Sebastian Luckeneder (p), Tamás Krisztin , Stefan Giljum

Tamás Krisztin

The implications of metal ore extraction are an important topic in academic and policy debates. This work investigates whether mining activities relate to the economic performance of mining regions and their surrounding areas. Usually, sub-national impact assessments of mining activities are conducted in the form of qualitative in-field case studies and focus on a smaller sample of mining properties and regions. In contrast, we employ a Spatial Durbin Model (SDM) with heteroskedastic errors to provide a flexible econometric framework to measure the impact of natural resource extraction. The study exploits a panel of 32 Mexican, 24 Peruvian and 16 Chilean regions over the period 2008 - 2015 and, in doing so, relates mine-specific data on extraction intensity to regional economic impacts. The results suggest that mining intensity does not significantly affect regional economic growth in both short-run and medium-run growth models. Popular arguments of the mining industry that the extractive sector would trigger positive impulses for regional economic development cannot be verified. Rather, the findings support narratives that mining regions do not benefit from their wealth in natural resources due to low labour intensity, loose links to local suppliers and profit outflows.

Florian Huber, Michael Pfarrhofer , Philipp Piribauer (p)

Tamás Krisztin

Policy makers in the European Central Bank require precise measures and forecasts for aggregate output and inflation to efficiently enact expansionary or restrictive policies for the euro area. Focusing on aggregate measures, however, entails the risk of obscuring important country-specific dynamics, including wrongly identifying country-specific shocks as threats to aggregate European inflation or output. In this paper we develop a multi-country business cycle model for the euro area (EA). The proposed model assumes that country-specific business cycles are driven by a common latent factor and thus exploits cross-sectional information in the data. Specifically, we develop a multivariate dynamic factor model that exploits euro area country-specific information on output and inflation for estimating an area-wide measure of the output gap. In the proposed multi-country framework we moreover allow for flexible stochastic volatility (SV) specifications for both the error variances and the innovations to the latent quantities in order to deal with potential changes in the commonalities of business cycle movements. By tracing the relative importance of the common euro area output gap component as a means to explaining movements in both output and inflation over time, the paper provides valuable insights in the evolution of the degree of synchronicity of the country-specific business cycles. In an out-of-sample forecasting exercise, the paper shows that the proposed approach performs well as compared to other well-known benchmark specifications.

Florian Huber, Manfred M. Fischer, Michael Pfarrhofer, Petra Staufer-Steinocher (p)

Tamás Krisztin

In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida.

Tamás Krisztin (p), Philipp Piribauer

Philipp Piribauer

In this paper we propose a Bayesian estimation approach for a spatial autoregressive logit specification. While estimation for spatial autoregressive probit models are already well documented, the involved computational problems renders estimation of spatial logit specifications particularly difficult. Our proposed approach uses recent advances in Bayesian computing, making use of Pólya-Gamma sampling for Bayesian Markov-chain Monte Carlo algorithms. Specifically, the proposed specification assumes that the involved log-odds of the model specification follows a spatial autoregressive process. The use of Pólya-Gamma sampling involves a computationally efficient treatment of spatial autoregressive logit models, allowing to extend existing specification in an elegant and straightforward way. In a Monte Carlo study we show that our proposed approach significantly outperforms existing spatial autoregressive probit specifications both in estimation precision and computation time. The paper moreover uses European regional data on foreign direct investment (FDI) activities to illustrate the performance of the proposed Bayesian spatial autoregressive logit specification. In the empirical application we use information on FDI press announcements from the fdiMarkets data base, which is maintained by a specialist division of Financial Times Ltd. Specifically, we aim at modelling the occurrence of greenfield FDI announcements in host regions in Europe by explicitly accounting for spatial autoregressive log-odds and regional neighbourhood characteristics.