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In recent years, model averaging, by which estimates are obtained based on not one single model but a weighted ensemble of models, has received growing attention as an alternative to model selection. To-date, methods for model averaging have been developed almost exclusively for point-valued data, despite the fact that interval-valued data are commonplace in many applications and the substantial body of literature on estimation and inference methods for interval-valued data. This paper focuses on the special case of interval time series data, and assumes that the mid-point and log-range of the interval values are modelled by a two-equation vector autoregressive with exogenous covariates (VARX) model. We develop a methodology for combining models of varying lag orders based on a weight choice criterion that minimises an unbiased estimator of the squared error risk of the model average estimator. We prove that this method yields predictors of mid-points and ranges with an optimal asymptotic property. In addition, we develop a method for correcting the range forecasts, taking into account the forecast error variance. An extensive simulation experiment examines the performance of the proposed model averaging method in finite samples. We apply the method to an interval-valued data series on crude oil future prices.

Since 2017, the Bitcoin blockchain system has experienced 105 fork divergences. The rapidly increasing blockchain forks have resulted in fierce competition and created significant controversies in blockchain community. To analyze this competitive aspect, we consider blockchain as a two-sided platform that serves both customers and miners. We develop a game-theoretic model to investigate how a blockchain platform's decision on its settings, such as block size and transaction fee, affects the competition between blockchain platforms as well as the participation behavior of customers and miners. Our findings suggest that increasing the transaction fee alleviates congestion on the platform when customers have a relatively balanced need between efficiency and safety. In contrast, it induces congestion when efficiency is valued over safety. In addition, under hard fork competition, the difference in blockchain platforms' block sizes directs the attention of miners towards different types of mining rewards. Moreover, it also affects the optimal types of customers the blockchain platforms should target. Furthermore, we find that the degree of congestion and the risk attitudes of participants play vital roles in differentiating different block-sized platforms' optimal transaction fees. We provide empirical evidence on the theoretical findings and practical implications for blockchain platform competition with respect to the behaviors of platform participants.

Structural changes often occur in economics and finance due to changes in preferences, technologies, institutional arrangements, policies, crises, etc. Improving forecast accuracy of economic time series with structural changes is a long-standing problem. Model averaging aims at providing an insurance against selecting a poor forecast model. All existing model averaging approaches in the literature are designed with constant (non-time-varying) combination weights. Little attention has been paid to time-varying model averaging, which is more realistic in economics under structural changes. This paper proposes a novel model averaging estimator which selects optimal time-varying combination weights by minimizing a local jackknife criterion. It is shown that the proposed time-varying jackknife model averaging (TVJMA) estimator is asymptotically optimal in the sense of achieving the lowest possible local squared error loss in a class of time-varying model averaging estimators. Under a set of regularity assumptions, the TVJMA estimator is T h -consistent. A simulation study and an empirical application highlight the merits of the proposed TVJMA estimator relative to a variety of popular estimators with constant model averaging weights and model selection.

In network games, a network is an important attribute of players’ strategies: each player adopts her behavior not only by taking into account standard information about her opponents such as objectives, game dynamics, and information structure; but also by evaluating the communication structure of players represented by the network. In this paper, we investigate a dynamic game with network externalities in which a state variable of each player is influenced by her own decision and the decisions of her predecessors in the network. For the game under consideration, we identify Nash equilibrium and cooperative behavior. Additionally, we examine the model under myopic equilibrium and myopic cooperation where players place no weight on their future gains. Next, we use our findings to take in the important environmental problem of river pollution. We suppose that firms, which are located along the river flow, produce goods and compete in a market. The production results in water pollution, and the pollution emissions of a firm can influence downstream counterparts. We analyze this model in detail by incorporating a firm’s location and analytically comparing the equilibrium and cooperative behavior.

We consider a joint inventory-pricing control problem in a single-product, periodic-review, dual-supplier inventory system. The two suppliers have different lead times. One expedited supplier offers instantaneous replenishment, and one regular supplier requires an L-period lead time for delivery. The supply quantity is stochastic and the demand is price-dependent. For the expedited inventory replenishment, we characterize the optimal policy as a state-dependent almost-threshold policy by extending the stochastically linear in mid-point to a multidimensional setting. To investigate the optimal regular inventory replenishment and pricing policy, we propose the notions of partially stochastic translation (PST) and increasing partially stochastic translation (IPST), which help in obtaining the antimultimodularity preservation in dynamic programming problems. We provide properties, sufficient conditions, and examples for PST and IPST functions. By applying PST and IPST, we obtain the antimultimodularity of the profit functions. The antimultimodular profit functions ensure that the optimal regular ordering quantity and the optimal price are monotone in the current inventory level and outstanding order quantities. Moreover, we reveal that as the time interval increases, the effects of previous outstanding orders on the optimal regular ordering and pricing decisions are decreasing and increasing, respectively. PST and IPST also enable us to further characterize the optimal expedited ordering quantity as decreasing in the inventory level. However, the optimal expedited ordering quantity can be non-monotone with respect to the outstanding order quantities, as shown in the example.

Because of the growing awareness of balancing the economy, the society and the environment, there has been an increased focus on goal setting under sustainable context. However, underperforming suppliers may set impractical goals that are difficult to achieve in the short term due to the poor information analysis, which could cause benchmarking difficulties. Therefore, rather than focusing on a single ultimate goal, this paper developed an implementable goal sequence for which a dynamic Data Envelopment Analysis based benchmarking model was developed as a multi-period process. The indicators that could not be directly measured using numerical values were incorporated into the model as fuzzy numbers, and based on efficiencies, the suppliers were divided into several layers. A method that identified supplier attractiveness and progress was then introduced to select the most appropriate goal in each layer for the goal sequence. A numerical example is provided that illustrated the feasibility and efficacy of the proposed method to successfully identify the inefficient suppliers and determine a goal sequence that had three attainable goals. The sensitivity analysis and comparisons revealed that the proposed model avoided selecting period-inefficient suppliers and provided a better benchmarking process.

We consider a finite-agent Hellwig (1980) economy with an extension to allow traders to observe their neighbors’ signals in an exogenously given social network. There are two potential approaches for traders to process observed signals: directly infer information about the fundamentals from the complete collection of observed signals, or indirectly from an average of observed signals. The two approaches lead to different information sets for traders. In this study, we investigate whether the two economies corresponding to the two approaches are equivalent in the sense that they have the same market equilibrium. For general network and signal structures, we present a necessary and sufficient condition for the equivalence, revealing that the two finite-agent economies are not equivalent in general unless the network structure and signal structure coordinate well. When traders have homogeneous preferences and the signal structure takes the classical form in the literature, we find that the two finite-agent economies are equivalent for regular graphs, but not for chain and star graphs. Finally, for the classical signal structure, we show that the two large economies, defined as the limit of a sequence of replica finite-agent economies, are equivalent for any network structure.

Studies have shown that the soaring demand for air conditioners in recent years is closely related to the worsening global warming; however, little evidence has been provided for China. This study uses weekly data of 343 Chinese cities to investigate how air conditioner sales respond to climate variability. We detected a U-shaped relationship between air-conditioning and temperature. An additional day with average temperature above 30°C increases weekly sales by 16.2%. Heterogeneity analysis shows that the adoption of air-conditioning is different for south and north China. By combining our estimates with shared socioeconomic pathway scenarios, we project China's mid-century air conditioner sales and the resulting electricity demand. Under the fossil-fueled development scenario, air conditioner sales in the Pearl River Delta would rise by 71% (65.7%–87.6%) in summer. On average, the per capita electricity demand for air-conditioning will surge by 28% (23.2%–35.4%) in China by mid-century.

This paper gives a new approach to show the existence and regularity of linear equilibrium established by Lou ⓡ al. (2019) for a noisy rational expectations economy. Different from the existing method which essentially requires to find a fixed point of a system of nonlinear algebraic equations, the new approach is operated directly on an alternative form of market-clearing conditions. One main advantage of the new approach is that besides homogeneous-valuation economies, it can also handle the existence of equilibrium in economies with heterogeneous valuations where the existing method for dealing with homogeneous-valuation economies fails to work.

This paper studies the production scheduling problem in a flexible manufacturing system with two adjacent working areas, whose products are incorporated with flexible non-linear process plans and assembling operations. The basic parts are produced in one area before they are transported to the other area for assembly. The assembling structures of products are either flat or multi-levelled. Sequence-dependent setup times of operations and transition times of jobs between machines are considered separately from processing times. Lot streaming is considered beforehand such that each job represents a basic part instead of a batch of identical parts. Identical subassemblies are shared by all possible assembling operations, instead of being pre-associated with any product. Makespan, total tardiness and total workload are taken as objectives to be optimised. We propose a distributed ant colony system to solve the problem and explore the Pareto front. The approach is first compared with other methods, using several sets of hypothetical test cases with different sizes and complexities; then, it is applied to solve a ball valve production scheduling problem under different scenarios. We show that the proposed approach outperforms most of the other methods for the tested problems, especially for large-scale instances, making it a valuable and competitive approach for solving practical production scheduling problems.

Belhaj (2010) established that a barrier strategy is optimal for the dividend problem under jump–diffusion model. However, if the optimal dividend barrier level is set too low, then the bankruptcy probability may be too high to be acceptable. This paper aims to address this issue by taking the solvency constrain into consideration. Precisely, we consider a dividend payment problem with solvency constraint under a jump–diffusion model. Using stochastic control and PIDE, we derive the optimal dividend strategy of the problem.

In order to eliminate the information asymmetry of supply and demand in transport service market, online car hailing platforms appear recent years. Almost all the transport service markets have the property of spatial differentiation, i.e, passengers' demand and vehicle supply are subject to different distributions in different regions at different time. Under the framework of two-sided market theory, this paper develops an intermediary pricing model for transport service market to evaluate the impact of spatial differentiation and network externality on online car hailing platform's pricing mechanism, based on the analysis of transport services' supply-demand matching. The analytic solution shows that the optimal price of the online car hailing platform is decided by the degree of heterogeneity in customer valuation on demand side and driver cost on supply side. Given optimal price on demand side, supply price in a region directly determines the supply of transport service in the same region, has cross effect on the supply in other region, and has indirect effect on demand price through the aggregate supply. Based on the trip record data of New York City, the simulation shows that online car hailing platform try to customize higher prices to attract more drivers and alleviate the passengers' demand pressure in morning rush hour. Furthermore, the simulation also shows that the disparity of the flow density in adjacent areas may affect the price adjustment mechanism of the online car hailing platform, and if the regional disparity further expands, the optimal choice of the platform is to increase the price of supply side, rather than the demand side.

Accompanying with strengthening consciousness of environmental protection and social security, developing sustainable supply chain (SSC) has been becoming more and more important. Under this background, it is essential to deeply understand and evaluate SSCs from comprehensive economy-environment-society aspects. In order to do this, this paper considers the SSC as a dynamic three-stage network system consisting of supplier, manufacturer and distributor across multiple periods, and establishes a novel dynamic network DEA model with desirable and undesirable indicators to compute the detailed efficiencies based on both effective and invalid production frontiers. In the model, the degrees of environmental pollution and customer satisfaction are described as interval type-2 fuzzy sets. Then a method considering the optimistic-pessimistic attitude of decision makers is introduced to get solutions. For case study, the proposed DEA model is used to evaluate 20 SSCs, and analysis from time and stage perspectives are conducted to show the detailed performance. According to the results, the evaluated SSCs can be divided into four groups with different patterns, the weakness stage and period for each SSC can also be identified. The comparison and sensitivity analysis demonstrates our proposed model has more discriminating power and the ability of providing more managerial insights. Not only the sensitivity degree of a SSC to the attitude change of decision makers can be determined, but also the important indicator in different aspects for each SSC can be pointed out.

This study proposes a new, novel crude oil price forecasting method based on online media text mining, with the aim of capturing the more immediate market antecedents of price fluctuations. Specifically, this is an early attempt to apply deep learning techniques to crude oil forecasting, and to extract hidden patterns within online news media using a convolutional neural network (CNN). While the news-text sentiment features and the features extracted by the CNN model reveal significant relationships with the price change, they need to be grouped according to their topics in the price forecasting in order to obtain a greater forecasting accuracy. This study further proposes a feature grouping method based on the Latent Dirichlet Allocation (LDA) topic model for distinguishing effects from various online news topics. Optimized input variable combination is constructed using lag order selection and feature selection methods. Our empirical results suggest that the proposed topic-sentiment synthesis forecasting models perform better than the older benchmark models. In addition, text features and financial features are shown to be complementary in producing more accurate crude oil price forecasts.

The development of big data generation, acquisition, storage, processing, and other technologies has greatly enriched our sensory world and fundamentally changed the basis of traditional economic and financial forecasting. Unexpected events in the economic and financial fields challenge our confidence in the performance of forecasting models. Obviously, the big data-driven decision theories and analysis methods are different from the traditional methods. In view of the important role of big data-driven economic and financial forecasting in social stability, innovative development, and sustainability, the research frontiers of big data-driven economic and financial forecasting in the future include: feature mining of complex economic systems with big data representation; accurate real-time correction of theories and methods of dynamic forecasting and early warning; general paradigm of big data forecasting research; formation and process of big data-driven economic and financial system management mechanism, etc. Systematic research on such issues will contribute to the formation of decision-making theories and research systems in the context of big data, thus improving the adaptability and scientificity of management decisions.