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This paper aims to extend the small body of literature on energy industry transitions on firm level. A growing number of articles shed light on paradigm shifts in the energy industry and the influence of renewable energies on industry structures. In the majority of cases, the authors analyze changes on a global or national level.

Energy companies’ forecasting capabilities are particularly important to enable them to react in time to upcoming changes in industry structures. In this context, we analyze annual reports of German energy companies to evaluate their economic and technological forecasting competencies.

Big energy providers offer high economic forecasting quality, but seem to be less able to derive valid forecasts in terms of renewable energies from the currently unstable political frameworks. On the contrary, renewable energy companies do not seem to suffer from these difficulties and provide good foresting accuracy in terms of renewable energy development, but show less accurate economic forecasting quality.

Big energy providers need to find the means of responding to the challenges and integrate changing political guidelines and support into their forecasting system. Renewable energy companies, in contrast, should focus on company-level profitability and the respective economic forecasting competencies.

This paper makes a significant contribution to the literature on the subject of energy industry transitions by providing insights from publicly available data on firm level. The findings are highly relevant for managers of the energy industry and policy makers in this field.

The purpose of this paper is to evaluate accuracy of macro fiscal forecasts done by Government of Zimbabwe and the spillover effects of forecasting errors over the period 2010-2015.

In line with the study objectives, the study employed the root mean square error methodology to measure the accuracy of macro fiscal forecasts, borrowing from the work of Calitz et al. (2013). The spillover effects were assessed through running simple regression in Eviews programme. The data used in the analysis are based on annual national budget forecasts presented to the Parliament by the Minister of Finance. Actual data come from the Ministry of Finance budget outturns and Zimbabwe Statistical Agency published national accounts.

The results of the root mean square error revealed relatively high levels of macro-fiscal forecasting errors, with revenue recording the highest. The forecasting errors display a tendency of under predicting the strength of economic recovery during boom and over predicting its strength during periods of weakness. The study although found significant evidence of GDP forecasting errors translating into revenue forecasting inaccuracies, the GDP forecasting errors fail to fully account for the revenue errors. Revenue errors were, however, found to be positive and significant in explaining the budget balance errors.

In other jurisdictions, particularly developed countries, they undertake regular evaluation of their forecasts in order to improve their forecasting procedures, which translate into quality public service delivery. The situation is lagging in Zimbabwe. Given the poor performance in public service delivery in Zimbabwe, this study contributes in dissecting the sources of the challenge by providing a comprehensive review of macro fiscal forecasts.

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking. Forecasts are subject to the systemic uncertainty of human systems, considerable event-driven uncertainty, and show biases towards optimistic growth paths. The purpose of this study is to consider approaches to improve economic foresight.

This study describes the practice of economic foresight as evolving in two separate, non-overlapping branches, short-term economic forecasting, and long-term scenario analysis of development, the latter found in studies of climate change and sustainability. The unique case of Ireland is considered, a country that has experienced both steep growth and deep troughs, with uncertainty that has confounded forecasting. The challenges facing forecasts are discussed, with brief review of the drivers of growth, and of long-term economic scenarios in the global literature.

Economic forecasting seeks to manage uncertainty by improving the accuracy of quantitative point forecasts, and related models. Yet, systematic forecast failures remain, and the economy defies prediction, even in the near-term. In contrast, long-term scenario analysis eschews forecasts in favour of a set of plausible or possible alternative scenarios. Using alternative scenarios is a response to the irreducible uncertainty of complex systems, with sophisticated approaches employed to integrate qualitative and quantitative insights.

To support economic and fiscal policymaking, it is necessary support advancement in approaches to economic foresight, to improve handling of uncertainty and related risk.

While European Union Regulation (EC) 1466/97 mandates pursuit of improved accuracy, in short-term economic forecasts, there is now a case for implementing advanced foresight approaches, for improved analysis, and more robust decision-making.

Building economic resilience and adaptability, as part of a sustainable future, requires both long-term strategic planning, and short-term policy. A 21st century policymaking process can be better supported by analysis of alternative scenarios.

To the best of the authors’ knowledge, the article is original in considering the application of scenario foresight approaches, in economic forecasting. The study has value in improving the baseline forecast methods, that are fundamental to contemporary economics, and in bringing the field of economics into the heart of foresight.

The objective of the paper is to explore the out-of-sample forecasting connections in income growth across the globe.

An autoregressive distributed lag (ARDL) framework is employed and the forecasting performance is analyzed across several horizons using different forecast combination techniques.

Results show that the foreign country's income provides superior forecasts beyond what is provided by the country's own past income movements. Superior forecasting power is particularly held by Belgium, Korea, New Zealand, the UK and the US, while these countries' income is rather difficult to predict by global counterparts. Contrary to conventional wisdom, improved forecasts of income can be obtained even for longer horizons using our approach. Results also show that the forecast combination techniques yield higher forecasting gains relative to individual model forecasts, both in magnitude and the number of countries.

The forecasting paths of income movement across the globe reveal that predictive power greatly differs across countries, regions and forecast horizons. The countries that are difficult to predict in the short run are often seen to be predictable by global income movements in the long run.

Even while it is difficult to predict the income movements at an individual country level, combining information from the income growth of several countries is likely to provide superior forecasting gains. And these gains are higher for long-horizon forecasts as compared to the short-horizon forecast.

In evaluating the forward-looking social implications of economic policy changes, the policymakers should also consider the possible global forecasting connections revealed in the study.

Employing an ARDL model to explore global income forecasting connections across several forecast horizons using different forecast combination techniques.

Financial market holds superior information that can give insights into the future trajectory of economic growth. Further, identifying sectors that hold the key to future economic growth is important for all economies, but particularly relevant to emerging markets. However, unlike existing studies, the paper provides new insights into the forward-oriented nexus between financial markets and economic growth.

This paper takes a forward-looking approach of using financial market information to predict future economic growth. The authors use ARDL modeling approach to predict economic growth using the information from stock market sectoral returns.

The authors find that sectoral stock returns significantly improve economic growth forecasts. However, the forecasting superiority is not uniform across sectors and horizons. Auto, consumers' spending, materials and realty sectors provide the most forecasting gains. In contrast, banking, capital goods and industrial sectors provide superior forecasts, but only at horizons beyond one year. The authors also find that the forecast superiority of sectors at longer horizons is inversely related to volatility.

Research highlights the need for sector-focused policy actions in driving economic growth. Further, the findings of the paper identify sectors that drive short-, medium- and long-term economic growth.

There is a significant heterogeneity among different sectors and across horizons in predicting economic growth. Results suggest that targeted policy actions in sectors like materials, metals, oil and gas, and realty are key in driving economic growth. Further, policies geared toward the grassroots industries are at least as beneficial as the large-scale industries. Evidence also suggests the need for an active fiscal policy to address infrastructural bottlenecks in primary industries like basic materials and energy. Evidence nevertheless does not undermine the role of monetary policy actions.

Unlike any paper till date, the innovation of the paper is that it takes an ARDL modeling approach to measure stock market sectoral returns' ability to forecast economic growth several months ahead in the future. Though the paper considers the Indian case, the innovation and contribution extents to the entire field of economic studies.

One component of revenue forecast error has been attributed to the phenomena of consistent underestimation bias due asymmetrical loss. Because underestimation of revenue forecast results in less loss to forecasters than overestimations, there appears to be a bias for forecasters to underestimate revenue forecasts. This paper confirms this hypothesis. Additionally, with the greater usage of national forecasting organizations that provide economic forecasts on which revenue forecasts are based, a secondary source of forecaster bias may be present in many state level forecasts. This hypothesis is supported by the increase in number of states using such organizations and a decrease in the standard deviation of the annual mean percentage state forecast error.

The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions.

This article is conceptual but also informed by the author’s long contact and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management.

Strategic forecasting is seen as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority.

The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts.

The article provides five examples of ideas that may enable managers to analyze and understand the future of their firm’s environment, thus improving investments in a wide variety of areas.

This article’s contribution is a relatively novel way of theorizing within a somewhat neglected area. It also suggests several new practical ideas that may improve management decisions.

This paper verifies whether popular Internet information from Internet forum and search engine exhibit useful content for forecasting the volatility in Chinese stock market.

First, the authors’ study commences with several HAR-RV-type models, then the study amplifies them respectively with the posting volume and search frequency to construct HAR-IF-type and HAR-BD-type models. Second, from in-sample and out-of-sample analysis, the authors empirically investigate the interpretive ability, forecasting performance (statistic and economic). Third, various robustness checks are utilized to reconfirm the authors’ findings, including alternative forecast window, alternative evaluation method and alternative stock market. Finally, the authors further discuss the forecasting performance in different forecast horizons (h = 5, 10 and 20) and asymmetric effect of information from Internet forum.

From in-sample perspective, the authors discover that posting volume exhibits better analytical ability for Chinese stock volatility than search frequency. Out-of-sample results indicate that forecasting models with posting volume could achieve a superior forecasting performance and increased economic value than competing models.

These findings can help investors and decision-makers obtain higher forecasting accuracy and economic gains.

This study enriches the existing research findings about the volatility forecasting of stock market from two dimensions. First, the authors thoroughly investigate whether the Internet information could enhance the efficiency and accuracy of the volatility forecasting concerning with the Chinese stock market. Second, the authors find a novel evidence that the information from Internet forum is more superior to search frequency in volatility forecasting of stock market. Third, they find that this study not only compares the predictability of the posting volume and search frequency simply, but it also divides the posting volume into “good” and “bad” segments to clarify its asymmetric effect respectively.

This study aims to verify whether posting volume and search frequency contain predictive content for estimating the volatility in Chinese stock market.

The forecasting model with posting volume can achieve a superior forecasting performance and increases economic value than competing models.

The results are robust in alternative forecast window, alternative evaluation method and alternative market index.

The posting volume still can help to forecast future volatility for mid- and long-term forecast horizons. Additionally, the role of posting volume in forecasting Chinese stock volatility is asymmetric.

The purpose of the paper is to forecast economic indicators of the Saudi economy in the context of low oil prices which have taken a toll on the Saudi oil-dependent economy between 2014 and 2017. Trades and investments have plummeted, leading to significant budget deficits. In response, the government unveiled a plan called Saudi Vision 2030 in 2016 which has triggered structural economic reforms leading to an unprecedented strategy of transition from an oil-driven economy to a modern market economy.

This paper forecasts with spectral analysis economic indicators of the Saudi economy up to 2030 to provide a clearer picture of the future economy assuming that the effects of recent reforms have not yet been traced by most of the economic indicators.

2018–2030 forecasts are all bearish except West Texas Intermediate (WTI) oil price expected to average $64.40 during the period 2019–2030. Two additional exceptions are the Saudi population that should grow to 40 million in 2030 and the swelling gross domestic product (GDP) generated by the non-oil sector resulting from bold actions of the Saudi government who is willing to become less dependent on revenues generated by the oil sector.

Government policymakers, economists and investors would have with spectral forecasts better insight and understanding of the Saudi economy dynamics at the early stage of major economic reforms implemented in the country. In 2020, the COVID-19 pandemic has brutally hurt the Saudi economy following a collapse in the global demand for oil and an oversupplied industry. The impact on the Saudi economy will depend on the optimal response brought by its government.

Saudi Vision 2030 plan has already triggered a deep transformation of the Saudi society that is reviewed in this paper.

The forecast of Saudi economic indicators is a timely topic considering the challenges facing the economy and reforms being undertaken. Applying an original forecasting technique to economic indicators adds to the originality of the paper.

State and federal revenues fell well short of projections in 2002. While revenues normally turn down in a recession, those revenue shortfalls were much greater than would have been expected given how mild the 2001 recession turned out to be. This paper examines some of the reasons for the large forecast variances observed in recent years using specific examples from forecasts made for the state of Minnesota. Key factors identified include inaccurate forecast for U.S. economic growth; inadequate, untimely and inaccurate data; imperfect models; and unrecognized changes in the structure of the economy. These factors came together and reinforced each other, ultimately producing a larger reduction in state revenues than could have been anticipated in advance.