by this problem, this strongly support the view that dierences in natural hedge matter for the composition of a country's exports.

# 4.3 Robustness

The baseline results were obtained measuring the correlation of the international prices and nominal exchange rates, as well as the volatility of the exchange rate using the growth rates of each series to remove trends. Under the assumption that the series are dierence stationary (in logs), this procedure correctly removes trends, but it may still lead to spurious correlations if the growth rates are trending, which could happen over short periods of time. An alternative procedure would be to de-trend the series by ltering their long run trends using a Hodrick-Prescott lter (HP lter). This more complex detrending procedure allows for changes in the low frequency trends followed by the series.<sup>10</sup> To check for the importance of the choice of detrending procedure I also estimated the regressions for the global share specication (Eq. [7]) using standard deviations and correlations computed from HP ltered series.<sup>11</sup> The results, reported in Table 6, are somewhat smaller but very much in line with those obtained in the baseline regressions, both in sign and magnitude.

A potential concern with the results is that extreme and potentially unreliable observations for the correlation may be behind the ndings. The baseline sample already dropped country-industries with correlations larger than 0.9 in absolute value, with correlations computed using too few annual observations (less than 8), as well as countries that exported too few goods and industries exported by few countries. Still, to check the role of extreme observations, the regressions in Table 7 impose further constraints on those variables. Column (1) drops country-industries with correlations above 0.8 (in absolute value), Column (2) drops those computed with less than 12 annual observations, and Column (3) drops sectors exported by less than 20 countries and countries that export less than 20 goods. As before, the results are largely unaected.

The ndings are not crucially driven by individual countries or goods. This is shown in Figure 4 that present histograms of the values for the main coecient _β_ obtained after dropping one country and one good at a time from the sample, respectively. The gure presents ve histograms. Panels A and B show the histogram of the coecient for the regression with global export shares as dependent variable after dropping one exporting country (Panel A) and one industry (Panel B) at a time. Panels C to E show the histogram for the coecient of the bilateral export share regression obtained by sequentially dropping one exporting country (Panel C), importing country (Panel D), and industry (Panel E) at a time. Summary statistics of the distribution of coecients depicted in each histogram are reported at the bottom of each gure. It is apparent that the estimated

> 10The annual series were ltered using a smoothing parameter of 100. The monthly series were ltered with a parameter of 14400.

> 11I did not compute the HP ltered series for the bilateral specications because the length of importer based unit values are shorter and less continuous than those for global unit values, which dicults the use of the HP ltering algorithm.

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