Spatial Propagation of Macroeconomic Shocks in Europe CRED WP 2010 / 09

This paper develops a Spatial Vector Auto-Regressive (SpVAR) model that takes into account both the time and the spatial dimensions of economic shocks. We apply this framework to analyze the propagation through space and time of macroeconomic (ination, output gap and interest rate) shocks in Europe. The empirical analysis identi es an economically and statistically signi cant spatial component in the transmission of macroeconomic shocks in Europe. JEL classi cations: E3, E43, E52, C51, C33 Keywords: Macroeconomics, Spatial Models, VAR We acknowledge nancial support from FWO grant N G.0626. 07. yCES, University of Leuven, RSM Rotterdam and CESIFO. zCRED, CEREFIM-University of Namur, CES, University of Leuven. xCES, University of Leuven. 1 Introduction Over the last decades, spatial econometrics has emerged as a distinct eld in econometrics. Contributions in this eld include Anselin (2001), Cli¤ & Ord (1981), Elhorst (2003), Kapoor et al. (2007), Lee (2004), Lee & Yu (2010), and LeSage & R. Kelley (2004) (see Anselin (2010) and Lee & Yu (2009) for a recent review of the literature), while textbook treatments of spatial econometrics can be found in Anselin (1988) and LeSage & Pace (2009). The development and success of (dynamic) spatial econometrics primarily stems from the need to appropriately account for economic dynamics with a pronounced spatial dependence. Examples of data generating this kind of dynamics comprise housing prices, economic activity, nancial indicators and census data in general. The presence of such a spatial dependence necessitates the modeling of the joint dynamics. However, standard time series techniques, i.e. unrestricted VARmodels, run quickly into problems associated with the so-called curse of dimensionality. Spatial econometrics provides tools to overcome the curse of dimensionality by introducing, next to temporal lags, the concept of spatial lags, which allows to concisely capture spatial dependence.1 In this paper, we develop a Spatial Vector Auto-Regressive (SpV AR) model to account for both the time and spatial dimensions of standard macroeconomic shocks. In particular, we model the interdependence across three key macroeconomic indicators for a set of European countries. Speci cally, we focus on ination, output and interest rate dynamics across eleven European countries. For these purposes, Spatial Auto-Regressive models (SAR) including both spatial and temporal lags (see Anselin (1988), Anselin et al. (2008), Cli¤& Ord (1981), Lee & Yu (2009) and LeSage & Pace (2009)) are estimated for, respectively, ination, output and interest rate dynamics. Subsequently, we aggregate the variable-speci c SAR models into a SpV AR model by taking into account the dynamic interactions between the respective economic variables. The model is estimated using standard ML techniques and the spatial propagation of shocks is analyzed by means of impulse response functions and variance decompositions. The SpV AR model used in this paper is a restricted version of Global V AR (GV AR) models introduced in the literature (see for instance M.H. et al. (2004), di Mauro et al. (2007)). In particular, the SpV AR and the GV AR speci cations model spatial dependence 1Note that regional interdependency issues can also be addressed in factor models. Applications include Kose et al. (2003), Forni & Reichlin (1998) and Houssa (2008).