Paolo Postiglione

A regression tree algorithm for the identification of convergence clubs

The concept of convergence clubs is analyzed and compared with classical methods for the study of economic @b-convergence, which often consider the entire data set as one sample. A technique for the identification of convergence clubs is proposed. The algorithm is based on a modified version of the usual regression trees procedure. The objective function of the method is represented by the difference among the parameters of the model under investigation. Different strategies are adopted in the definition of the model used in the objective function of the algorithm. The first is the classical non-spatial @b-convergence model. The others are modified @b-convergence models which take into account the dependence showed by spatially distributed data. The proposed procedure identifies situation of local stationarity in the economic growth of the different regions: a group of regions is divided into two sub-groups if the parameter estimates are significantly different among them. The algorithm is applied to 191 European regions for the period 1980-2002. Given the adaptability of the algorithm, its implementation provides a flexible tool for the use of any regression model in the analysis of non-stationary spatial data.

Semiparametric modelling of spatial binary observations

In the past decade various attempts have been made to extend standard random effects models to the analysis of spatial observations. This extension is a source of theoretical difficulty due to the multidirectional dependence among nearest observations; much of the previous work was based on parametric assumptions about the random effects distribution. To avoid any restriction, we propose a conditional model for spatial binary responses, without assuming a parametric distribution for the random effects. The model parameters are estimated using the EM algorithm for nonparametric maximum likelihood estimation of a mixing distribution. To illustrate the proposed approach, the model is applied to a remote sensed image of the Nebrodi Mountains (Italy).

Hierarchical spatial interaction among the Italian regions: a nonlinear relative dynamics approach

Regional interactions and spillover effects should be considered as important factors in growth analysis of regional economies. Using modified versions of the Dendrinos–Sonis model, this paper analyses the spatial hierarchical system of Italy. The interaction among Italian regions is considered at three different levels of spatial aggregation, the NUTS-1, NUTS-2 and NUTS-3 levels. Compared to more popular spatial econometric approaches, the Dendrinos–Sonis model and its extensions provide greater flexibility in the way interaction between regions is handled but the results strongly depend on the choice of a reference region.

A two-step approach to account for unobserved spatial heterogeneity

ABSTRACT A two-step approach to account for unobserved spatial heterogeneity. Spatial Economic Analysis. Empirical analysis in economics often faces the difficulty that the data are correlated and heterogeneous in some unknown form. Spatial econometric models have been widely used to account for dependence structures, but the problem of directly dealing with unobserved spatial heterogeneity has been largely unexplored. The problem can be serious particularly if we have no prior information justified by economic theory. In this paper we propose a two-step procedure to identify endogenously spatial regimes in the first step and to account for spatial dependence in the second step. This procedure is applied to hedonic house price analysis.

A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data

Abstract The missing data problem has been widely addressed in the literature. The traditional methods for handling missing data may be not suited to spatial data, which can exhibit distinctive structures of dependence and/or heterogeneity. As a possible solution to the spatial missing data problem, this paper proposes an approach that combines the Bayesian Interpolation method [Benedetti, R. & Palma, D. (1994) Markov random field-based image subsampling method, Journal of Applied Statistics, 21(5), 495–509] with a multiple imputation procedure. The method is developed in a univariate and a multivariate framework, and its performance is evaluated through an empirical illustration based on data related to labour productivity in European regions.

Le componenti principali pesate geograficamente per la definizione di indicatori compositi locali

Il presente lavoro analizza il problema della costruzione degli indicatori compositi a livello locale. Gli indicatori rappresentano sempre piu un valido strumento di ausilio per la definizione di interventi adeguati di policy che siano basati su un’effettiva analisi della realta. Un indicatore composito misura concetti multidimensionali che sono difficili da comprendere attraverso l’analisi di una molteplicita di indicatori semplici. Il problema della sintesi degli indicatori semplici e un argomento spesso dibattuto nella letteratura specialistica. La tecnica statistica dell’analisi in componenti principali e uno strumento frequentemente utilizzato per risolvere tale problema. In generale, quando l’unita statistica di osservazione e geo-riferita, la versione classica dell’analisi in componenti principali risulta non adeguata per la sintesi di indicatori semplici. Infatti, usando l’analisi in componenti principali standard, vengono trascurati alcuni effetti spaziali che caratterizzano in modo cruciale le unita che si distribuiscono sul territorio. In particolare, possono essere considerati gli effetti di eterogeneita e dipendenza spaziale. In questo articolo, gli autori applicano una tecnica di analisi in componenti principali pesata geograficamente che e stata introdotta recentemente in letteratura. Tale tecnica tiene in debita considerazione l’effetto di eterogeneita spaziale. La metodologia e utilizzata al fine della definizione di indicatori compositi di benessere a livello locale. In particolare, il caso di studio riguarda le 110 province italiane per l’anno 2011. I risultati evidenziano come l’eterogeneita spaziale non possa essere ignorata quando si analizzano dati rilevati su unita territoriali, e pertanto, l’utilizzo del¬l’analisi in componenti principali modificata spazialmente risulta piu adeguata per lo studio del fenomeno sotto investigazione.

Discriminant Analysis Using Markovian Automodels

Spatially distributed observations occur naturally in a number of empirical situations; their analysis represents a significant source of theoretical challenge due to the multidirectional dependence among nearest observations. The presence of a dependence often causes the standard statistical methods, instead based on independence assumptions, to fail badly. This paper concerns the problem of discrimination and classification of spatial binary data. It presents a suitable discrimination function based on Markovian automodels and suggests a solution to the allocation problem through a Gibbs sampler-based procedure.

Modelli spaziali di regressione quantilica per l’analisi della convergenza economica regionale

La letteratura in materia di convergenza regionale ha utilizzato finora principalmente modelli parametrici volti alla semplice individuazione di un parametro globale di β-convergenza. Sembra piu plausibile, pero, che la convergenza economica si realizzi solo sotto l’ipotesi di gruppi eterogenei (Postiglione et al., 2013). In aggiunta la presenza di dipendenza a livello spaziale richiederebbe una piu approfondita e accurata analisi degli spillover. In questo lavoro, si propone l’utilizzo della spatial quantile regression al fine di delineare le distribuzioni condizionate della crescita economica e ottenere uno strumento utile a considerare la difformita dei parametri in corrispondenza di diversi quantili. Le variabili condizionanti utilizzati sono quelli del modello di crescita introdotto da Mankiw et al. (1992). Il classico modello di quantile regression (Koenker, 2005) e arricchito all’interno di un modello aumentato spazialmente di tipo spatial Durbin (McMillen, 2013). Il metodo sara verificato sulle 103 province italiane. Le conclusioni evidenziano che il PIL, il capitale umano, il tasso di risparmio e il tasso di crescita della popolazione incidono in maniera significativamente differente a seconda del quantile osservato e ci permettono di svolgere importanti considerazioni su differenti sentieri di crescita.

Advanced Methods to Design Samples for Land Use/Land Cover Surveys

The particular characteristics of geographically distributed data should be taken into account in designing land use/land cover survey. The traditional sampling designs might not address the specificity of this survey. In fact, in the presence of spatial homogeneity of the phenomenon to be sampled, it is desirable to make use of this information in the sampling design. This paper discusses several methods for sampling spatial units that have been recently introduced in literature. The main assumption is to consider the geographical space as a finite population. The methodological framework is of design-based typology. The techniques outlined are: the GRTS, the cube, the SPCS, the LPMs, and the PPDs. These methods will be verified on data deriving from LUCAS 2012.

Spatial Survey Data Modeling

The predictive approach and the analysis of survey data are two topics that have only attracted a small amount of attention when compared with the traditional approach of sampling from a finite population.