1,720,983 research outputs found
Evaluating the effects of heteroscedasticity on the economic growth of EU regions
No full textThe hypothesis of homoscedasticity of errors is convenient for the simplification of the estimation procedures. Unfortunately, this assumption is rather restrictive in the case of the analysis of spatially distributed data. Spatial units, in fact, can be very different in size and in other economic characteristics. This circumstance suggests the presence of heteroscedasticity in this typology of data. In this paper we study the effects of heteroscedasticity in regional economic convergence. We use two different estimators of the coefficient of variance and covariance matrix recently introduced in spatial econometrics literature that take into account the heteroscedasticity highlighted by the error terms. This methodology can be considered a suitable alternative to the identification of convergence clubs that represents a very popular approach for the analysis of structural economic differences between regions. The empirical analysis concerns the estimate of conditional economic convergence on EU NUTS 2 regions for the period 1981-2004
Discussione su "Il grado di interazione tra Mezzogiorno e Centro Nord: evidenze empiriche da un modello VAR multi-regionale
No full text
Economic inequality, spatial scale, and spatial concentration
No full textSpatial effects have been mainly neglected in the regional inequality literature.
Little attention has been paid to the relationship between inequality and spatial autocorrelation
and to the sensitivity of the inequality measures to the choice of spatial scale. This paper tries
to address these issues analyzing regional income inequality in Italy, over the period 1981-
2008, mainly through a spatial decomposition of the Gini coefficient
Forecasting aggregated Euro area inflation rate with space-time models
No full textEconomic variables are typically observed over time and across different but
likely correlated areas. When interested in forecasting the aggregate across the various
areas, a question that naturally arises is whether gains in efficiency can be obtained using
a direct approach or an indirect approach. This issue has been recently considered in
Giacomini and Granger (2004), where it is shown that stationary space-time AR(1, 1)
models are relatively more efficient than traditional ARMAs and V ARs models in
terms of forecasting accuracy. We extend these findings by considering a more general
and realistic non-stationary context, where cointegration constraints in time are allowed
to exist. A concrete application with monthly inflation rate for Euro-zone economies is
presente
Discriminant analysis using markovian automodels
No full textSpatially 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
Dynamic spatial regimes for spatial panel data
No full textSpatial heterogeneity in terms of spatially-varying coefficients is often not properly considered in modeling economic data. This neglect might cause serious problems in the estimation of the parameters of a model specification when group-wise heterogeneity is at work. In this paper we propose a two-step algorithm for the identification of endogenous (data-driven) spatial regimes by using an iterative procedure that is based on weighting functions updated dynamically over time. In the first step, clusters of spatial units (i.e. spatial regimes) are defined using both space and time information. In the second step, a spatial panel data model with random effects is estimated with the spatial regimes identified in the previous step. The additional random effects assumption on the model specification ensures the possibility of controlling also for individual effects as well as group-wise slope coefficients. The proposed method is applied to two real data sets to illustrate our procedure
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