Search results

From the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined or ensemble forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. This uncertainty formula should incorporate forecaster discord, as justified by (i) disagreement as a component of combined forecast uncertainty, (ii) the model averaging literature, and (iii) central banks’ communication of uncertainty via fan charts. Using new statistics to test for the homogeneity of idiosyncratic errors under the joint limits with both T and n approaching infinity simultaneously, the authors find that some previously used measures can significantly underestimate the conceptually correct benchmark forecast uncertainty.

The purpose of this paper is to explain why the high quality cassava flour (HQCF) value chain in Nigeria has not performed as well as expected. The specific objectives are to: analyse important sources of uncertainty influencing HQCF value chains; explore stakeholders’ strategies to respond to uncertainty; and highlight the implications of different adaptation strategies for equity and the environment in the development of the value chain.

The authors used a conceptual framework based on complex adaptive systems to analyse the slow development of the value chain for HQCF in Nigeria, with a specific focus on how key stakeholders have adapted to uncertainty. The paper is based on information from secondary sources and grey literature. In particular, the authors have drawn heavily on project documents of the Cassava: Adding Value for Africa project (2008 to present), which is funded by the Bill & Melinda Gates Foundation, and on the authors’ experience with this project.

Policy changes; demand and supply of HQCF; availability and price of cassava roots; supply and cost of energy are major sources of uncertainty in the chain. Researchers and government have shaped the chain through technology development and policy initiatives. Farmers adapted by selling cassava to rival chains, while processors adapted by switching to rival cassava products, reducing energy costs and vertical integration. However, with uncertainties in HQCF supply, the milling industry has reserved the right to play. Vertical integration offers millers a potential solution to uncertainty in HQCF supply, but raises questions about social and environmental outcomes in the chain.

The use of the framework of complex adaptive systems helped to explain the development of the HQCF value chain in Nigeria. The authors identified sources of uncertainty that have been pivotal in restricting value chain development, including changes in policy environment, the demand for and supply of HQCF, the availability and price of cassava roots, and the availability and cost of energy for flour processing. Value chain actors have responded to these uncertainties in different ways. Analysing these responses in terms of adaptation provides useful insights into why the value chain for HQCF in Nigeria has been so slow to develop.

Recent developments suggest that the most effective strategy for the milling industry to reduce uncertainty in the HQCF value chain is through vertical integration, producing their own cassava roots and flour. This raises concerns about equity. Until now, it has been assumed that the development of the value chain for HQCF can combine both growth and equity objectives. The validity of this assumption now seems to be open to question. The extent to which these developments of HQCF value chains can combine economic growth, equity and environmental objectives, as set out in the sustainable development goals, is an open question.

The originality lies in the analysis of the development of HQCF value chains in Nigeria through the lens of complex adaptive systems, with a particular focus on uncertainty and adaptation. In order to explore adaptation, the authors employ Courtney et al.’s (1997) conceptualization of business strategy under conditions of uncertainty. They argue that organisations can assume three strategic postures in response to uncertainty and three types of actions to implement that strategy. This combination of frameworks provides a fresh means of understanding the importance of uncertainty and different actors’ strategies in the development of value chains in a developing country context.

This paper aims to investigate how oil price uncertainty affects real gross domestic product (GDP) and industrial production in eight Central and Eastern European countries (CEEC).

In the research process, the authors use the Bayesian method of inference for the two applied methodologies – Markov switching generalized autoregressive conditional heteroscedasticity (GARCH) model and quantile regression.

The results clearly indicate that oil price uncertainty has a low effect on output in moderate market conditions in the selected countries. On the other hand, in the phases of contraction and expansion, which are portrayed by the tail quantiles, the authors find negative and positive Bayesian quantile parameters, which are relatively high in magnitude. This implies that in periods of deep economic crises, an increase in the oil price uncertainty reduces output, amplifying in this way recession pressures in the economy. Contrary, when the economy is in expansion, oil price uncertainty has no influence on the output. The probable reason lies in the fact that the negative effect of oil volatility is not strong enough in the expansion phase to overpower all other positive developments which characterize a growing economy. Also, evidence suggests that increased oil uncertainty has a more negative effect on industrial production than on real GDP, whereas industrial share in GDP plays an important role in how strong some CEECs are impacted by oil uncertainty.

This paper is the first one that investigates the spillover effect from oil uncertainty to output in CEEC.

The purpose of the paper is 1) to address the importance of contingency at the right level when defining project control baseline, including cost reserves / “room to manoeuvre” and 2) present proactive uncertainty management as a regime to ensure cost effective management of project reserves and contribute to project success.

The paper is a combination of literature study and quantitative research on how contingency develops during the lifetime of a case project. The investigation into the case project includes document study into quantitative material from the case project. The combination of empirical material and theory makes the discussion robust.

Unrealistic low cost uncertainty will lead to unrealistic low contingency. The case study from a Norwegian mega project shows a contingency of 15 per cent in addition to expected costs. The case study shows that by continuous opportunity management and risk reduction, the needs for management reserves are systematically reduced and the contingency is controlled.

This research is limited to one case study. A higher number of cases are necessary to generalise the findings. However, the authors would claim that the systematic mapping of need for management reserve towards the project contingency, and a continuous uncertainty management system will help to obtain cost effective management. The findings from the case study could be applied on similar cases.

The case study shows a way of setting contingencies and managing contingencies through systematic uncertainty management.

Improved management of project provisions will increase the value of future projects.

An increasing demand of agricultural intensification and value addition necessitates the use of improved inputs such as improved seed. Smallholder farmers contribute about 70% of agricultural production in Tanzania. Agriculture sector in Tanzania contributes about 24.1% of the GDP, 30% of exports and 65% of industrial raw materials. Thus, agriculture development, economic growth and industrialization are inseparable. Due to the nature of the product, smallholder farmers cannot judge the overall excellence of seed at the time of buying. This paper assessed quality uncertainty in maize and vegetable seed and its implication for market exchange between farmers and seed sellers in Kilolo district, Iringa Tanzania. The study used a random sample of 130 smallholder farmers and representatives from ten seed companies. Asymmetric information prevails between the two trading sides, i.e. sellers and buyers, leading into quality uncertainty. Moreover, product augmentation is profoundly overlooked whereby most of seed companies have not augmented their products. Because an improved seed is a quintessential example of an experience good, quality uncertainty of some crop varieties under field conditions favored some seed brands to be used more by farmers compared to others. This paper offers a thorough deduction on quality uncertainty under farmers' field condition and its implication on market exchange. It adds information in the body of knowledge on how an improved seed can contribute to sustainable production of food and industrial raw materials, which is a step towards desired industrialization agenda in Tanzania.

This study examines how specific micro-level macroeconomic indicators influence corporate performance volatility among US corporate bodies in the short run.

The study employs error correction autoregressive distributed lagged (ARDL) model (ECM) to examine how micro-level variables influence volatility associated with corporate performance in the short run.

This paper finds that disaggregated or micro-level variables examined, tend to exhibit features that are not readily apparent from the aggregate variable from which such variables are derived. For instance, reported empirical estimate suggests that, growth in expenditures on services and nondurable goods tend to lower volatility associated with corporate performance, whereas government expenditures and expenditures on durable goods rather worsens volatility associated with corporate performance, all things being equal. Additionally, presented empirical estimates further provide evidence suggesting that macroeconomic uncertainty and inflation uncertainty significantly moderate or influence the extent to which disaggregated variables impact corporate performance volatility.

Compared to related studies in the reviewed literature, this study rather examines volatility associated with corporate performance instead of the corporate performance indicator itself. Additionally, this paper also examines how disaggregated variable instead of aggregate variables impact such volatility. Finally, the moderating role of key macroeconomic conditions in such a relationship is also examined.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

The aim of this study is to investigate the impact of local political uncertainty on the asymmetric cost behavior (i.e. cost stickiness) for listed firms in China.

In this study, the authors manually collect the turnover data of prefecture-city officials as a measure of exogenous fluctuations in political uncertainty and obtain firm-level financial information from the China Stock Market Accounting Research (CSMAR) database. To perform the analysis, the authors augment the traditional cost stickiness model by including the interaction terms of the prefecture-city official turnover, and firm-level and prefecture-city level control variables.

The authors find that political turnover leads to a higher degree of cost stickiness, implying that firms retain slack resources when political uncertainty is high. Moreover, the effect of political turnover on cost stickiness is more pronounced for firms residing in regions with weaker institutional environments, and firms that are privately owned and with smaller size. The authors further provide evidence that policy uncertainty and the threat of losing political connection are two underlying channels. Overall, this study documents that the local political process is an important channel that influences corporate operational decisions.

This study provides the first piece of evidence on the relation between political uncertainty and cost stickiness at the local government level. Moreover, the authors propose and demonstrate two underlying channels through which political uncertainty affects firms' asymmetric cost behavior.

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

Uncertainty, a state of unknowing linked to threats and opportunities, is a key characteristic of megaprojects, making it challenging for government officials and politicians to decide on their initiation. For them, implementation by the private sector adds an extra layer of complexity and uncertainty to megaproject planning. In this context, only a few studies have focussed on governing and the mobilization of uncertainty arguments in communication between government actors and private developers either in favour of or against megaprojects. The purpose of this article is to shed light on how private megaproject proposals progress towards political acceptance or rejection in public decision-making.

This process of public decision-making on private megaproject proposals is examined in the case of the Helsinki–Tallinn undersea rail tunnel. In line with the interpretive research tradition, the authors’ study draws on a qualitative methodology underpinned by social constructionism. The research process can be characterized as abductive.

The authors’ findings suggest that while public decision-making on megaprojects is a conflictual and dynamic process, some types of uncertainty are relatively more important in affecting the perceived feasibility of the projects in the eyes of public sector decision-makers.

This study contributes to the debate on uncertainty management in megaprojects, proposing a new type of uncertainty – uncertainty about privateness – which has not been explicitly visible thus far.