**B. Implications of rising uncertainty on investment in EMDEs** . To assess the role of uncertainty for EMDE investment during 1998Q1‐2016Q2, aggregate vector autoregressive models for 18 EMDEs are applied. Given limited data availability, the sample varies for each indicator of uncertainty. Therefore, a series of separate vector autoregressive models are estimated. The results are statistically significant within the usual 16‐84 percent confidence bands.

The sample includes 18 EMDEs with available data for key quarterly macroeconomic indicators and stock market indexes: Brazil, Bulgaria, Chile, Costa Rica, Hungary, India, Indonesia, Malaysia, Mexico, Paraguay, Peru, Philippines, Poland, Romania, Russia, South Africa, Thailand, and Turkey. The literature on uncertainty often uses the option‐induced volatility measure VXO (e.g., Bloom 2009) or rich monthly macro‐data (279 macro and financial series in the case of Jurado, Ludvigson, and Ng 2015) to construct uncertainty measures. However, for many EMDEs, such measures cannot be constructed.

There are two sources of uncertainty: domestic and global. Global uncertainty is captured by financial market and policy uncertainty in the United States and the European Union (EU). Financial market uncertainty is proxied by the VIX for the United States and by the standard deviation of daily stock price changes for the Euro Area. Policy uncertainty is captured by the Economy Policy Uncertainty Index for the United States and the EU. Domestic financial market uncertainty is proxied by the standard deviation of daily stock market changes; domestic policy uncertainty is proxied by the ICRG index of political risk or, for Brazil, the Economic Policy Uncertainty Index.

_Global uncertainty_ . Vector autoregressions are used to estimate the impact of global uncertainty on EMDE investment. The data consist of investment‐weighted averages for 18 EMDEs for 1998Q1‐2016Q2. Endogenous variables follow this Cholesky ordering: global financial market or policy uncertainty, EMDE stock price index; EMDE bond price index, and aggregate real output and investment in EMDEs. Exogenous regressors, included with two lags, are: G7 real GDP growth, world stock price index, and U.S. 10‐year bond yields. For the estimation of the impact of EU uncertainty (as measured by the EPU), the sample includes EMDEs in Europe and Central Asia (Bulgaria, Hungary, Poland, Romania, Russia, Turkey).<sup>24</sup> The results are statistically significant within the usual 16‐84 percent confidence bands.

_Domestic uncertainty_ . Country‐specific vector autoregressions are used to estimate the impact of domestic uncertainty on EMDE investment growth. The sample includes data for the same 18 EMDEs as listed above for 1998Q1‐2016Q2. The variables include, in this Cholesky ordering: global financial market uncertainty, domestic financial market or political uncertainty, domestic stock prices, short‐term interest rates, and domestic real investment. G7 real GDP growth is included as an exogenous regressor to preserve degrees of freedom. The regression is estimated with two lags. The model is adapted from the Bloom (2009) U.S. model, with these changes: employment is dropped due to data constraints, global uncertainty measures are added, and quarterly data replaces monthly data. For the full sample of emerging market and developing economies, on average, the impact of domestic uncertainty—whether financial or political in nature—is insignificant throughout the forecast horizon. These results are not reported in the text. Data for the International Country Risk Guide variables are quite smooth; a short‐ term quarterly vector autoregression model therefore struggles to identify any significant correlations. Economic Policy Uncertainty data show more variance for the Brazilian economy; in these cases, the

> 24 A similar estimation for other EMDEs yielded insignificant results, likely reflecting weaker trade and financial links with the EU.

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