. Simnic are investigated from a statistical point of view. Using the method of least squares, the mathematical functions that model the trend are found and with the help of which financial forecasts are made, also using the growth rates with a chain basis and a fixed basis for the time series corresponding to the period 20082022. Finally, an analysis is made of the correlations between these indicators, such as income, expenses, profit, assets, liabilities, stocks, receivables, capital and the results found are interpreted. Thus, the forecasts on the financial indicators and the correlations between them can be of great help in the management of a company, being able to contribute to the efficiency of the activity through adequate budget planning, performance evaluation, investment decision-making or human resources planning. They can help managers identify opportunities and risks, optimize the use of resources, and achieve better financial results for the firm. financial indicators, forecasts, time series, correlations

Multivariate time series forecasting has an important role in many real-world domains. Especially, price prediction has always been on the focus of researchers. Yet, it is a challenging task that requires the capturing of intra-series and inter-series correlations. Most of the models in literature focus only on the correlation in temporal domain. In this paper, we have curated a new dataset from the official website of Turkish Ministry of Commerce. The dataset consists of daily prices and trade volume of vegetables and covers 1791 days between January 1, 2018 and November 26, 2022. A Spectral Temporal Graph Neural Network (StemGNN) is employed on the curated dataset and the results are given in comparison to Convolutional neural networks (CNN), Long short-term memory (LSTM) and Random Forest models. GNN architecture achieved a state-of-the-art result such as mean absolute error (MAE): 1,37 and root mean squared error (RMSE): 1.94). To our knowledge, this is one of the few studies that investigates GNN for time series analysis and the first study in architecture field. [ABSTRACT FROM AUTHOR]

In this paper, a statistical analysis is made of the health personnel in Romania in the public and private systems using data from the period 1990-2022, provided by the National Institute of Statistics. Using the least squares method, analytical functions are found that model the trend and forecasts are made for the years 2023 and 2024. The confidence intervals for the predictions are also found, at a significance threshold of 5%, and the correlations between certain indicators are analysed. It was found that in the public sector there was a downward trend, in general, for all categories of medical personnel in the period 1990-2017, followed by an increasing evolution after 2018, due to salary increases in the system. However, there is still a shortage of physicians in the public sector, even if their number has steadily increased in the last years. There is no correlation between the number of physicians and the number of beds in public hospitals, although this should be positive and high. On the other hand, in the private sector the trend is obviously increasing throughout the studied period, the correlations are very strong between the analysed indicators, a situation explained by the fact that in this system the goal is to minimize expenses and increase profit and less access of all citizens to health services. [ABSTRACT FROM AUTHOR]

Analysts' perspectives on economic risk are a crucial element in the investor decision-making process. Several researchers have immersed themselves in analyst forecasts and stock prices. However, it is imperative to examine the consistency of findings from previous studies in order to draw general conclusions. This analysis is necessary to establish whether analysts' forecasts actually influence stock market returns. Consistency between studies was assessed using a meta-analysis approach, facilitated by Comprehensive Meta-Analysis (CMA) software. Metaanalysis provides an effect size estimate to assess the relationship between the independent variable (analysts' forecasts) and the dependent variable (stock market returns). The results of the heterogeneity test indicate an Isquared value in excess of 50%, signifying substantial variation. Similarly, the correlation analysis reveals a p-value of 0.004, suggesting heterogeneity in the studies of analyst forecasts and stock market returns. Heterogeneity refers to disparities in data within or between studies, attributable to varying research locations and economic conditions. Consequently, the results of this study are heterogeneous due to significant sampling error arising from various research locations and economic circumstances. Thus, it is demonstrated that analysts' forecasts have an impact on stock market returns. [ABSTRACT FROM AUTHOR]

Aim: To analyze and summarize the biological indicators of the main industrial fish and river crayfish species in the Zaporizhzhia (Dnipro) reservoir under conditions of fishery exploitation. Methods: Biological analysis of fish was conducted according to classical methods in ichthyology, using the following parameters: standard and absolute body length, individual mass, gender, feeding coefficient. Crayfish, caught in fishing gear, underwent morphometric measurements to determine individual mass and gender. Biological sampling was carried out during the vegetation period of 2023 during both control and commercial fishing in the Zaporizhzhia (Dnipro) reservoir at three sites differing in hydrology and degree of anthropogenic impact. Research results: According to the data from the Department of the State Agency of Reclamation and Fisheries Management in Dnipropetrovsk Oblast, during 2023, 140,504 tons of fish were caught in the Zaporizhzhia (Dnipro) reservoir, nearly 8 times less than in 2021. In 2021, 1078.25 tons of aquatic resources were caught, which is 8 % less than in 2020. Silver carp was the dominant fish species in 2022, accounting for 80.5 % of the total catch in the reservoir. Herbivorous fish species accounted for 5.4 % of the catch, roach - 3.3 %, bream - 2.3 %, perch - 2.0 %. Based on the conducted research, recommendations have been provided regarding the establishment of forecasts and limits for the extraction of aquatic resources from the Zaporizhzhia (Dnipro) reservoir for the year 2024. Conclusions: The Zaporizhzhia (Dnipro) reservoir has potential for the development of fishing, fish farming, and aquaculture in the Prydniprovsky region. The research results suggest that limits can be set on the catch of certain fish species: bream-up to 210 tons, pike-perch - up to 42 tons, roach - up to 350 tons, and perch - up to 138 tons. However, the catch of silver carp, bleak, sprat, herbivorous fish, and sun-perch is recommended to be unrestricted. The forecasted catch of crayfish is within 1 ton. The catch of other aquatic resources should also be conducted within forecasted levels. [ABSTRACT FROM AUTHOR]

The spread of COVID-19 in the entire world has put humankind in danger. The assets of probably the biggest economies are worried because of the enormous infectivity and contagiousness of this illness. The ability of machine learning algorithms to predict the number of possible COVID-19 patients is generally seen as a potential challenge to mankind. The undermining components of COVID-19 were determined using four normal estimating models: Support Vector Machine (SVM), least total shrinkage, and determination administrator (LASSO), linear regression (LR). Any one of the models makes three types of predictions, such as the number of newly infected occurrences, the number of passings, and the rate of recoveries, but they cannot predict the exact result for the patients. To defeat the issue, the Proposed strategy utilizing exponential smoothing (ES) The number of cases of COVID-19 and the impact of COVID-19 preventive steps including certain social insulation and latch on infectious diseases was expected in the next 30 days to come. [ABSTRACT FROM AUTHOR]

There are currently no quantitative short-term eruption forecasts based on peer-reviewed and validated models that are operational for New Zealand's volcanoes. Specific forecasts produced for work-risk assessments are not generally publicised. During a volcanic crisis, eruption forecasts are demanded under high stress and time-restricted conditions. Many forecasting options exist but none are proven as universally viable, with testing and calibration limited to the hindcasting of specific events. Here, we compare the requirements of six methods with currently available data and monitoring capabilities at each of New Zealand's volcanoes to determine which methods are currently feasible, as well as those options that may be implemented with additional effort or equipment. In New Zealand, the major limiting factor in method selection is the low number of past instrumentally monitored eruptions. This data gap may be filled by carefully selected analogue data from a global volcano set and expert knowledge. Event trees and the failure forecasting method may be set up at most volcanoes with minimal effort, but the latter can only forecast eruption onset time. Expert interpretation is the only method available in New Zealand for any forecast output type. [ABSTRACT FROM AUTHOR]

We offer novel evidence on how the nature of brokerage-client relationships can influence the quality of equity research. We exploit a unique setting provided by the Protocol for Broker Recruiting to examine whether relaxed broker noncompete agreement enforcement generates spillover effects on sell-side analysts. Entry into this agreement reassigns ownership of the client relationship from the brokerage to individual brokers, potentially generating a greater standard of care. Using a generalized difference-in-differences research design, we provide evidence consistent with brokers reducing pressure on analysts to produce optimistic research following protocol entry. This effect is concentrated among less experienced and non-All Star analysts, who previously may have faced the greatest pressures to sacrifice objectivity. Additionally, we find that analysts issue more accurate forecasts and generate reports with heightened market reactions following protocol entry. Our collective evidence sheds new light on how the nature of brokerage relationships can influence analysts' research production. [ABSTRACT FROM AUTHOR]

Abstract The Italian territory is located at the heart of one of the global hot spots of climate change, where the implementation of climate‐smart land management practices is imperative to guarantee the present and future maintenance of ecosystem functions as well as the sustainability of human socioeconomic activities. The project HIGHLANDER (HIGH performance computing to support smart LAND sERvices) led by Cineca aims at building a comprehensive and multi‐sector framework for land‐management decision‐making in Italy. The project relies on high quality information on different components of the landscape, with a focus on climate‐driven processes, and state‐of‐the‐art computing infrastructures. The HIGHLANDER Data Portal maximizes the impact of HIGHLANDER results by providing access to data products and services. In this article, we describe the architectural features of the platform, as well as the available HIGHLANDER datasets and downstream applications.

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models.

This review describes advances in understanding and forecasting tropical cyclone (TC) subseasonal variability during the past four years. A large effort by the scientific community has been in understanding the sources of predictability at subseasonal timescales beyond the well-known modulation of TC activity by the Madden-Julian Oscillation (MJO). In particular, the strong modulation of TC activity over the western North Pacific by the Boreal Summer Intra-Seasonal Oscillation (BSISO) has been documented. Progress has also been realized in understanding the role of tropical-extratropical interactions in improving subseasonal forecasts. In addition, several recent publications have shown that extratropical wave breaking may have a role in the genesis and development of TCs. Analyses of multi-model ensemble data sets such as the Subseasonal to Seasonal (S2S) and Subseasonal Experiment (SubX) have shown that the skill of S2S models in predicting the genesis of TCs varies strongly among models and regions but is often tied to their ability to simulate the MJO and its impacts. The skill in select models has led to an increase over the past four years in the number of forecasting centers issuing subseasonal TC forecasts using various techniques (statistical, statistical-dynamical and dynamical). More extensive verification studies have been published over the last four years, but often only for the North Atlantic and eastern North Pacific.

Marine ecosystems face many consequential pressures. Yet, we lack an integrative and predictive capacity to understand how marine ecosystems will respond to the cumulative impacts of these pressures, including climate change. It is not enough to detect responses after the fact; it has become imperative to know in advance where major biological resources or hazards will occur, when they will peak, and how that will impact economic performance. Although forecasts exist for some components of marine ecosystems, these are disparate and suffer from a lack of coordination. There is a need for coordinated, cross-ecosystem scale, integrated, marine ecosystem predictions and synthesis products. The value proposition relative to the blue economy is quite high, positively influencing billions if not trillions of marine sector dollars. [ABSTRACT FROM AUTHOR]

Abstract This is the first empirical study that analyzes the nature of business cycles in India using quarterly GDP growth data from 1998Q1 to 2022Q4 and the Markov Switching Auto-regressive model. In order to capture the impact of exclusion and inclusion of post pandemic period data, it has developed two alternative models. Based on these models, the study provides the forecast of quarterly GDP growth and cumulative real output losses due to the pandemic. It also provides alternative scenarios and trajectories of the Indian economy towards the US$ 5 trillion milestone. It is our hope that these results will be useful to policymakers and other stakeholders to understand the business cycle issue in India and take appropriate strategies to overcome the problems associated with these cycles. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is undoubtedly a global crisis that has forced the world economy to a standstill. Subsequent lockdowns have caused downtime in all industries and all transportation sectors. The removal of the restrictions has made it possible to begin a slow return to pre-pandemic conditions, but research indicates that this will be a long process. Therefore, an indication of the development trends of passenger maritime transport in Poland, considering the impact of the COVID-19 pandemic, is the purpose of the article. Two specific objectives are identified: (1) To visualize the impact of the COVID-19 pandemic on passenger maritime transport in Poland; (2) To make long-term forecasts of passenger maritime traffic in Poland. The analyses showed that the COVID-19 pandemic had a very negative impact on passenger streams. It may take several more years to recover from the pre-pandemic state. [ABSTRACT FROM AUTHOR]

USDA invests significant public resources into developing its crop projection reports. These publications inform decisions across the supply chain. Several previous studies find that revisions to the department's production and yield forecasts for major agricultural commodities are positively correlated and conclude that they deviate from what would be observed under rational expectations, possibly due to smoothing on the part of forecasters. Yet correlated revisions may also be explained by information rigidities that cause forecasts to be infrequently or only partially updated. We apply a recently developed test to these USDA revisions for corn, soybeans, and wheat, and find no significant evidence that the forecasts are smoothed strategically. Rather, we show that information rigidities are the more likely culprit, due to production and yield information that is either too costly to obtain or too noisy. Our results demonstrate that data challenges are the main source of inefficiency in USDA projections, and that the department can improve the efficiency of its forecasts by making investments that improve its access to crop data, perhaps through crop‐monitoring satellite and remote sensing technology. [ABSTRACT FROM AUTHOR]

The purpose of this explanatory research was to understand how firms align strategic planning and budgeting both ex ante and ex post. After the literature review indicated that there was a shortcoming in explaining how the alignment was done, we interviewed management accountants at 20 large, profitable, stock-market listed firms with head offices in the Toronto area of Canada. To understand practice through interviews, we used qualitative, multi-case field research to address our research question, how do firms achieve alignment between their strategic plans and budgets, both ex ante and ex post? Our findings and contribution were that, rather than multiple processes (strategy, strategic planning, budgeting, and forecasting), strategic planning and budgeting are part of a single process. Alignment of strategic planning and budgeting is undertaken prior to the beginning of the fiscal year (ex ante) and during the fiscal year (ex post). Both provide opportunities to change ineffective strategies, strategic plans, and actions to minimize financial harm. Ex ante and ex post alignments enable the accomplishment of firms' financial objectives through explicit and verifiable decisions. With forecasting heretofore being an unclear and ambiguous subprocess, this chapter has made it transparent and manageable in assisting with accomplishing the strategy, strategic plan, and budget. [ABSTRACT FROM AUTHOR]

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models. [ABSTRACT FROM AUTHOR]

The Brazilian civil aviation sector suffered significant impacts on its activities as a result of the COVID-19 pandemic. Thus, the present work aimed to evaluate the impact of the first year of the pandemic on the movement of cargo and passengers at Brazilian airports. For this, historical data (time series) of cargo and passenger movements of the Horus system of the National Civil Aviation Secretariat were collected, covering the period from January 2004 to February 2020. Then, descriptive statistics were obtained and the patterns of the two series. Afterwards, the performances of five forecast models (two individual and three combination) were evaluated to obtain future values of the time series from March 2020 to February 2021. It was possible to observe that the Additive Holt-Winters model was the best model. more accurate for predicting future cargo movement. In addition, it was possible to verify that the SARIMA (0,1,1)(0,1,1)12 model was the most recommended for predicting the future movement of passengers at Brazilian airports. Finally, it was found that the observed volume of cargo handling was 28.55% lower than the volume predicted by the Additive Holt-Winters model for the period from March 2020 to February 2021). The observed volume of passenger movement was 63.41% lower than the volume predicted by the SARIMA (0,1,1)(0,1,1)12 model for the same period. [ABSTRACT FROM AUTHOR]

Forecasts on transportation meteorology, such as pavement temperature, are becoming increasingly important in the face of global warming and frequent disruptions from extreme weather and climate events. In this study, we propose a pavement temperature forecast model based on stepwise regression—model output statistics (SRMOS) at the short-term timescale, using highways in Jiangsu, China, as examples. Experiments demonstrate that the SRMOS model effectively calibrates against the benchmark of the linear regression model based on surface air temperature (LRT). The SRMOS model shows a reduction in mean absolute errors by 0.7–1.6 °C, with larger magnitudes observed for larger biases in the LRT forecasts. Both forecasts exhibit higher accuracy in predicting minimum nighttime temperatures compared to maximum daytime temperatures. Additionally, it overall shows increasing biases from the north to the south, and the SRMOS superiority is greater over the south with larger initial LRT biases. Predictor importance analysis indicates that temperature, moisture, and larger-scale background are basically the key predictors in the SRMOS model for pavement temperature forecasts, of which the air temperature is the most crucial factor in the model's construction. Although larger-scale circulation backgrounds are generally characterized by relatively low importance, their significance increases with longer lead times. The presented results demonstrate the considerable skill of the SRMOS model in predicting pavement temperatures, highlighting its potential in disaster prevention for extreme transportation meteorology events. [ABSTRACT FROM AUTHOR]

Analysts' street earnings forecasts are sometimes based on GAAP earnings and sometimes based on non-GAAP earnings, which exclude various GAAP earnings components. Therefore, differences in analysts' street earnings forecasts may capture differences in not only expected performance but also the earnings metric forecasted. We argue that analysts who forecast non-GAAP, rather than GAAP, street earnings are more likely to separately analyze earnings components. Consistent with this argument, we find that analysts who forecast non-GAAP street earnings issue relatively more accurate forecasts. We also argue that excluded earnings components often reflect negative transitory items, and that variation across analysts in the earnings metric forecasted suggests that the negative excluded items are forecasted by only a subset of analysts. Consistent with this assertion, we find that variation across analysts in the earnings metric forecasted is associated with a lower consensus GAAP earnings surprise and lower stock returns around the earnings announcement. Finally, although variation in the earnings metric forecasted is a source of analyst forecast dispersion, we find that it is also incrementally associated with a lower earnings response coefficient, consistent with the existence of transitory items. We therefore find that the variation in the earnings metric forecasted is an important source of analyst forecast dispersion that predicts not only a lower earnings surprise but also a lower earnings response. This paper was accepted by Brian Bushee, accounting. Funding: Financial support from the Gatton College of Business & Economics, the KPMG Fellowship at the Mendoza College of Business, and the Goizueta Business School is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4553. [ABSTRACT FROM AUTHOR]