Spatial Filtering, Model Uncertainty and the Speed of Income Convergence in Europe
Abstract
In this paper we put forward a Bayesian Model Averaging method dealing with model uncertainty in the presence of potential spatial autocorrelation. The method uses spatial filtering in order to account for different types of spatial links. We contribute to existing methods that handle spatial dependence among observations by explicitly taking care of uncertainty stemming from the choice of a particular spatial structure. Our method is applied to estimate the conditional speed of income convergence across 255 NUTS-2 European regions for the period from 1995 to 2005. We show that the choice of a spatial weight matrix - and in particular the choice of a class thereof - can have an important effect on the estimates of the parameters attached to the model covariates. We also show that estimates of the speed of income convergence across European regions depend strongly on the form of the spatial patterns which are assumed to underlie the dataset. When we take into account this dimension of model uncertainty, the posterior distribution of the speed of convergence parameter has a large probability mass around a rate of convergence of 1%, approximately half of the value which is usually reported in the literature. JEL classification: C11, C15, C21, R11, O52Download Info
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Paper provided by Oesterreichische Nationalbank (Austrian Central Bank) in its series Working Papers with number 160.
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Length: 37 pages
Date of creation: 11 Jan 2010
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Handle: RePEc:onb:oenbwp:160
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For corrections or technical questions regarding this item, or to correct its listing, contact: (Markus Knell and Helmut Stix).
Related research
Keywords: Model uncertainty; spatial filtering; determinants of economic growth; European regions;Other versions of this item:
- Jesus Crespo Cuaresma & Martin Feldkircher, 2009. "Spatial Filtering, Model Uncertainty and the Speed of Income Convergence in Europe," Working Papers 2009-17, Faculty of Economics and Statistics, University of Innsbruck.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- R11 - Urban, Rural, Regional and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
- O52 - Economic Development, Technological Change, and Growth - - Economywide Country Studies - - - Europe
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-02-05 (All new papers)
- NEP-ECM-2010-02-05 (Econometrics)
- NEP-EEC-2010-02-05 (European Economics)
- NEP-TRA-2010-02-05 (Transition Economics)
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Jesús Crespo Cuaresma, & Martin Feldkircher & Peter Mayerhofer, 2010. "Regional Convergence in Europe and the Role of Urban Agglomerations," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 64â78, August.
- Jesús Crespo-Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2009.
"The Determinants of Economic Growth in European Regions,"
CESifo Working Paper Series
2519, CESifo Group Munich.
- Jesus Crespo-Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2009. "The Determinants of Economic Growth in European Regions," wiiw Working Papers 57, The Vienna Institute for International Economic Studies, wiiw.
- Jesus Crespo Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2008. "The Determinants of Economic Growth in European Regions," Working Papers 2008-26, Faculty of Economics and Statistics, University of Innsbruck.
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