Convergence analysis as distribution dynamics when data are spatially dependent

Conditional distributions for the analysis of convergence are usually estimated using a standard kernel smoother but this is known to be biased. Hyndman et al. (1996) thus suggest a conditional density estimator with a mean function specified by a local polynomial smoother, i.e. one with better bias properties. However, even in this case, the estimated conditional mean might be incorrect when observations are spatially dependent. Consequently, in this paper we study per capita income inequalities among European Functional Regions and U.S. Metropolitan Statistical Areas through a distribution dynamics approach in which the conditional mean is estimated via a procedure that allows for spatial dependence (Gerolimetto and Magrini, 2009).Regional convergence, Distribution dynamics, Nonparametric smoothing, Spatial dependence

Nonparametric regression with spatially dependent data

In this paper we present a new procedure for nonparametric regression in case of spatially dependent data. In particular, we extend usual local linear regression (along the lines of Martins-Filho and Yao, 2009) and propose a two-step method where information on spatial dependence is incorporated in the error covariance matrix, estimated nonparametrically. The finite sample performance of our proposed procedure is then shown via Monte Carlo simulations for various data generating processes.nonparametric smoothing, spatial dependence

Understanding the lead/lag structure among regional business cycles

The analysis of synchronization among regional or national business cycles has recently been attracting a growing interest within the economic literature. Far less attention has instead been devoted to a closely related issue: given a certain level of synchronization, some economies might be systematically ahead of others along the swings of the business cycle. In other words, there could be a lead/lag structure in which some economies systematically lead or lag behind others. In the present paper we aim at providing a thorough analysis of the lead/lag structure among a system of regional economies. This task is achieved in two steps. First, we show that leading (or lagging behind) is a feature that does not occur at random across the economies. Second, we investigate the economic drivers that could explain such a behavior. To do so, we employ data for 48 conterminous US states between 1979 and 2010.Regional business cycles, lead/lag structure, synchronization

Synchronization among real business cycles of U.S. states

In this paper we present a synchronization analysis of the real business cycles of U.S. states. We employ data on real GDP covering the period stretching from 2005:Q1 to 2020:Q2. From the methodological point of view, to isolate the business cycles we adopt a recently developed MatLab function for signal extraction. Once the business cycles of the U.S. states are extracted using CiSSA, we study their level of synchronization with the national one using various indices of synchronization. Moreover, based on the literature that documents how workers with different levels of education and human capital exhibit different level of propensity to migrate, we study the correlation between immigrant flows and the level of synchronization of the regional cycles with respect to the national on

Clustering time series: an application to COVID-19 data

In this paper we present an attempt of clustering time series focusing on Italian data about COVID-19. From the methodological point of view, we first present a review of the most important methods existing in literature for time series clustering. Similarly to cross-sectional clustering, time series clustering moves from the choice of an opportune algorithm to produce clusters. Several algorithms have been developed to carry out time series clustering and the choice of which one is more adapt depends on both the aim of the analysis itself and the typology of data at hand. We apply some of these methods to the data set of daily time series on intensive care and deaths for COVID19 stretching from, respectively, 23/02/2020 to 15/02/2022 and from 23/02/2020 to 29/03/2022. These data refer to the 19 Italian regions and the two autonomous provinces of Trento and Bolzano

Bootstrap methods for long-range dependence Monte Carlo evidence

In this paper we present a review of some well-known bootstrap methods for time series data. We concentrate on block bootstrap and sieve bootstrap, whose validity has been proved to be extended to stationary long memory time series. We will start by reviewing briefly the peculiar features of the bootstrap methods and the issues raised in case of long range dependent data; then we present a Monte Carlo experiment to compare the performance of the methods for a variety of ARFIMA processes. Comments about the finite sample performance of the methods will be provided also in light of the established theoretical properties of the method

Local inequality analysis in the US: evidence from some metropolitan statistical areas

In this paper we present an analysis of per capita personal income inequality within seven Metropolitan Statistical Areas of the USA between 2010 and 2018. The analysis is conducted by calculating several well-known income inequality indexes and their variations over the selected period. Then, for each MSA we produce kernel density estimates of the distributions in 2010 and 2018 and perform Kolmogorov-Smirnoff and Kramér-Von Mises tests to evaluate whether they are the same. All results unequivocally portray a picture of significant increases in per capita personal income inequalities

Estimating the long memory parameter in nonstationary models: further Monte Carlo evidence

In this work we perform a Monte Carlo experiment to show and compare the performance of a variety of estimators of the long memory parameter d in case of nonstationary processes. Both parametric and semiparametric estimators are considered. Moreover they have been employed both on the original time series and on the first difference of the series. Results show that the Whittle estimator is the best performing and the strategy of preliminarily differentiate the series is worthy, but not for all the estimators

Dynamic cointegration and relevant vector machine: the relationship between gold and silver

We use the Relevant Vector Machine, a technique of supervised learning introduced by Tipping (2001), to conduct a dynamic cointegration analysis on the time series of the price of gold and silver over the period 1971-2004. Unlike the results of traditional cointegration analysis, this study reveals that there is a dynamic long run relationship over the whole periodDynamic cointegration, relevant vector machine

Inference for inequality measures: a review

In this paper we present a review of the most recent contributes of the econometric literature on comparing inequality measures, focusing in particular on Theil index and Gini index. We will start by discussing the main issue behind this bulk of literature, which is the heavy tail of the income distribution. Specifically, the severity of the inference problem responds to the exact nature of the right tail of the distribution. Attention in the literature has been given to determining the limits of conventional inference in the presence of heavy tails and, in particular, of bootstrap inference. Then we review a number of methods based on alternative parametric bootstrap and, more recently on permutations that heated in this debate in the last 10 years