House price determinants in Athens: a spatial econometric approach

In this paper, we try to identify the price determinants in the biggest real estate market of Greece, the metropolitan area of Athens. For that purpose, various spatial econometric models are used to explore their prediction ability and we are displaying the variations in property prices for the wider area of Athens. These models have been compared based on different criteria such as model fit, the Akaike information criterion and variance of the residuals. Our results indicate that, in our case, the spatial general model is the most appropriate simultaneous autoregressive model when dealing with spatially autocorrelated prices of housing properties data, in terms of our selection criteria.     

Segmented Labour Markets: Theory and Evidence

This paper examines the possibility of accepting the labour market segmentation approach as a valid alternative to the classical and neo-classical analysis of labour markets. It consists of three main parts. The first part contains a historical analysis of both the distant and recent origins of the labour market segmentation hypothesis. Part two outlines the central ideas of both segmentationalists and radical theorists who attempted to explain the fragmented nature of labour markets and the importance of institutional and social influences upon pay, employment and mobility of individual workers between different labour market sectors. The third part examines the case for labour market segmentation using four alternative techniques and discusses the issue of mobility among different labour market segments. It is thereby concluded that the lack of agreement among the segmented labour market theorists on both theoretical and methodological issues has prevented them from developing a consistent and convincing argument based on verifiable empirical evidence to validate their thesis.     

Distance Metrics and Clustering Methods for Mixed‐type Data

In spite of the abundance of clustering techniques and algorithms, clustering mixed interval (continuous) and categorical (nominal and/or ordinal) scale data remain a challenging problem. In order to identify the most effective approaches for clustering mixed‐type data, we use both theoretical and empirical analyses to present a critical review of the strengths and weaknesses of the methods identified in the literature. Guidelines on approaches to use under different scenarios are provided, along with potential directions for future research.