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Proposes an empirical analysis of regional convergence in Canada based on the growth model of Barro et al. In an open economy with perfect capital mobility, if domestic residents cannot borrow abroad with human capital as collateral, the dynamics of human capital accumulation is the driving force of per capita income growth. Empirical results indicate that, as predicted by the theoretical model, various indicators of the stock of human capital did converge at the same speed as per capita income during the 1951‐1996 period. A substantial part of the relative growth of per capita income indicators across Canadian provinces since the early 1950s could be explained by the convergence process of human capital indicators based on the percentage of the population, both sexes and males, who have at least a university degree. The estimates of the human capital share in national income based on those indicators are in the neighbourhood of 0.5, a number consistent with other measures of the implicit income share of human capital. The convergence speed of per capita income at the regional level might have been two to three times faster, if all persons had invested in education at the same rate as the young.

The aim of this paper is to examine the informational efficiency of prices of all exchange traded funds (ETFs) that are actively traded on the NYSE Arca, based on methodology developed by Chordia et al.

The authors estimate the speed of convergence to market efficiency based on short‐horizon return predictability from past order flows of 273 ETFs that were traded every day on the NYSE Arca during the first six months of 2008, and compare the resulting price formation process to that of shares traded on the NYSE and NYSE Arca.

Despite the significant differences in trading costs, volatility, and informational effects between ETFs and regular stocks, the paper documents that price adjustments to new information for ETFs occur in about 30 minutes, which is comparable to price adjustments for traditional stocks traded on Arca. In multivariate setting, the paper further shows that the speed of convergence to market efficiency of ETFs is not only significantly driven by volume, but also by the probability of informed trading.

The findings provide direct answers and insights to questions posed in a recent SEC concept release document. The analysis of the speed of convergence provides a feasible measure to assess how efficiently prices of ETFs respond to new information.

The authors are first to utilize the short‐horizon return predictability from historical order flow approach to evaluate the price formation process of ETFs and to provide evidence on the determinants of its efficiency.

The purpose of this paper is to analyse whether several groups of European countries are on track for real “conditional” economic convergence in per capita income and the likely speed of convergence. The paper focusses also on the changes of the convergence processes over time.

Unlike the simple “absolute convergence”, it explores the concept of “conditional” or “club” convergence. Moreover, it adopts the approach of extending the univariate model to take into account the panel dimension over an extended time interval and endogeneity.

A process of real economic convergence has characterised the period under investigation (1995–2016), but, in general, the size and significance of the parameter is greater for the wide European Union (EU) area (EU25 and above) rather than the Eurozone (EZ). However, the crises occurred after 2008 caused most of such lower convergence in the Euro area.

This paper gives an estimate of the speed/time needed to several groups of European countries (EZ, in particular) to achieve real economic convergence. Future research could further develop the “stochastic” convergence concept.

This is an analysis of convergence in enlarging EU and EZ for an extended period (including the big crisis period and the subsequent recovery). It shows that EZ experienced a drop in the speed of real convergence after 2008 and converge at lower speed than the EU. As a consequence, a specific budget for EZ would be important to provide adjustment mechanisms after potentially large shocks.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

The purpose of this paper is to propose an improved optimization method for image matching problem, which is based on multi-scale Gaussian mutation pigeon-inspired optimization (MGMPIO) algorithm, with the objective of accomplishing the complicated image matching quickly.

The hybrid model of multi-scale Gaussian mutation (MGM) mechanism and pigeon-inspired optimization (PIO) algorithm is established for image matching problem. The MGM mechanism is a nonlinear model, which can adjust the position of pigeons by mutation operation. In addition, the variable parameter (VP) mechanism is exploited to adjust the map and compass factor of the original PIO. Low-cost quadrotor, a type of electric multiple rotorcraft, is used as a carrier of binocular camera to obtain the images.

This work improved the PIO algorithm by modifying the search strategy and adding some limits, so that it can have better performance when applied to the image matching problem. Experimental results show that the proposed method demonstrates satisfying performance in convergence speed, robustness and stability.

The proposed MGMPIO algorithm can be easily applied to solve practical problems and accelerate convergence speed of the original PIO, and thus enhancing the speed of matching process, which will considerably increase the effectiveness of algorithm.

A hybrid model of the MGM mechanism and PIO algorithm is proposed for image matching problem. The VP mechanism and low-cost quadrotor is also utilized in image matching problem.

The purpose of this paper is to propose successive-over-relaxation (SOR) based recursive Bayesian approach (RBA) for the configuration identification of a Power System. Moreover, to present a comparison between the proposed method and existing RBA approaches regarding convergence speed and robustness.

Swift power network configuration identification is important for adopting the smart grid features like power system automation. In this work, a new SOR-based numerical approach is adopted to increase the convergence speed of the existing RBA algorithm and at the same time maintaining robustness against noise. Existing RBA and SOR-RBA are tested on IEEE 6 bus, IEEE 14 bus networks and 48 bus Danish Medium Voltage distribution network in the MATLAB R2014b environment and a comparative analysis is presented.

The comparison of existing RBA and proposed SOR-RBA is performed, which reveals that the latter has good convergence speed compared to the former RBA algorithms. Moreover, it is robust against bad data and noise.

Existing RBA techniques have slow convergence and are also prone to measurement noise. Their convergence speed is effected by noisy measurements. In this paper, an attempt has been made to enhance convergence speed of the new identification algorithm while keeping its numerical stability and robustness during noisy measurement conditions. This work is novel and has drastic improvement in the convergence speed and robustness of the former RBA algorithms.

In this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.

In this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.

The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.

The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

The purpose of this study is to examine whether the electricity consumption patterns across Indian states do converge.

This study considers 18 Indian states spanning over the period 1970-1971 and 2014-2015, using the recently developed Phillips and Sul panel convergence technique that accounts the multiple steady states.

The results provide the following insights. First, the authors find evidence of convergence in electricity consumption among all Indian states. This suggests that electricity consumption patterns for Indian states are converging to a common steady state. Second, to provide broader insights, we further investigate the convergence in electricity consumption among user groups such as agriculture, industry, commercial, domestic and miscellaneous. The results reveal that commercial, domestic and miscellaneous groups are also converging. Third, the non-convergence patterns in agriculture and industry enable us to investigate the possibility of clubs or the multiple common steady states. The results indicate the occurrence of three clubs in case of agriculture and two clubs in case of the industry. Fourth, this study also inspects the relative speed of convergence among the user groups. The results reveal the higher speed of convergence in case of the domestic user group.

The findings enable policymakers to formulate an appropriate energy policy to accommodate the future electricity demand across Indian states and prioritize low electricity consumption states so that they receive a greater share.

This is the first study that examines the convergence in electricity consumption across Indian states at aggregate and user groups using a new panel club convergence technique.

Purpose – The main aim of this chapter is to analyze Spanish internal and external territorial conflicts, mostly associated with the border effect between two continents with different economic and cultural systems. We assess the impact that the emergence of the new economy, represented by new technologies, R&D, privatizations, and foreign direct investment, has had in South-Spain, particularly in Andalusia, throughout the period 1995–2010. Special attention has been paid to the dynamics of convergence–divergence processes in terms of per capita income with respect to its neighboring different economic and cultural areas: Europe and the Maghreb.

Methodology – For the aforementioned purposes, we suggest applying the game theory approach to solve domestic secessionist conflicts, and the method followed by Mankiw, Romer, and Weil (1992) to address economic conflicts by means of promoting convergence with Europe. We propose economic competition between cities as a way to deal with external territorial conflicts concerning neighboring countries.

Findings – The main results obtained from econometric applications indicate that privatization processes, foreign direct investment, research and investment, and investment in new technologies allow for the real convergence of Spain and Southern Spain with European economies.

Research limitations – This chapter does not address smaller conflicts.

Social implications – Conflicts resolutions promote peace in both continental borders.

Originality – This chapter analyzes the most relevant domestic and external Spanish conflicts. The most important domestic conflicts are the linguistic and cultural conflicts in bilingual regions. The major external Spanish conflicts analyzed herein are both territorial conflicts between Spain and Morocco and Muslim immigration.

A major lesson of the European Monetary Union (EMU) crisis is that serious disequilibria result from regional monetary arrangements not designed to be robust to a variety of shocks. The purpose of this paper is to assess these disequilibria within the Economic and Monetary Community of Central Africa (CEMAC), West African Economic and Monetary Union (UEMOA) and Financial Community of Africa (CFA) zones.

In the assessments, monetary policy targets inflation and financial dynamics of depth, efficiency, activity and size while real sector policy targets economic performance in terms of GDP growth. The author also provides the speed of convergence and time required to achieve a 100 percent convergence.

But for financial intermediary size within the CFA zone, findings, for the most part, support only unconditional convergence. There is no form of convergence within the CEMAC zone.

The broad insignificance of conditional convergence results has substantial policy implications. Monetary and real policies, which are often homogenous for member states, are thwarted by heterogeneous structural and institutional characteristics, which give rise to different levels and patterns of financial intermediary development. Therefore, member states should work towards harmonizing cross‐country differences in structural and institutional characteristics that hamper the effectiveness of monetary policies.

The paper provides warning signs to the CFA zone in the heat of the Euro zone crises.