This paper summarizes the background, type, logic, and working procedure of the statistical matching used in the Levy Institute Measure of Economic Well-Being (LIMEW) project to combine the various data sets used to produce the synthetic data set with which the LIMEW is constructed. The authors...

Contrary to frequent recommendations of the public finance literature and international institutions, a persistently high tax wedge on labor is observed in Europe. At the same time, the scope for shifting taxes from labor to more growth-friendly revenue sources appears underused in many European...

We introduce a data driven and model free approach for computing conditional expectations. The new method combines Gaussian Mean Mixture models with classic analytic techniques based on the properties of the Gaussian distribution. We also incorporate a proxy hedge that leads to analytic...

In this paper we describe and apply the methods of Symbolic Time Series Analysis to an experimental framework. We discuss data symbolization as a tool for identifying temporal patterns in experimental data and use symbol sequence statistics in a model strategy. In particular, we introduce a...

Nonlinear nonparametric statistics (NNS) algorithm offers new tools for curve fitting. A relationship between k-means clustering and NNS regression points is explored with graphics showing a perfect fit in the limit. The goal of this paper is to demonstrate NNS as a form of unsupervised...

In this paper I describe and apply the methods of Symbolic Time Series Analysis (STSA) to an experimental framework. The idea behind Symbolic Time Series Analysis is simple: the values of a given time series data are transformed into a finite set of symbols obtaining a finite string. Then, we...

A major feature of healthcare systems is substantial variation in hospital productivity. Productivity widely varies across NHS hospitals. This research investigates factors that may explain variations in hospital productivity. We employ a non-parametric panel regression technique to explore the...

Infra-monthly time series have increasingly appeared on the radar of official statistics in recent years, mostly as a consequence of a general digital transformation process and the outbreak of the COVID-19 pandemic in 2020. Many of those series are seasonal and thus in need for seasonal...

Infra-monthly economic time series have become increasingly popular in official statistics in recent years. This evolution has been largely fostered by official statistics’ digital transformation during the last decade. The COVID-19 pandemic outbreak in 2020 has added fuel to the fire as many...