The Importance of Global Shocks for National Policymakers: Rising Challenges for Central Banks

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5. Empirical results

5.1. Baseline FAVAR

The common factors are estimated by principal component analysis. As is common in
factor model applications, the variables are initially de-trended and standardized. Apart
from the short-term interest rates, all X_t are log first-differenced. For the short-term
interest rates only, first differences are calculated. Afterwards, all the de-trended
variables are standardized so that each of them has a mean of zero and a variance of one.
Otherwise, the results would have been systematically affected by cross-country
differences in variability.

For each global variable, the proportion of the total variance of the series
attributable to each principal component is calculated. For the first principal component
(PC1) to suitably qualify as a factor capturing international co-movement, one important
condition must be met. PC1 should explain a sufficiently large fraction of the total
variance of the relevant data set in comparison to the remaining principal components of
higher order. As can be seen in the table below, the requirements are met for all of the
common factors. For example, in the case of global money, a significant part of the total
variance (48.6%) can be attributed to the first principal component. In contrast, PC2
accounts for only 18%. Even for the inflation factor which includes four different
measures of inflation for each country or region (CPI, PPI, import prices and the GDP
deflator), the first principal component’s share is 31.8% compared to 16.9% for PC2.
With 55.9% and 74.3%, the PC1’s share for commodity and share prices is clearly the
highest.

Table 1 - Share of variance explained by first three principal components

Note: Calculations based on first standardized differences

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