Conference
Workshop Computer Modelling Decision Making
About: Workshop Computer Modelling Decision Making is an academic conference. The conference publishes majorly in the area(s): Risk management & Data envelopment analysis. Over the lifetime, 30 publications have been published by the conference receiving 31 citations.
Papers
1 Feb 2019
TL;DR: The market graph is constructed as follows: each company is a node and the value of sign correlation between assets of the two stocks establishes a link between them and it was shown that distribution of degrees and clustering coefficient for the network follows the power law.
Abstract: In recent years the network models have been successfully employed for the analysis of the stock market. The network model of the stock market is defined as a full weighted graph, which vertices correspond to the returns of market assets, and the weights of the edges are determined by the measure of its interdependencies. To obtain important information from the network model, many researches extract subgraphs, which are called network structures. One of the most popular network structures is the so called market graph. In this paper the market graph is constructed as follows: each company is a node and the value of sign correlation between assets of the two stocks establishes a link between them. Network analysis is carried out for the companies whose shares are traded on the NYSE and NASDAQ for the period from November 22, 2013 to November 10, 2017. It was shown that distribution of degrees and clustering coefficient for our network follows the power law.
5 citations
12 Dec 2019
TL;DR: In this paper, the analysis of eight regression estimates for 2007-2009 and 2012-2016 of Russian University students' wage expectations demonstrates consistency of estimates of all coefficients but regional dummy and wage of working students.
Abstract: Factor structure of expected graduate’s wage is considered in the paper. The analysis of eight regression estimates for 2007–2009 and 2012–2016 of Russian University students’ wage expectations demonstrates consistency of estimates of all coefficients but regional dummy and wage of working students. The dynamics of expected graduates’ wage and average nominal wage suggests that the former is much more exposed to influence of economic cycles than the latter.
5 citations
12 Dec 2019
TL;DR: A machine learning approach is used for empirical evaluation of the Erdős-Rényi, BarabásiAlbert, Bollobás-Riordan, Buckley–Osthus, Chung-Lu models in comparison to the Twitter graph.
Abstract: In this paper, we conducted an experiment for comparison of the graphs generated by Erdős-Rényi, BarabásiAlbert, Bollobás-Riordan, Buckley–Osthus, Chung-Lu models and a web graph constructed using real data. Twitter data have been employed to construct social network, and C++ has been used for network analysis as well as network visualization. It was shown that distribution of degrees and clustering coefficient for this network follows the power law. A machine learning approach is used for empirical evaluation of the Erdős-Rényi, BarabásiAlbert, Bollobás-Riordan, Buckley–Osthus, Chung-Lu models in comparison to the Twitter graph.
4 citations
12 Dec 2019
TL;DR: In this paper, the authors present the results of a study of innovative spillover effects using Data Envelopment Analysis (DEA) tools, in which an assessment methodology has been developed based on the Malmquist index and an output-oriented DEA model has been built to analyze the dynamics of the regional innovation system development.
Abstract: The article presents the results of a study of innovative spillover effects using Data Envelopment Analysis (DEA) tools. The study is novel, in that an assessment methodology has been developed based on the Malmquist index and an output-oriented DEA model has been built to analyze the dynamics of the regional innovation system development. The development of innovative systems at the regional and national levels has been assessed, the Malmquist Index has been calculated, the characteristics of the regions have been determined taking into account the evaluation of spillover effects, and conclusions have been drawn on the dynamics of the development of innovative activities. The results of the study indicate the presence of positive innovative spillover effects over 2005–2017 in the Russian economy.
4 citations
12 Dec 2019
TL;DR: In this article, the role of spatial autocorrelation in regional economic development is analyzed using data from the socioeconomic development of the Russian regions and reveal the role that spatial correlation plays in economic development.
Abstract: In this paper, we analyze σand β-convergence using data from the socioeconomic development of the Russian regions and reveal the role of spatial autocorrelation in regional economic development. We consider 80 regions of Russia for the period 2010–2017. We estimate spatial autocorrelation based on Moran’s coefficients. We construct a Moran scatter plot of GDP per capita and the growth rate of GDP per capita in 2017 compared to 2014. We investigate the impact on investment growth in fixed capital and the expenditure on technological innovation. We evaluate a wide range of specifications of spatial econometric models for different weight matrices. It is shown that according to the results of estimation of conditional β-convergence models, the models of 2010–2014 and 2014–2017 differ significantly. There is a statistically significant β-convergence for the period 2010–2014, as well as the presence of spatial autocorrelation. However, according to the results of estimation models constructed from data after 2014, the estimates of the coefficients for the explanatory variables are not significantly different from zero and there is no trend toward regional convergence in terms of economic development. All conclusions obtained in the work are resistant to the choice of spatial weights matrices and model specifications.
4 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2019 | 30 |