Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models

This article introduces two absolutely continuous global-local shrinkage priors to enable stochastic variable selection in the context of high-dimensional matrix exponential spatial specifications. Existing approaches as a means to dealing with overparameterization problems in spatial autoregressive specifications typically rely on computationally demanding Bayesian model-averaging techniques. The proposed shrinkage priors can be implemented using Markov chain Monte Carlo methods in a flexible and efficient way. A simulation study is conducted to evaluate the performance of each of the shrinkage priors. Results suggest that they perform particularly well in high-dimensional environments, especially when the number of parameters to estimate exceeds the number of observations. For an empirical illustration we use pan-European regional economic growth data.Comment: Keywords: Matrix exponential spatial specification, model selection, shrinkage priors, hierarchical modeling; JEL: C11, C21, C5

Thirty Years of Spatial Econometrics

In this paper, I give a personal view on the development of the field of spatial econometrics during the past thirty years. I argue that it has moved from the margins to the mainstream of applied econometrics and social science methodology. I distinguish three broad phases in the development, which I refer to as preconditions, takeoff and maturity. For each of these phases I describe the main methodological focus and list major contributions. I conclude with some speculations about future directions.

A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices

Abstract Land price studies typically employ hedonic analysis to identify the impact of land characteristics on price. Owing to the spatial fixity of land, however, the question of possible spatial dependence in agricultural land prices arises. The presence of spatial dependence in agricultural land prices can have serious consequences for the hedonic model analysis. Ignoring spatial autocorrelation can lead to biased estimates in land price hedonic models. We propose using a flexible quantile regression-based estimation of the spatial lag hedonic model allowing for varying effects of the characteristics and, more importantly, varying degrees of spatial autocorrelation. In applying this approach to a sample of agricultural land sales in Northern Ireland we find that the market effectively consists of two relatively separate segments. The larger of these two segments conforms to the conventional hedonic model with no spatial lag dependence, while the smaller, much thinner market segment exhibits considerable spatial lag dependence. Un mod�le h�donique � r�gression quantile spatiale des prix des terrains agricoles R�sum� Les �tudes sur le prix des terrains font g�n�ralement usage d'une analyse h�donique pour identifier l'impact des caract�ristiques des terrains sur le prix. Toutefois, du fait de la fixit� spatiale des terrains, la question d'une �ventuelle d�pendance spatiale sur la valeur des terrains agricoles se pose. L'existence d'une d�pendance spatiale dans le prix des terrains agricoles peut avoir des cons�quences importantes sur l'analyse du mod�le h�donique. En ignorant cette corr�lation s�rielle, on s'expose au risque d'�valuations biais�es des mod�les h�doniques du prix des terrains. Nous proposons l'emploi d'une estimation � base de r�gression flexible du mod�le h�donique � d�calage spatial, tenant compte de diff�rents effets des caract�ristiques, et surtout de diff�rents degr�s de corr�lations s�rielles spatiales. En appliquant ce principe � un �chantillon de ventes de terrains agricoles en Irlande du Nord, nous d�couvrons que le march� se compose de deux segments relativement distincts. Le plus important de ces deux segments est conforme au mod�le h�donique traditionnel, sans d�pendance du d�calage spatial, tandis que le deuxi�me segment du march�, plus petit et beaucoup plus �troit, pr�sente une d�pendance consid�rable du d�calage spatial. Un modelo hed�nico de regresi�n cuantil espacial de los precios del terreno agr�cola Resumen T�picamente, los estudios del precio de la tierra emplean un an�lisis hed�nico para identificar el impacto de las caracter�sticas de la tierra sobre el precio. No obstante, debido a la fijeza espacial de la tierra, surge la cuesti�n de una posible dependencia espacial en los precios del terreno agr�cola. La presencia de dependencia espacial en los precios del terreno agr�cola puede tener consecuencias graves para el modelo de an�lisis hed�nico. Ignorar la autocorrelaci�n espacial puede conducir a estimados parciales en los modelos hed�nicos del precio de la tierra. Proponemos el uso de una valoraci�n basada en una regresi�n cuantil flexible del modelo hed�nico del lapso espacial que tenga en cuenta los diversos efectos de las caracter�sticas y, particularmente, los diversos grados de autocorrelaci�n espacial. Al aplicar este planteamiento a una muestra de ventas de terreno agr�cola en Irlanda del Norte, descubrimos que el mercado consiste efectivamente de dos segmento relativamente separados. El m�s grande de estos dos segmentos se ajusta al modelo hed�nico convencional sin dependencia del lapso espacial, mientras que el segmento m�s peque�o, y mucho m�s fino, muestra una dependencia considerable del lapso espacial.Spatial lag, quantile regression, hedonic model, C13, C14, C21, Q24,

Cereal production and technology adoption in Ethiopia:

The Ethiopian government has been promoting a package-driven extension that combines credit, fertilizers, improved seeds, and better management practices. This approach has reached almost all farming communities, representing about 2 percent of agricultural gross domestic product in recent years. This paper is the first to look at the extent and determinants of the adoption of the fertilizer-seed technology package promoted in Ethiopia using nationally representative data from the Central Statistical Agency of Ethiopia. We estimate a double hurdle model of fertilizer use for four major cereal crops: barley, maize, teff, and wheat. Since maize is the only crop that exhibits considerable adoption of improved seed, we estimate a similar model for the adoption of improved seed in maize production. We find that access to fertilizer and seed is related to access to extension services and that production specialization together with wealth play a major role in explaining crop area under fertilizer and improved seed. One of the most important factors behind the limited adoption of the technological package is the inefficiency in the use of inputs, which implies that changes are needed in the seed and fertilizer systems and in the priorities of the extension service to promote more efficient use of inputs and to accommodate risks associated with agricultural production, especially among small and poor households.Agriculture, cereals, double-hurdle model, maize, Technical change, Technology adoption, teff, Wheat,

Spatial clustering and nonlinearities in the location of multinational firms

We propose a semiparametric geoadditive negative binomial model of industrial location which allows to simultaneously address some important methodological issues, such as spatial clustering and nonlinearities, which have been only partly addressed in previous studies. We apply this model to analyze location determinants of inward greenfield investments occurred over the 2003-2007 period in 249 European regions. The inclusion of a geoadditive component (a smooth spatial trend surface) allows to control for omitted variables which induce spatial clustering, and suggests that such unobserved factors may be related to regional policies towards foreign investors Allowing for nonlinearities reveals, in line with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some limit value.industrial location, negative binomial models, geoadditive models, european union.

An Analysis of the Pricing of Traits in the U.S. Corn Seed Market

This paper investigates the pricing of patented traits in the U.S. hybrid corn seed market under imperfect competition. In a multiproduct context, we first examine how substitution/complementarity relationships among products can affect pricing. This is used to motivate multi-product generalizations of the Herfindahl-Hirschman index (GHHI) capturing cross-market effects of imperfect competition on bundle pricing. The GHHI model is applied to pricing of conventional and patented biotech seeds in the US from 2000-2007. One major finding is that standard component pricing in biotech traits is soundly rejected in favor of subadditive bundle pricing. The econometric estimates show how changes in market structures (as measured by both own- and cross-Herfindahl indexes) affect U.S. corn seed prices.

Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data

Regions, independent of their geographic level of aggregation, are known to be interrelated partly due to their relative locations. Similar economic performance among regions can be attributed to proximity. Consequently, a proper understanding, and accounting, of spatial liaisons is needed in order to effectively forecast regional economic variables. Several spatial econometric techniques are available in the literature, which deal with the spatial autocorrelation in geographically-referenced data. The experiments carried out in this paper are concerned with the analysis of the spatial autocorrelation observed for unemployment rates in 439 NUTS-3 German districts. We employ a semi-parametric approach – spatial filtering – in order to uncover spatial patterns that are consistently significant over time. We first provide a brief overview of the spatial filtering method and illustrate the data set. Subsequently, we describe the empirical application carried out: that is, the spatial filtering analysis of regional unemployment rates in Germany. Furthermore, we exploit the resulting spatial filter as an explanatory variable in a panel modelling framework. Additional explanatory variables, such as average daily wages, are used in concurrence with the spatial filter. Our experiments show that the computed spatial filters account for most of the residual spatial autocorrelation in the data.spatial filtering, eigenvectors, Germany, unemployment

Spatial Weighting Matrix Selection in Spatial Lag Econometric Model

This paper investigates the choice of spatial weighting matrix in a spatial lag model framework. In the empirical literature the choice of spatial weighting matrix has been characterized by a great deal of arbitrariness. The number of possible spatial weighting matrices is large, which until recently was considered to prevent investigation into the appropriateness of the empirical choices. Recently Kostov (2010) proposed a new approach that transforms the problem into an equivalent variable selection problem. This article expands the latter transformation approach into a two-step selection procedure. The proposed approach aims at reducing the arbitrariness in the selection of spatial weighting matrix in spatial econometrics. This allows for a wide range of variable selection methods to be applied to the high dimensional problem of selection of spatial weighting matrix. The suggested approach consists of a screening step that reduces the number of candidate spatial weighting matrices followed by an estimation step selecting the final model. An empirical application of the proposed methodology is presented. In the latter a range of different combinations of screening and estimation methods are employed and found to produce similar results. The proposed methodology is shown to be able to approximate and provide indications to what the ‘true’ spatial weighting matrix could be even when it is not amongst the considered alternatives. The similarity in results obtained using different methods suggests that their relative computational costs could be primary reasons for their choice. Some further extensions and applications are also discussed

The determinants of the recent interregional migration flows in Italy: A panel data analysis

The present study investigates the determinants of interregional migration flows in Italy in the light of the upsurge occurred in 1996, after two decades of decreasing internal migration rates. We apply the fixed effect vector decomposition estimator (FEVD) on a gravity model using bilateral migration flows for the period 1996-2005 and show that it improves the estimates with respect to the traditional panel data estimators. We find that omitting distance and in presence of rarely time invariant covariates (e.g., population and income) the standard panel data models significantly bias the estimates. The overall economic level and the probability to find a job (proxied by per capita GDP and unemployment rate) appear to be the key variables whose changes are able to push flows of migrants away from their regions and to direct them to “better off” destinations. We find that migrants leaving the regions in the Centre-North respond differently to the push and pull forces with respect to southern migrants. We then estimate a dynamic model and find evidence for the presence of social networks which in our model take place between each pair of regions.Interregional migration, gravity model, panel data, FEVD.