We develop a theoretical model in which whites mainly use private vehicles to commute whereas non-whites use public transportation. We show that, for whites and non-whites, higher (time) distance-to-jobs leads to lower search effort. Because of different transport modes, we also show that, at...

A new and extensive panel of outward foreign direct investment (FDI) at the sector level is used to estimate the determinants of non-resource and resource FDI. Since FDI is I(1), we estimate panel error-correction models of FDI with spatial lags for FDI and market potential. Our main result is...

This paper estimates the impact of public investment on regional economic growth and convergence at the NUTS III level in Greece. Using a new database of public expenditure per region for the period 1978-2007, it proposes a model which captures not just the impact of public investment in Greek...

This paper is concerned with methods for analysing spatial data. After initial discussion on the nature of spatial data, including the concept of randomness, we focus most of our attention on linear regression models that involve interactions between agents across space. The introduction of...

Based on a modified Spatiotemporal Autoregressive Model (STAR), we analyze whether borders still constitute significant impediments to labor market integration in the European Union, despite the formal law of free movement of labor. Using regional data from the EU-15 countries over 21 years, we...

Geography shapes economic outcomes in a major way. This Paper uses spatial empirical methods to detect and analyse trade patterns in a historical dataset on Chinese rice prices. Our results suggest that spatial features were important for the expansion of interregional trade. Geography dictates,...

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model that accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for...

This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those...

We consider mean-variance portfolio choice of a robust investor. The investor receives advice from J experts, each with a different prior for the distribution of returns. Confronted with these multiple priors the investor follows a min-max portfolio strategy. We study the structure of the robust...

We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest estimating the appropriate grouping of time series simultaneously along with the group-specific model parameters. We cast...