OeNB Publications

If you have any further questions on the studies, please contact us.

See also:

 

Stress Testing Austrian Households ( Albacete, Fessler)

Auswirkungen des Zahlungsdienstegesetzes auf den österreichischen Finanzmarkt (Freitag, Schimka)

Die Relevanz österreichischer KAGs und Investmentfonds für die Finanzmarktstabilität (Kavan, Sedlacek, Seliger, Ubl)

Banking sector Kazakhstan (Barisitz)

 

Shocks, the Crisis and Inflation Expectations Uncertainty: Some Theory and Evidence for the Euro Area (Ernest Gnan, Johannes Langthaler, Maria Valderrama)

The Relationship between Competition and Inflation(Philipp Schmidt-Dengler, Jürgen Janger)

Determinants for Sectoral Price Comparison and Switching Rates in Austria(Jürgen Janger)

Bank Recapitalization and Restructuring: An Economic Analysis of Various Options(Helmut Elsinger, Martin Summer)

 

Privat Sector Credit in CESEE: Long-Run Relationships and Short-Run Dynamics (Eller, Frömmel, Szentric)

OeNB Euro Survey (Dvorsky, Scheiber, Stix)

Contagion and Spillovers – New Insights from the Crisis (Backé, Feldkircher, Gnan, Lahnsteiner) (report on SUERF conference / Special OeNB East Jour Fixe on February 12, 2010)

The Working Paper series of the Oesterreichische Nationalbank is designed to disseminate and to provide a platform for discussion of work of either the staff of the OeNB economists or outside contributors on topics which are of special interest to the OeNB. To ensure the high quality of their content, the contributions are subjected to an international refereeing process.

All working papers:

Working Papers

Working Paper 160

“Spatial Filtering, Model Uncertainty and the Speed of Income Convergence in Europe” (January 11, 2010) by Jesús Crespo Cuaresma and Martin Feldkircher

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