nent of cross-border financial flows—tends to respond to structural, long-term determinants, such as the institutional rating of the economy. This is consistent with our understanding that portfolio flows react most to the various effects of _conventional_ monetary policy—after all, monetary policy is generally effective only in the short run, and portfolio flows are by definition the most easily reassigned—and this bias toward shorter-term flows evidently carries over to unconventional monetary policy as well.

Turning to the portfolio flow decompositions, we first note that the statistically significant coefficients in columns ( _D1_ ) and ( _D4_ ) are broadly comparable, which lends credibility to our use of the fund inflows data. In terms of the decomposition, it is notable that while bond flows appear to react to more transmission channels than equity flows—debt is associated with changes in the VIX as well as the global PMI,<sup>34</sup> while equity is not—the _magnitude_ (and standard errors) of the coefficients on equity are generally larger than those for debt. Alternatively, although bond flows are liable to react to a wider range of possible QE transmission channels, equities react more strongly to the few channels to which they do react to.

# **6.3 Accounting for commonality in gross inflows**

Given the strong representation of global factors our baseline specification, it is natural to question whether how pervasive this source of commonality is in our data. In this subsection, we probe the relative importance of our global factors using a principal components approach inspired by Longstaff, Pan, Pedersen & Singleton (2011).<sup>35</sup> We construct two alternative global factors from our gross inflows data: a factor constructed from the varimax orthogonal rotation of the first principal component of the vector of gross inflows (which we term _PC1_ ), and a proportion-weighted sum of the first three principal components (which we term _PC3_ ).<sup>36</sup> We then use the global factor—which by construction captures the common elements among cross-country gross inflows—as a dependent variable in regressions where we include our global variables as regressors.

Table 6 reports our results from this exercise. The first two columns report (OLS) regressions for, respectively, the first and weighted principal components, using only global variables as covariates. Since our goal is to ascertain the relative importance of these global variables in gross inflows, this

> 34This coefficient is negative, which indicates that inflows into debt decrease when global growth prospects improve. This outcome is consistent with substitution into riskier assets when growth outlooks turn upward.

> 35In contrast to Longstaff _et al._ (2011), we extract principal components directly from actual gross inflows, rather than its correlation matrix. We then use this extracted factor _directly_ as our dependent variable in the analyses that follow (as an aggregate time series), rather than perform country-specific or panel regressions.

> 36The first three components had eigenvalues in the excess of 3, and cumulatively explain slightly more than half of the cross-country variation. Including the next 7 components (all components that have eigenvalues greater than unity) increases the explained variation to 87 percent, but at the cost of the weighted-sum component being comprised of a very large number of (linearly uncorrelated) subcomponents, none of which singularly contribute much to the overall variance in the data.

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