Integrating R and Hadoop for Big Data Analysis

Analyzing and working with big data could be very diffi cult using classical means like relational database management systems or desktop software packages for statistics and visualization. Instead, big data requires large clusters with hundreds or even thousands of computing nodes. Offi cial statistics is increasingly considering big data for deriving new statistics because big data sources could produce more relevant and timely statistics than traditional sources. One of the software tools successfully and wide spread used for storage and processing of big data sets on clusters of commodity hardware is Hadoop. Hadoop framework contains libraries, a distributed fi le-system (HDFS), a resource-management platform and implements a version of the MapReduce programming model for large scale data processing. In this paper we investigate the possibilities of integrating Hadoop with R which is a popular software used for statistical computing and data visualization. We present three ways of integrating them: R with Streaming, Rhipe and RHadoop and we emphasize the advantages and disadvantages of each solution.Comment: Romanian Statistical Review no. 2 / 201

Non-stationary iterative methods for solving macroeconomic numeric models

Macroeconometric modeling was influenced by the development of new and efficient computational techniques. Rational Expectations models, a particular class of macroeconometric models, give raise to very large systems of equations, the solution of which requires heavy computations. Therefore, such models are an interesting testing ground for the numerical methods addressed in this research. The most difficult problem is to obtain the solution of the linear system that arises during the Newton step. As an alternative to the direct methods, we propose non-stationary iterative methods, also called Krylov methods, to solve these models. Numerical experiments conducted by authors confirm the interesting features of these methods: low computational complexity and storage requirements

A Comparative Study of Some Features of Higher Education in Romania, Bulgaria and Hungary

The transition from the centralized economy to the market economy has determined important changes in higher education in the countries of the former socialist bloc. In a relatively short period an impressive growth in the number of students has been recorded without taking into account the evolution of the number of pupils in high school and demographic phenomena, such as increased migration, reduced birth rate, and so on. In a medium and long time horizon the current increases in the number of students cannot be sustained. For the analysis of the data series we used a number of econometric techniques.regression models, cointegration, higher education statistics, education expenditures, ADF, unit roots

Decomposition of the Inequality of Income Distribution by Income Types - Application for Romania

This paper identifies the salient factors that characterize the inequality income distribution for Romania. Data analysis is rigorously carried out using sophisticated techniques borrowed from classical statistics (Theil). Decomposition of the inequalities measured by the Theil index is also performed. This study relies on an exhaustive (11.1 million records for 2014) data-set for total personal gross income of Romanian citizens.Comment: 12 pages, 5 figures, 4 tables, 49 reference

Quantitative Techniques used for the Informal Economy Analysis at National and Regional Level

The main issues in the informal economy domain are related to its definition, quantification, its position at the level of national economy and the application of econometric models to quantify its effects on economic and social development, at national and regional level. In order to evaluate the informal economy is presented the Ahumada’s model (2007). Using data series from the official statistics we made estimations of the informal economy for Romania for 2000-2009 time period. The obtained results for the ratio between cash transactions from the informal economy and the ones from the formal economy indicate that this one is situated between 24 and almost 41%. The parameter estimation for the applied regression models was realized using EViews software.Informal Economy, Econometric Models, Regional Economy