Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects.

This paper studies the convergence of per capita GDP across European regions over a fairly long period. Most of the works are based on either cross-sectional or fixed-effects estimates. We propose the estimation of convergence in per capita GDP across European regions by making use of panel-data models extended to include spatial error autocorrelation and spatially lagged dependent variable (Anselin,1988;Elhorst,2002). This will allow us to extend the traditional ß convergence model to include a rigorous treatment of the spatial correlation among the intercept terms. A spatial analysis of such intercept terms will also be performed in order to shed light on the concept spatially conditional convergence.

sphet: Spatial Models with Heteroskedastic Innovations in R

<b>sphet</b> is a package for estimating and testing spatial models with heteroskedastic innovations. We implement recent generalized moments estimators and semiparametric methods for the estimation of the coefficients variance-covariance matrix. This paper is a general description of <b>sphet</b> and all functionalities are illustrated by application to the popular Boston housing dataset. The package in its current version is limited to the estimators based on Arraiz, Drukker, Kelejian, and Prucha (2010); Kelejian and Prucha (2007, 2010). The estimation functions implemented in <b>sphet</b> are able to deal with virtually any sample size

Impact estimates for static spatial panel data models in R

In the present note we demonstrate how to implement the Lee and Yu (2010) procedure for fixed effects spatial panel data models available from the R (R Development Core Team 2012) package splm (Millo and Piras 2012). Additionally, we also show how to compute the impact estimates introduced by Kelejian, Tavlas, and Hondroyiannis (2006) and formalized in LeSage and Pace (2009). Unlike Matlab (MATLAB 2011), there was no R function specific to static panel data models for the calculation of the impact measures. After receiving numerous requests from the users of splm, we decided to extend the cross sectional functions available from spdep (Bivand 2013) to spatial panel data models

Analyzing Intra-Distribution Dynamics: A Reappraisal

In this paper we suggest an alternative estimator and an alternative graphical analysis, both developed by Hyndman et al. (1996), to describe the law of motion of cross-sectional distributions of per-capita income and its components in Europe. This estimator has better properties than the kernel density estimator generally used in the literature on intra-distribution dynamics (cf. Quah, 1997). By using the new estimator, we obtain evidence of a very strong persistent behavior of the regions considered in the study, that is poor regions tend to remain poorer and rich regions tend to remain richer. These results are also in line with the most recent literature available on the distribution dynamic approach to regional convergence (Pittau and Zelli, 2006).

L'antiga comparseria catalana del Corpus Christi: l'Alguer i Berga com exemple

La comparseria festiva catalana estava integrada per diversos elements que eren comuns a tot el territori catalanoparlan

Comparing Implementations of Estimation Methods for Spatial Econometrics

Recent advances in the implementation of spatial econometrics model estimation techniques have made it desirable to compare results, which should correspond between implementations across software applications for the same data. These model estimation techniques are associated with methods for estimating impacts (emanating effects), which are also presented and compared. This review constitutes an up to date comparison of generalized method of moments (GMM) and maximum likelihood (ML) implementations now available. The comparison uses the cross sectional US county data set provided by Drukker, Prucha, and Raciborski (2011c, pp. 6-7). The comparisons will be cast in the context of alternatives using the MATLAB Spatial Econometrics toolbox, Stata, Python with PySAL (GMM) and R packages including sped, sphet and McSpatial

splm: Spatial Panel Data Models in R

splm is an R package for the estimation and testing of various spatial panel data specifications. We consider the implementation of both maximum likelihood and generalized moments estimators in the context of fixed as well as random effects spatial panel data models. This paper is a general description of splm and all functionalities are illustrated using a well-known example taken from Munnell (1990) with productivity data on 48 US states observed over 17 years. We perform comparisons with other available software; and, when this is not possible, Monte Carlo results support our original implementation

GMM Estimators for Binary Spatial Models in R

Despite the huge availability of software to estimate cross-sectional spatial models, there are only few functions to estimate models dealing with spatial limited dependent variable. This paper fills this gap introducing the new R package spldv. The package is based on generalized methods of moment (GMM) estimators and includes a series of one- and two-step estimators based on different choices of the weighting matrix for the moments conditions in the first step, and different estimators for the variance-covariance matrix of the estimated coefficients. An important feature of spldv is that users can estimate the spatial Durbin model and compute the direct, indirect, and total effects in a friendly and flexible way

The distributional effects of NAFTA in Mexico: evidence from a panel of municipalities

This paper studies the regional distribution of the benefits from trade in Mexico after NAFTA. Specifically, we ask whether or not NAFTA has increased the concentration of economic activity in Mexico. Unlike previous work which uses state-level data, we identify the effect of NAFTA on economic activity at the municipal level allowing us to observe detailed growth patterns across space. Further, to explicitly identify the effect of the trade agreement, we compare results for growth in traded and non-traded sectors. Given the spatial nature of these data, we make explicit use of spatial econometrics methods. We find that NAFTA caused the wealthy regions nearest to the border to grow faster than others, increasing regional disparity. Second, we find that larger municipalities experienced greater per-capita economic benefits from NAFTA. This effect is particularly noticeable in the north. Somewhat surprisingly, we find that regions with a less literate workforce and worse infrastructure grew faster than other areas after the trade agreement, decreasing regional disparity. We notice these redistributive effects occur primarily in the non-traded sectors.Regional Disparities, Trade Liberalization, Agglomeration Economies, Economic Growth, Mexico, Transport Cost, Spatial econometrics, Community/Rural/Urban Development, International Development, International Relations/Trade,

Estimation of Spatial Models with Endogenous Weighting Matrices and an Application to a Demand Model for Cigarettes

Weighting matrices are typically assumed to be exogenous. However, in many cases this exogeneity assumption may not be reasonable. In these cases, typical model specifications and corresponding estimation procedures will no longer be valid. In this paper we specify a spatial panel data model which contains a spatially lagged dependent variable in terms of an endogenous weighting matrix. We suggest an estimator for the regression parameters, and demonstrate its consistency and asymptotic normality. We also suggest an estimator for the large sample variance-covariance matrix of that distribution. We then apply our results to an interstate panel data cigarette demand model which contains an endogenous weighting matrix. Among other things, our results suggest that, if properly accounted for, the bootlegging effect of buyers, or “agents” for them, crossing state borders to purchase cigarette turns out to be positive and significant