Bayesian Model Averaging (BMA) is a common econometric tool to assess the uncertainty regarding model specification and parameter inference and is widely applied in fields where no strong theoretical guidelines are present. Its major advantage over single-equation models is the combination of evidence from a large number of specifications. The three papers included in this thesis all investigate model structures in the BMA model space. The first contribution evaluates how priors can be chosen to enforce model structures in the presence of interactions terms and multicollinearity. This is linked to a discussion in the Journal of Applied Econometrics regarding the question whether being a Sub-Saharan African country makes a difference for growth modelling. The second essay is concerned with clusters of different models in the model space. We apply Latent Class Analysis to the set of sampled models from BMA and identify different subsets (kinds of) models for two well-known growth data sets. The last paper focuses on the application of "jointness", which tries to find bivariate relationships between regressors in BMA. Accordingly this approach attempts to identify substitutes and complements by linking the econometric discussion on this subject to the field of Machine Learning.(author's abstract)

Spatial interaction models of the gravity type are used in conjunction with sample data on flows between origin and destination locations to analyse international and interregional trade, commodity, migration and commuting patterns. The focus is on the classical log-normal model version and spatial econometric extensions that have recently appeared in the literature. These new models replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for spatial dependence in flow magnitudes. The paper also discusses problems that arise in applied practice when estimating (log-normal) spatial interaction models. (authors' abstract)

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.

This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries. In addition, a correct interpretation of the spatial regression parameter estimates that takes into account the simultaneous feed- back nature of the spatial autoregressive model is provided. Our findings indicate that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates. (authors' abstract)

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A six-year panel of 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity. (authors' abstract)

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. We provide an econometric derivation of the relationship and show that the presence of latent/unobservable regional knowledge capital leads to a model relationship that includes both spatial and technological dependence. This model specification accounts for both spatial and technological dependence between regions, which allows us to quantify spillover impacts arising from both types of interaction. Sample data on 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity. (authors' abstract)