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Mr. Selim A Elekdag and Maxwell Tuuli
This paper assesses the stabilization properties of fixed versus flexible exchange rate regimes and aims to answer this research question: Does greater exchange rate flexibility help an economy’s adjustment to weather shocks? To address this question, the impact of weather shocks on real per capita GDP growth is quantified under the two alternative exchange rate regimes. We find that although weather shocks are generally detrimental to per capita income growth, the impact is less severe under flexible exchange rate regimes. Moreover, the medium-term adverse growth impact of a 1 degree Celsius increase in temperature under a pegged regime is about –1.4 percentage points on average, while under a flexible regime, the impact is less than one half that amount (–0.6 percentage point). This finding bolsters the idea that exchange rate flexibility not only helps mitigate the initial impact of the shock but also promotes a faster recovery. In terms of mechanisms, our findings suggest that the depreciation of the nominal exchange rate under a flexible regime supports real export growth. In contrast to standard theoretical predictions, we find that countercyclical fiscal policy may not be effective under pegged regimes amid high debt, highlighting the importance of the policy mix and precautionary (fiscal) buffers.
Pelin Berkmen and Eduardo A. Cavallo
The paper identifies the contemporaneous relationship between exchange rate policy and liability dollarization using three different definitions of dollarization. The presence of endogeneity makes the empirical identification elusive. We use identification through heteroskedasticity to solve the endogeneity problem in the present context (Rigobon, 2003). While we find that countries with high liability dollarization (external, public, or financial) tend to be more actively involved in exchange rate stabilization operations, we do not find evidence that floating, by itself, promotes de-dollarization.
Pelin Berkmen and Eduardo A. Cavallo

certain periods, which allows us to achieve identification. Figure 3. Variances and Correlation of Endogenous Variables For the baseline scenario, we will take three years as a regime. However, in the robustness section we will change the regime windows to make sure that does not affect our point estimates. Table 3 presents the relative variance of the structural shocks for each regime. As can be seen from the table relative variances are different across regimes, which is exactly what we need to achieve identification. Table 3: Relative Variances across

Mr. Tamim Bayoumi and Mr. Trung T Bui

is still identified and its estimators are consistent even if heteroskedasticity is misspecified. In other words, this method is robust to either misspecification of the regime windows or under-specification of the number of regimes. Below we exploit this characteristic to calculate a series of consistent estimates of A , which can then be bootstrapped to calculate standard errors on the estimated coefficients in the A matrix. Identification through heteroskedasticity requires users to distinguish “low” versus “high” regimes within the data sample. In Rigobon

Gustavo Adler, Kyun Suk Chang, and Zijiao Wang

symmetry across groups and regimes Window: L = 8 quarters L = 12 quarters Smaple: All AEs EMDEs EMDEs All AEs EMDEs EMDEs (1) (2) (3) (4) (5) (6) (7) (8) α G 0.329*** (0.0182) 0.0336 (0.0844) 0.357*** (0.0180) 0.624*** (0.0249) 0.343*** (0.0173) 0.0007 (0.0820) 0.374*** (0.0169) 0.649*** (0.0218) β G -0.102*** (0.0223) 0.0779 (0.0874) -0.0769*** (0.0239) -0.0937*** (0.0230) -0.0958*** (0.0208) 0.144* (0.0845) -0.0817*** (0.0217) -0.0996*** (0.0205) Res

International Monetary Fund

frequency and with shorter time spans than financial data. As a result, it may not be feasible to exactly identify multiple variability regimes in the sample. Moreover, it is often difficult to identify the exact lengths of specific variability regimes in the data sample. Fortunately, however, Rigobon (2003) shows that the contemporaneous correlation matrix is still identified and its estimators are consistent even if the heteroskedasticity is misspecified. In other words, his method is robust to either misspecification of the regime windows or under-specification of

International Monetary Fund
The 2008 crisis underscored the interconnectedness of the international business cycle, with U.S. shocks leading to the largest global slowdown since the 1930s. We estimate spillover effects across major advanced country regions in a structural VAR (SVAR) using pre-crisis data. Our new method freely estimates the contemporaneous correlation matrix for underlying shocks in the VAR and (uniquely, to our knowledge) the associated uncertainty. Our results suggest that the international business cycle is largely driven by U.S. financial shocks with a significant impact from global shocks, mainly reflecting commodity prices. Other advanced economic regions play a much smaller and regional role in growth spillovers. Our findings are consistent with the emerging evidence on the current crisis
Mr. Tamim Bayoumi and Mr. Trung T Bui
This paper uses a novel variant of identification through hetroscedacity to estimate spillovers across U.S., Euro area, Japanese, and UK government bond and equity markets in a vector autoregression. The results suggest that U.S. financial shocks reverberate around the world much more strongly than shocks from other regions, including the Euro area, while inward spillovers to the U.S. from elsewhere are minimal. There is also evidence of two-way spillovers between the UK and Euro area financial markets and spillovers from Europe to Japan. The results also suggest that the uncertainty about the direction of causality of contemporaneous correlations—an issue that other techniques cannot tackle—is the dominant source of uncertainty in the estimated impulse response functions.
Mr. Selim A Elekdag and Maxwell Tuuli

three-year window beginning in the year before the shock. As shown in Figure 7 the results are similar to those under the baseline specification (and also robust to a longer constant regime window, see Table A6 in the appendix). Figure 7: Robustness: Constant Regimes: Temperature Shocks and Growth (percent) Source: Authors’ calculations . Notes The figure shows the impact of 1 o C increase in temperature on per capita income growth across the different exchange rate regime s and is obtained by estimating equation (1) separately for each horizon (h