of these models– especially in terms of shocks and comparison of simulated and actual moments– often remain an open question. In this context, empirical estimates of spillovers using reduced-form models provide useful guidance for theoretical work (Canova and Marrinan 1998). Our empirical findings thus provide ground work for the specification and evaluation of DSGE models that feature both advanced and emerging market economies.<sup>5</sup> To estimate spillovers from EM7, we use a parsimonious Bayesian vector autoregressive (VAR) model. The model features growth in EM7, other emerging and frontier markets (EFM), and G7 (representing the major advanced economies) plus additional controls for global financing conditions and commodity prices. The set of variables included in the VAR is consistent with that of a standard small open economy DSGE model that is calibrated to capture the main features of most of the EFMs included in our sample (Kose 2002; Neumeyer and Perri 2005). Exogenous shocks to EM7 growth are first identified using a recursive identification scheme in the model, and then the dynamic effects of those shocks on growth in other economies (i.e. spillovers) are traced out. We estimate the model using data over the period 2000Q1-2015Q2 for G7 economies and 16 EFMs. We employ the same model to estimate spillovers originating from G7 and compare the size of G7 spillovers with those originating from EM7. We use a similar model to estimate and compare spillovers from individual EM7 economies. Thus, our empirical approach utilizes a common methodology that allows a disciplined comparison of spillovers originating from the EM7 and G7, and also from the individual EM7 economies.

Our main findings are as follows: First, spillovers from EM7 are significant: a 1 percentage point increase in EM7 growth is associated with a 0.9 percentage point increase in growth in other EFM and a 0.6 percentage point increase in world growth at the end of three years. Second, notwithstanding sizeable spillovers from EM7 to other EFM, spillovers originating from G7 economies are larger. Specifically, a 1 percentage point increase in G7 growth is associated with an increase in growth in other EFM and the global economy that is one-half to three times larger than a similarly sized increase in EM7 growth. Moreover, fluctuations in G7 growth explain a much larger fraction, about four times, of the variance of growth in other EFM and the global economy than fluctuations in EM7 growth. Third, among the EM7, spillovers from China are the largest and permeate globally, but spillovers originating from the other EM7 are much smaller.

The rest of the paper is organized as follows. Section 2 describes the econometric methodology and the database. We present the empirical results in Section 3. In Section 4, we discuss the robustness exercises. Section 5 concludes.

# 2 Methodology and Database

We use a set of Bayesian structural vector autoregression (VAR) models to quantify growth spillovers from EM7 to other economies in the world.<sup>6</sup> In this section, we describe the VAR models and the database used for estimation.

> 5 Economic theory is ambiguous about the implications of increased trade and financial linkages for spillovers (Frankel and Rose 1998; Doyle and Faust 2002; Kose, Prasad, and Terrones 2009). Our study is also related to this literature as it is useful in quantifying the magnitude of spillovers.

> 6 An alternative empirical approach for measuring spillovers is a panel regression framework. Compared with a structural VAR, the identification of exogenous growth shocks and tracing their dynamic effects are less straightforward. A related empirical approach is a dynamic factor model. Compared with a structural VAR, dynamic factor models are more useful in quantifying comovement among a large set of variables, but they are less useful for analyzing cross-border spillovers unless these spillovers are explicitly modeled.

3