1,721,085 research outputs found
EU regional unemployment as a transnational matter : an analysis via the Gompertz diffusion process
No full textAt the end of 1990s, Danny Quah devoted several papers to the analysis of polarization and stratification in the convergence processes of economies, creating the image of the ‘convergence clubs’ and suggesting the importance of studying the distribution dynamics of the macroeconomic variables. As for the labour markets, Overman and Puga (2002) showed that a progressive polarization of unemployment was in fact occurring among the European regions in 1986–1996, causing a phenomenon of cross-border clusterization. Here we propose to analyse the evolution of the unemployment rates of the EU 27 regions in the last two decades assuming that the unemployment rates evolve according to a Gompertz stochastic process. The estimated parameters of the process – intrinsic growth rate, deceleration factor, volatility – represent the evolutionary path of the unemployment rate and allow for estimating the steady state of the process. A cluster analysis is performed on the steady state values of the unemployment rates. The analysis confirms the emergence of several ‘convergence clubs’ among the European regional labour markets, which are compared to the clusters resulting from the more traditional clusterization on the current unemployment rate
RRP : random recursive partitioning : a matching method for the estimation of the average treatment effect
No full textIn this paper we introduce the Random Recursive Partitioning (RRP) matching method. RRP generates a proximity matrix which might be useful in econometric applications like average treatment effect estimation. RRP is a Monte Carlo method that randomly generates non-empty recursive partitions of the data and evaluates the proximity between two observations as the empirical frequency they fall in a same cell of these random partitions over all Monte Carlo replications. From the proximity matrix it is possible to derive both graphical and analytical tools to evaluate the extent of the common support between data sets. The RRP method is “honest” in that it does not match observations “at any cost”: if data sets are separated, the method clearly states it.
The match obtained with RRP is invariant under monotonic transformation of the data. Average treatment effect estimators derived from the proximity matrix seem to be competitive compared to more commonly used estimators. RRP method does not require a particular structure of the data and for this reason it can be applied when distances like Mahalanobis or Euclidean are not suitable, in the presence of missing data or when the estimated propensity score is too sensitive to model specification
cem : Software for Coarsened Exact Matching
Full text linkThis program is designed to improve causal inference via a method of matching that is
widely applicable in observational data and easy to understand and use (if you understand
how to draw a histogram, you will understand this method). The program implements the
coarsened exact matching (CEM) algorithm, described below. CEM may be used alone
or in combination with any existing matching method. This algorithm, and its statistical
properties, are described in Iacus, King, and Porro (2008)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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