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Journal Home > Volume 16, Issue 1 - March 30, 2021

JAQM Volume 16, Issue 1 - March 30, 2021


A spatial analysis of the renewable energy potential in Romania
Mihai GHEORGHE, Florin-Cristian MIHAI, Horatiu Gabriel ȚIBREA

Renewable energy is likely the way moving forward to meet global energy demands while building a sustainable future. The current study performs a spatial analysis of the renewable energy sources in Romania at an administrative level, based on aggregated installed capacity, with the aim to confirm or infirm various levels of spatial correlation for relevant renewable technologies. The current analysis takes into consideration only the technologies which are present in Romania’s energy generating mix. Two geo-statistical indexes, Location Quotient and Local Moran’s I, are computed, clustered and symbolized on the map. The methodology for the analysis is described along with the implementation of the geoprocessing models built in ArcGIS and published as web services.

Size and power of tests for dependency in regressions with ordinal variables

In political science which factors that influence citizens' faith and trust in government, i.e. Political Efficacy, is of great importance. The statistical analysis is often based ordinal variables collected using surveys. In this paper we perform an interesting Monte Carlo simulation investigating the size and size adjusted power properties of ten tests for the dependency of one ordinal variable on one continuous, one binary and one ordinal variable. Tests include regression models, logit and probit where the exogenous variable is coded as a Likert scale variable or where dummies are used, test based on polychoric correlation and tests based on canonical correlation. Overall the test based on polychoric correlation performs best but is beaten in certain circumstances by the canonical correlation test. But the canonical correlation test performs really bad in some cases while the polychoric correlation test does not have a bad performance in any situation. Interestingly, the motivating example of analyzing Political Efficacy shows very similar results as the Monte Carlo simulation.

Classifying companies that have registered headquarters and subsidiaries in OFCs using unsupervised learning techniques
Alexandra Georgiana SIMA, Gheorghe HURDUZEU, Stefan Alexandru IONESCU

We proposed an improved methodology for estimating, prioritizing and evaluating the effects of the transfer of activities, assets and profits to offshore financial centers (OFCs) and shadow banking systems. We collected financial information for 2019 for approximately 16,000 companies, the raw data being used to build a set of financial rates that covered four main areas: liquidity, solvency (risk), activity and profitability. The data set comprises three broad categories of companies: companies with registered headquarters in an OFC, companies that have headquarters registered in classic jurisdictions but having subsidiaries in OFCs, companies registered in non-OFC jurisdictions and which have no connection with OFCs. We initially used hierarchical cluster analysis, an amalgamation method that starts from a number of clusters equal to the number of companies considered. We continued by using partitioning algorithms, superior in terms of performance. The k-means method provided the best results. We noticed a good separation of companies that do not carry out activities in OFCs, but the solvency indicators offer a better separation within the groups of companies that carry out activities in OFCs.