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This paper examines the predictive content of financial variables above and beyond past GDP growth in a small open economy in the Eurozone. We aim to clarify potential differences in forecasting economic activity during periods of steady growth and economic turbulence.

The out-of-sample forecasting analysis is conducted recursively for two different time periods: the steady growth period from 2004:1 to 2007:4 and the financial crisis period from 2008:1 to 2011:2.

Our results from Finland suggest that the proper choice of forecasting variables relates to general economic conditions. During steady economic growth, the preferable financial indicator is the short-term interest rate combined with past growth. However, during economic turbulence, the traditional term spread and stock returns are more important in forecasting GDP growth.

The results highlight the importance of long-term interest rates in determining the level of the term spread when the central bank implements a zero interest rate policy. Moreover, during economic turbulence, stock markets are able to signal the expected effects of unconventional monetary policy on GDP growth.

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.

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.

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.

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.

This work proposes a methodology to project future freight demand for all commodity types that begin and end in each geographical region and the amount of freight that moves between all origin and destination pairs. Following the traditional four-step demand forecasting framework, the procedure corresponds to trip generation and trip distribution analysis for interregional freight demand. Using future economic growth factors from macroeconomic and input-output models, the amounts of freight production and attraction in each analysis zone are forecasted and taken as given. Subsequently, an iterative matrix balancing method is applied to determine the estimated freight shipment demand for all origin and destination zone pairs. The proposed algorithm is applied to generate predicted future freight demand within the United States from 2010-2050 in five-year increments based on the national freight demand data from 2007. Four different scenarios are proposed that consider variations in both global economic growth and environmental regulation. This study will assist transportation planners and decision makers in public and private sectors to assess how future freight delivery demand on the national scale considering various future global economic growth and environmental policy scenarios will affect various issues such as air quality and human health problems.

This study aims to examine the effect of the COVID-19 pandemic in comparison to the global financial crisis (GFC) on the gross domestic product (GDP) growth rate of China.

Empirical analyses are conducted through alternative methods such as ordinary least squares, Markov regime switching (MRS) and mixed data sampling (MIDAS) regressions. The flexibility of MIDAS regression enables us to use different variables with quarterly (GDP), monthly (export sales and foreign-exchange reserves) and daily frequencies (foreign exchange rates and Brent oil price).

The results indicate that the COVID-19 pandemic has had a considerable negative effect on China’s GDP growth, while the dummy variables used for the GFC are found to be insignificant. Further, the forecast accuracy test statistics exhibited a superior performance from MIDAS regression compared to the alternative models, such as MRS regression analysis. According to the forecast results, the authors expect a recovery in China’s economic growth in the second quarter of 2020.

This is one of the earliest studies to examine the effect of the COVID-19 pandemic on the Chinese economy, and to compare the impact of COVID-19 with the GFC. The authors provide further evidence regarding the performance of MIDAS regression analysis vs alternative methods. Findings obtained shed light on policymakers, corporations and households to update their consumption, saving and investment decisions in the chaotic environment of this pandemic.

Forecasts of rental growth are increasingly being required by and provided to property investors by a growing number of suppliers. Reviews the uses to which such forecasts are put by a major Uk institutional investor and, from a relatively unique vantage point, critically reviews the forecasting services available in the marketplace. In doing so, it identifies the main forecasting approaches adopted, highlights some of the clear inconsistencies between forecasters in terms of what they are forecasting, how they are forecasting and the different data sources they are using. Explains some of the causes for substantial variations observed in the forecasts provided and, finally, explores the potential for systematic forecasting errors. Concludes by emphasizing the need to switch attention from technical methods to improved “view formation”.

Does Islamic finance affect economic growth? The empirical literature in this area seems to be in early stages and the results are often mixed and inconclusive. This paper aims to examine the causality between financial development in general, Islamic finance in particular and real economic growth in the United Arab Emirates (UAE).

Using time series data from 1990 to 2012, a bivariate vector autoregressive model was used to document the financial development-Islamic finance-growth causal nexus and to forecast growth under various scenarios. A composite indicator, as a proxy for financial development, was determined using a non-parametric approach: data envelopment analysis.

The direction of causality runs from financial development to economic growth and the reverse causality does not drive this relationship; however, the real gross domestic product (GDP) causes Islamic financial development with no reverse effect. Furthermore, the forecasting results indicate that the past relation has been a proxy for the future where financial development leads to better progress in real economic activity. This will likely continue to stimulate the development of Islamic finance.

Because the financial markets in the UAE were established in 2000, this study ignored Islamic bonds and equity product. The value of the Sukuk listed on Dubai’s exchanges is around US$36.75bn (Thomson Reuters, 2015), reinforcing Dubai’s position as an international center for Sukuk activity. Among the most important tools of the Islamic financial sector, Sukuk deserves a closer empirical study. This can set the agenda for future work.

The financial sector appears to be one of the main drivers of real economic activity. However, more effort in the area of Islamic finance is needed to promote Shari’ah-compliant economic activities and thus better contribute toward making Dubai-UAE the capital of the Islamic economy.

A new indicator was used to evaluate the financial strength of the UAE and analyze its effect on economic development. In addition, as one of UAE’ emirates, Dubai declared its vision in 2013 to become the “capital of the Islamic economy”, this study analyzed the finance, Islamic finance and growth relations over the period 2013-2022.

The purpose of this paper is to present the great importance and impact of the virtual intellectual capital (VIC) in the frameworks of digital economics, e-governance and business, e-trading and commerce, virtual organizations and enterprises, and information communication technologies development (based on the comparative case studies of the world, Russian and Armenian economics). These conditions increase the importance of the measurement and assessment of the VIC.

The research findings are obtained through the method of comparative analysis of the complex models of the VIC. The features are studied through measuring and assessing the VIC parameters of virtual representations on the internet. The data are complemented through virtual cluster analysis, a multidimensional statistical procedure that collects data containing information on facility selection. Three cluster groups are used in the study: the clusters of the TNCs and their virtual representations; the clusters of the network of the leading innovation centers and their virtual representations; and the clusters of the leading universities and their virtual representations.

The paper establishes the research findings of the growth forecasts of the IC clusters in the world, Russian and Armenian economic processes. This is extremely important to ensuring sustainable growth of the country’s competitiveness, economy and general welfare. The paper proposes a new model of the virtual national or transnational intellectual capital (VNTIC). The VNTIC model presents three general components: virtual representations of universities, innovation center networks and transnational corporations in global networks. The research findings show that the interactive innovative tools (IIT) can be used for early diagnosis of the world economic and financial processes.

The authors developed for the first time the IIT for measuring and assessing the three intellectual capital components. The paper presents a new approach and a more reliable tool for short-term forecasting at global and national levels based on QI ranking of the VIC clusters of the commercial enterprises, universities and networks of innovation centers.