of the instruments, panel-based Lambda-Pearson statistics (or Fischer statistics) are used.<sup>12</sup> Specifically, the Lambda-Pearson statistic is constructed as 2.0 × ∑lnPi i (where, lnPi is the natural log of the significance level associated with the F-test for significance of the rainfall instrumental variable for country i). Under the null hypothesis of significance of the rainfall instrumental variable for the panel, the Lambda-Pearson panel statistic will have a chi-square distribution with 2xN degrees of freedom, where N is the number of countries. The results show that the instrumental variables are significant at the 5 percent level. (In the case of LICs, they are significant at the 1 percent level.) To help explain the variation of domestic inflation, several potentially important country-specific characteristics were used that may affect a nation’s inflation rate. The characteristics considered are (i) the exchange rate regime by the classification of Shambaugh (2004) (where “1” is assigned to countries that have pegged or fixed exchange rates, and “0” is assigned to those with flexible exchange rates); (ii) an indicator of whether a country has an inflation targeting framework; (iii) Dincer and Eichengreen’s (2014) central bank transparency index (the higher the index is, the more transparent and independent the central bank is); (iv) gross public debt as a percentage of gross domestic product (GDP); (v) trade openness, defined as the sum of exports and imports of goods and services as a share of GDP from the World Bank’s World Development Indicators and the IMF’s World Economic Outlook; (vi) an indicator of the degree of global value chain (GVC) participation (where “1” is assigned to countries that are considered to be well-integrated into GVCs and “0” otherwise); (vii) the Chinn and Ito (2018) index of capital account openness; (viii) an indicator of whether a country is a commodity importer or exporter; and (ix) central bank turnover, using data compiled by Dreher, Sturm, and de Haan (2010).

# 5. Transmission of shocks into core price inflation

# 5.1. Impact of global shocks

Medians and interquartile ranges of the cumulative IRFs of domestic core inflation (which are equivalent to responses of the level of the log CPI) in advanced economies, non-LIC EMDEs, and LICs are shown in Figure 3, for 6 and 18 months after the shock, to illustrate the persistence of the impact. During the sample period, 1970-2016, core inflation responded very differently in LICs, compared with advanced economies and other EMDEs, to global core price shocks. A 1 percentage point increase in global core inflation increased median core inflation in LICs by close to 0.6 percentage point after 18 months, compared with less than 0.2 percentage point in advanced economies and other EMDEs. Thus, LICs appear to import more of the fluctuations in core global inflation than do the other country groups. Next, the effects on domestic core inflation of international relative price changes is considered, in the form of separate shocks to global food and energy inflation, holding global core inflation constant. Shocks to global food inflation have more notable consequences for domestic core inflation in LICs. A 1 percentage point increase in global food inflation raised median core inflation in LICs by around 0.1 percentage point (and by up to 0.3 percentage point) within six months, larger than the effects in advanced economies and other EMDEs. With respect to shocks to global energy inflation, median core inflation in LICs responded more sharply and quickly than that in advanced economies and other EMDEs, although

> 12 Constructing the F-statistic for joint significance of all the corresponding members of the panel would not be desirable, because F-statistics are well known to behave poorly as the number of implied restrictions grows large.

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