With the advent of big data in economics machine learning algorithms become more and more appealing to economists. Despite some attempts of establishing artificial neural networks in in the early 1990s, only little is known about their ability of estimating causal effects in policy evaluation....

High rates of student attrition in tertiary education are a major concern for universities and public policy, as dropout is not only costly for the students but also wastes public funds. To successfully reduce student attrition, it is imperative to understand which students are at risk of...

Artificial neural networks have become increasingly popular for statistical model fitting over the last years, mainly due to increasing computational power. In this paper, an introduction to the use of artificial neural network (ANN) regression models is given. The problem of predicting the GDP...

Data Mining represents a Business Intelligence (BI) methodology which provides an insight into the 'hidden' information about its operations thus improving the process of making strategic business decisions based on a clear and understandable interpretation of existing results. Data mining can...

The aim of this paper is comparison of multivariate statistical analysis and machine learning methods based on the model used for the measurement of current and forecasting of the future customer profitability. Modern customer profitability analysis shows that customer-company relationship is...

Rapid technological development and progress lead to the need for better and more efficient education which should prepare the applicant for increasingly flexible labour market. The goal of this research is to create models for prediction of student's efficacy, compare them, find the key factors...

To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and a private university to predict...

The main goal of this paper is to apply a combination of statistical and connectionist schemes to examine, via clustering analysis, private indebtedness in different countries. Thirty-nine such experiences are used. The relationship between private debts and some macroeconomic variables are...

Echo State Neural Networks (ESN) were applied to forecast the realized variance time series of 19 major stock market indices. Symmetric ESN and asymmetric AESN models were constructed and compared with the benchmark realized variance models HAR and AHAR that approximate the long memory of the...

In the wake of regulatory, information technology and methodological changes, statistical property valuation has been gaining traction in Hungary. This paper looks at the available methods of appraisal based on the literature. We provide an overview of the advantages and drawbacks of the...