Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers
Abstract
:1. Introduction
2. The Econometric Framework
2.1. The GVAR Model
2.2. Time-Varying Uncertainty
- (i)
- The GVAR is repeatedly estimated over recursive and rolling sample windows. We first consider the recursive scheme, in which the shortest window goes from time 1 to time , then the sample is extended by one-quarter increments up to , where identifies the last observation in the dataset. To estimate the country-specific VECX* models on each window, window-specific foreign variables are constructed using trade data that were available in the final quarter of the window under consideration (Section 3 provides more details). In a generic window w ending in period , the maximum-likelihood estimate of the GVAR obtained using actual data is expressed as:
- (ii)
- In each window, we perform a non-parametric bootstrap of the estimates, following the approach by Dées et al. (2007a, 2007b) First, we simulate alternative historical paths for all the variables in the global model within the sample window, using the maximum-likelihood GVAR (10) and the empirical distribution of residuals. Then, we re-estimate the model on the simulated time series.More specifically, in window w:
- (a)
- The window-specific maximum-likelihood GVAR estimate (10) produces a matrix of global residuals .
- (b)
- In the generic b-th bootstrap iteration, with , the columns of matrix are resampled. Then, we simulate time series for all the variables using model (10) and adding the resampled residuals as shocks. Denoting iteration b in window w with the superscript , let be the bootstrap shocks, generated by randomly drawing columns from (thereby preserving the cross-sectional covariances) with replacement. The simulated time series are given by:Iteration-specific foreign variables are then constructed using the window-specific trade weight matrix for every i.
- (c)
- In each bootstrap iteration, all the VECX* models are re-estimated on the simulated data. Following Dées et al. (2007a, 2007b), the estimated model is:
As a result, we obtain B different estimates of the GVAR model for each quarter from to , denoted as: - (iii)
- Each of the B window-specific GVAR estimates is used to produce pseudo-out-of-sample forecasts for all the variables in the global economy (taking as starting values for each variable the last two actual values within the sample window). Let denote the h-step-ahead forecasts of the model estimated on window w in iteration b.
2.3. Spillovers of Uncertainty
3. The Empirical Implementation
3.1. Data
3.2. Results
3.2.1. Global Macroeconomic Uncertainty (GMU) Index
3.2.2. Global Spillovers of Uncertainty
4. Conclusions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Data Sources for 2020Q1–2020Q4
Appendix B. Cointegration Relationships in the GVAR
Coeff. | Variable | EUR | GBR | JAP | USA | ||
---|---|---|---|---|---|---|---|
rgdp | 1.00 | 1.00 | 0.00 | 1.00 | 0.00 | 1.00 | |
(0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | ||
infl | 6.37 | 0.00 | 1.00 | 0.00 | 1.00 | 16.2 | |
(5.08) | (0.00) | (0.00) | (0.00) | (0.00) | (1.56) | ||
eq | 0.41 | −0.07 | 0.02 | −0.07 | 0.00 | −0.09 | |
(0.10) | (0.06) | (0.01) | (0.03) | (0.00) | (0.03) | ||
fx | 0.84 | 0.10 | −0.01 | 0.22 | −0.00 | ||
(0.16) | (0.05) | (0.01) | (0.05) | (0.00) | |||
rshort | −47.8 | −10.5 | −0.11 | −14.1 | −0.77 | −5.23 | |
(5.82) | (1.58) | (0.22) | (3.18) | (0.13) | (1.79) | ||
rgdp * | −1.65 | −1.07 | −0.07 | −0.22 | −0.01 | −0.47 | |
(0.62) | (0.23) | (0.03) | (0.32) | (0.01) | (0.28) | ||
infl * | 8.80 | −0.91 | −0.25 | 4.689 | −0.01 | 0.91 | |
(2.01) | (1.37) | (0.19) | (2.16) | (0.09) | (0.80) | ||
eq * | −0.44 | 0.10 | −0.00 | 0.08 | −0.01 | ||
(0.12) | (0.05) | (0.01) | (0.05) | (0.00) | |||
fx * | −1.43 | −0.11 | 0.00 | −0.36 | 0.01 | 0.03 | |
(0.310) | (0.05) | (0.01) | (0.11) | (0.00) | (0.09) | ||
rshort * | 50.8 | 12.2 | 0.01 | −10.4 | −0.15 | ||
(6.52) | (2.22) | (0.31) | (3.78) | (0.16) | |||
rgdp | 0.04 | −0.01 | −0.14 | −0.09 | 0.43 | −0.03 | |
(0.00) | (0.04) | (0.19) | (0.01) | (0.29) | (0.01) | ||
infl | 0.00 | 0.01 | −0.48 | 0.03 | −1.06 | −0.04 | |
(0.00) | (0.01) | (0.07) | (0.01) | (0.12) | (0.00) | ||
eq | 0.02 | 0.11 | 0.64 | 0.03 | 0.39 | −0.15 | |
(0.04) | (0.16) | (0.81) | (0.11) | (2.26) | (0.08) | ||
fx | 0.01 | 0.05 | −1.71 | 0.06 | 2.40 | ||
(0.02) | (0.12) | (0.60) | (0.07) | (1.57) | |||
rshort | 0.00 | 0.03 | 0.07 | 0.00 | 0.06 | 0.00 | |
(0.00) | (0.01) | (0.03) | (0.00) | (0.03) | (0.00) |
Coeff. | Variable | AUS | CAN | CHE | CHN | IND | KOR | NOR | NZL | SEA | SWE | ZAF | LAM | MEX | SAU | TUR | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
rgdp | 1.00 | 1.00 | 1.00 | 1.00 | 0.00 | 1.00 | 0.00 | 1.00 | 0.00 | 1.00 | 1.00 | 0.00 | 1.00 | 0.00 | 1.00 | 0.00 | 0.00 | 1.00 | 0.00 | 1.00 | 0.00 | 1.00 | 1.00 | 0.00 | 1.00 | 0.00 | |
(0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | ||
infl | −11.5 | −7.92 | −50.1 | 0.00 | 1.00 | 0.00 | 1.00 | 0.00 | 1.00 | 4.75 | 0.00 | 1.00 | 0.00 | 1.00 | 0.00 | 1.00 | 0.00 | 0.00 | 1.00 | 0.00 | 1.00 | −6.93 | 0.00 | 1.00 | 0.00 | 1.00 | |
(1.13) | (0.77) | (6.29) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.85) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.00) | (0.94) | (0.00) | (0.00) | (0.00) | (0.00) | ||
fx | 0.35 | 0.12 | 1.10 | 2.17 | −0.043 | 0.27 | 0.00 | −2.62 | 0.12 | −0.27 | 0.21 | −0.01 | 0.356 | −0.00 | 0.00 | 0.00 | 1.00 | −1.48 | −0.10 | −0.20 | −0.05 | 1.31 | 1.85 | 0.00 | −0.04 | −0.25 | |
(0.07) | (0.03) | (0.33) | (0.30) | (0.01) | (0.03) | (0.01) | (0.82) | (0.04) | (0.10) | (0.09) | (0.01) | (0.09) | (0.01) | (0.00) | (0.00) | (0.00) | (1.00) | (0.05) | (0.05) | (0.01) | (0.24) | (0.32) | (0.00) | (0.05) | (0.04) | ||
rshort | 7.31 | 2.46 | 10.8 | −86.5 | −0.11 | −1.11 | 0.37 | −129 | 4.43 | 4.29 | −9.41 | −1.12 | −17.6 | −0.68 | 165 | 15.0 | 190 | 116 | 5.92 | ||||||||
(1.15) | (1.45) | (5.28) | (12.8) | (0.49) | (1.21) | (0.30) | (15.9) | (0.81) | (1.30) | (1.29) | (0.14) | (2.28) | (0.13) | (25.3) | (2.28) | (28.5) | (20.6) | (1.04) | |||||||||
rgdp * | −0.48 | −1.02 | 0.14 | −6.12 | 0.11 | −0.65 | −0.01 | −8.57 | 0.25 | −1.00 | 0.68 | 0.04 | −1.67 | −0.03 | 0.99 | 0.12 | 1.48 | −8.65 | −0.50 | 1.26 | 0.25 | 1.78 | −1.24 | 0.01 | 0.62 | −1.06 | |
(0.19) | (0.08) | (0.66) | (1.40) | (0.05) | (0.18) | (0.05) | (2.78) | (0.14) | (0.18) | (0.35) | (0.04) | (0.60) | (0.03) | (2.32) | (0.21) | (2.61) | (3.88) | (0.19) | (0.64) | (0.13) | (0.81) | (0.81) | (0.01) | (0.31) | (0.25) | ||
infl * | 2.67 | 0.79 | 8.26 | −0.26 | −0.022 | −0.56 | −0.08 | −3.26 | 0.13 | 5.87 | 2.47 | −0.11 | −1.30 | −0.03 | −17.9 | −2.48 | −17.3 | −35.0 | −2.15 | −0.85 | −0.41 | 3.39 | −2.42 | 0.07 | 4.65 | 1.40 | |
(1.24) | (0.94) | (6.33) | (5.08) | (0.19) | (0.87) | (0.22) | (16.2) | (0.83) | (1.35) | (2.62) | (0.28) | (4.50) | (0.26) | (28.7) | (2.59) | (32.2) | (35.7) | (1.81) | (0.76) | (0.16) | (6.13) | (3.70) | (0.06) | (2.35) | (1.86) | ||
fx * | −0.62 | −0.26 | −0.90 | −2.72 | 0.05 | −0.11 | 0.03 | −2.07 | 0.06 | 0.20 | −0.04 | 0.04 | −0.81 | 0.02 | −0.24 | −0.02 | −1.29 | −1.52 | −0.06 | 0.88 | −0.02 | −0.60 | −0.86 | 0.00 | 0.27 | 0.32 | |
(0.10) | (0.04) | (0.39) | (0.45) | (0.02) | (0.05) | (0.01) | (0.72) | (0.04) | (0.12) | (0.10) | (0.01) | (0.26) | (0.02) | (0.59) | (0.05) | (0.67) | (1.23) | (0.06) | (0.21) | (0.04) | (0.27) | (0.30) | (0.00) | (0.10) | (0.08) | ||
rshort * | −6.01 | 1.17 | 16.8 | 6.27 | 1.02 | −9.48 | −0.81 | 138 | −5.13 | −0.96 | 9.88 | 0.93 | 30.4 | 0.65 | −169 | −15.8 | −211 | −115 | −6.68 | −22.0 | −8.00 | −2.97 | 12.6 | 0.00 | −8.61 | −0.20 | |
(2.23) | (1.71) | (11.8) | (14.37) | (0.55) | (1.79) | (0.45) | (43.5) | (2.23) | (2.37) | (3.86) | (0.41) | (10.5) | (0.61) | (49.0) | (4.42) | (55.2) | (51.2) | (2.60) | (11.6) | (2.37) | (9.07) | (7.81) | (0.13) | (4.35) | (3.43) | ||
rgdp | 0.00 | −0.04 | 0.00 | 0.018 | −0.24 | −0.51 | −0.43 | −0.00 | −0.30 | −0.13 | 0.06 | −0.22 | 0.03 | −0.06 | −0.05 | −0.12 | 0.05 | 0.02 | −0.55 | −0.02 | 0.08 | 0.03 | −0.01 | 0.28 | −0.23 | −0.04 | |
(0.01) | (0.02) | (0.00) | (0.00) | (0.14) | (0.07) | (0.30) | (0.01) | (0.12) | (0.02) | (0.02) | (0.16) | (0.01) | (0.12) | (0.02) | (0.21) | (0.01) | (0.01) | (0.16) | (0.01) | (0.04) | (0.01) | (0.01) | (0.55) | (0.04) | (0.05) | ||
infl | 0.09 | 0.10 | 0.01 | 0.00 | −0.22 | −0.00 | −0.80 | −0.00 | −0.11 | −0.06 | 0.06 | −0.59 | 0.02 | −0.61 | 0.05 | −0.78 | 0.02 | 0.04 | −0.76 | −0.12 | −0.47 | 0.06 | 0.04 | −0.84 | 0.11 | −0.25 | |
(0.01) | (0.01) | (0.00) | (0.00) | (0.08) | (0.02) | (0.10) | (0.00) | (0.08) | (0.01) | (0.01) | (0.10) | (0.00) | (0.08) | (0.01) | (0.11) | (0.00) | (0.00) | (0.10) | (0.03) | (0.10) | (0.01) | (0.01) | (0.30) | (0.05) | (0.07) | ||
fx | −0.19 | 0.03 | −0.02 | −0.05 | −0.85 | −0.02 | 0.10 | 0.02 | 0.48 | 0.10 | 0.06 | −0.96 | 0.02 | −4.52 | 0.16 | −0.82 | −0.08 | 0.04 | −0.87 | 0.23 | −0.52 | −0.04 | −0.04 | 0.21 | 0.22 | 0.45 | |
(0.07) | (0.09) | (0.02) | (0.01) | (0.34) | (0.07) | (0.31) | (0.02) | (0.38) | (0.05) | (0.06) | (0.54) | (0.05) | (0.95) | (0.06) | (0.65) | (0.03) | (0.04) | (0.78) | (0.04) | (0.14) | (0.02) | (0.01) | (0.32) | (0.10) | (0.13) | ||
rshort | −0.00 | −0.00 | −0.00 | 0.00 | 0.06 | −0.01 | 0.00 | 0.01 | 0.14 | −0.01 | 0.01 | 0.12 | 0.01 | −0.02 | 0.00 | −0.01 | −0.00 | 0.00 | −0.02 | ||||||||
(0.00) | (0.00) | (0.00) | (0.0) | (0.01) | (0.00) | (0.02) | (0.00) | (0.02) | (0.01) | (0.00) | (0.03) | (0.00) | (0.05) | (0.00) | (0.05) | (0.00) | (0.00) | (0.03) |
Appendix C. SVAR Impulse Response Functions
1 | Moreover, as mentioned by Bhattarai et al. (2020), this kind of two-step estimation procedure is generally subject to the so-called generated regressor problem (Pagan 1984). |
2 | The estimates of uncertainty may be inflated by explosive roots in (13). For this reason, in each iteration we check whether the estimated models are dynamically stable, i.e., whether all the eigenvalues of the companion matrices are less than or equal to 1 in modulus. The stability check is performed both on the country-specific models and on the resulting global model. Unstable models are discarded, so that uncertainty is measured using stable models only. At the country level, each cointegration rank is determined by the Johansen trace test (at the 5% significance level), unless the resulting VARX* is unstable. In this case, we select the highest rank that makes the model stable. Since this does not ensure the stability of the global model, we also check the eigenvalues of the global companion matrix . If the global model is unstable, the bootstrap iteration is repeated until stability is achieved. To further mitigate the impact of extreme forecasts on the uncertainty measures, we also remove iteration-specific forecasts that are outliers with respect to U.S. GDP, chosen as a representative variable. In particular, global forecasts are discarded whenever the forecasts of U.S. GDP lie more than 3 standard deviations away from their average across iterations. |
3 | The forecast variance decomposition used to calculate uncertainty spillovers relies on a first-order Taylor series approximation of the variance. To get an intuition, consider that the variance of a nonlinear function of two random variables X and Y can be approximated as:
|
4 | Like Klößner and Sekkel (2014) and Rossi and Sekhposyan (2017), we do not give a causal interpretation to the uncertainty spillovers, as they are not based on structural (orthogonal) shocks. In this respect, we also follow the GVAR literature, which typically uses non-orthogonalized shocks to conduct impulse response analysis, in the form of generalized impulse response functions (GIRFs) and generalized forecast error variance decomposition (GFEVD) (see Pesaran et al. 2004 and Dées et al. 2007a, 2007b). |
5 | As in Cesa-Bianchi et al. (2014), real GDP, exchange rates and equity indices are transformed to logs, while each interest rate is transformed to , where is the rate expressed in percentage values on an annual basis. |
6 | All results are obtained using 1000 bootstrap iterations. |
7 | The two schemes provide highly correlated results for global uncertainty (the correlation coefficient is 0.75). In the quarters 2020Q2–2020Q4, the GVAR estimated by rolling windows on actual data is explosive for any choice of the cointegration ranks. In this case, to generate the bootstrap samples, we use the model estimates obtained on the window ending in 2019Q4. |
8 | To apply the methodology by Jurado et al. (2015), we transform the non-stationary variables used in the GVAR by first differencing. Once uncertainty is calculated for each variable, we first average variable-specific uncertainties within each country (with equal weights), then calculate global uncertainty as the PPP GDP-weighted average of uncertainty across countries. |
9 | The estimated spillovers from all countries to a given country do not exactly sum to 1, because the forecast variance decomposition is based on a linear approximation of a nonlinear function, as explained in endnote 3. However, the discrepancy is in general very small. The sum of the estimated spillovers is 0.98 on average and ranges between 0.93 and 1.04 across countries. In the results reported here, all spillovers are rescaled so that they exactly sum to 1 for each “uncertainty-importing” country. |
10 | We use the fast algorithm developed by Klößner and Wagner (2014). |
11 | Cointegration ranks in Table A1 and Table A2 are determined using the Johansen (1995) trace test at the 5% level of significance. Estimates are reported for all countries except Brazil, for which the Johansen (1995) test indicates no cointegration relationships. For the U.K. (GBR) and the Latin American aggregate area (LAM), Table A1 and Table A2 report results using the cointegration ranks determined over the pre-COVID-19 sample 1979Q4-2019Q4 (2 cointegration relationships for both), instead of the ranks estimated over the full sample 1979Q4-2020Q4 (1 and 3 cointegration relationships, respectively): this adjustment provides more reasonable long-run properties, while having almost no effect on our uncertainty measure (in particular, a rank of 1 for GBR would result in unrealistically large long-run coefficients linking GDP and inflation, while a rank of 3 for LAM would imply that all variables, including GDP, are trend-stationary). |
12 | Both types of uncertainty measures are highly correlated across countries, implying that the IRFs strongly depend on the specific Cholesky ordering. Still, the analysis is mainly intended to check if country-specific uncertainty shocks generate significant cross-border dynamic spillovers. |
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VIX | JLN | WUI | GEPU | OS | Scotti | MR | GMU | |
---|---|---|---|---|---|---|---|---|
VIX | 1.00 | |||||||
JLN | 0.62 | 1.00 | ||||||
WUI | 0.01 | −0.02 | 1.00 | |||||
GEPU | 0.19 | 0.33 | 0.67 | 1.00 | ||||
OS | 0.67 | 0.89 | 0.14 | 0.35 | 1.00 | |||
Scotti | 0.34 | 0.61 | 0.05 | 0.49 | 0.56 | 1.00 | ||
MR | 0.83 | 0.65 | 0.01 | 0.32 | 0.75 | 0.22 | 1.00 | |
GMU | 0.64 | 0.74 | 0.06 | 0.39 | 0.75 | 0.87 | 0.62 | 1.00 |
AUS | BRA | CAN | CHE | CHN | EUR | GBR | IND | JAP | KOR | LAM | MEX | NOR | NZL | SAU | SEA | SWE | TUR | USA | ZAF | From | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AUS | 66.9 | 2.6 | 0.1 | −0.1 | 14.2 | 1.8 | 0.5 | 0.8 | 3.1 | 1.3 | 1.0 | 0.5 | 0.2 | 0.6 | 0.8 | 3.6 | −0.4 | 0.1 | 2.4 | 0.3 | 33.1 |
BRA | 0.4 | 80.8 | −0.2 | −0.3 | 5.7 | 3.0 | 0.1 | 0.4 | 1.9 | 0.8 | 2.4 | 1.0 | −0.1 | 0.1 | 0.1 | 1.0 | −0.5 | 0.2 | 3.0 | 0.1 | 19.2 |
CAN | 1.5 | 6.0 | 25.8 | 0.2 | 14.9 | 7.1 | 2.1 | 1.5 | 4.1 | 1.8 | 0.8 | 4.8 | 0.1 | 0.3 | 1.4 | 3.2 | 0.0 | 0.2 | 23.7 | 0.5 | 74.2 |
CHE | 0.9 | 12.2 | 0.1 | 41.7 | 10.7 | 13.6 | 3.6 | 0.6 | 3.3 | 0.3 | 0.9 | 1.8 | 0.0 | 0.2 | 0.8 | 1.0 | −0.5 | 0.3 | 8.2 | 0.3 | 58.3 |
CHN | 0.0 | 6.7 | 0.0 | −0.1 | 84.7 | 1.4 | 0.8 | 0.0 | 2.0 | 0.3 | 0.3 | 0.1 | 0.0 | 0.0 | 0.4 | 2.8 | −0.3 | 0.0 | 0.8 | −0.1 | 15.3 |
EUR | 0.9 | 9.7 | 0.1 | 0.2 | 11.8 | 46.8 | 3.7 | 0.9 | 3.1 | 1.3 | 1.1 | 2.2 | 0.1 | 0.2 | 1.0 | 2.2 | −0.5 | 0.6 | 14.8 | 0.0 | 53.2 |
GBR | 0.7 | 6.0 | 0.0 | 0.2 | 10.3 | 14.8 | 41.1 | 1.0 | 2.5 | 1.3 | 1.0 | 2.0 | 0.0 | 0.2 | 1.2 | 2.1 | −0.2 | 0.5 | 15.3 | −0.1 | 58.9 |
IND | 1.4 | 5.2 | 0.3 | 0.5 | 10.7 | 4.9 | 2.5 | 58.1 | 3.5 | 1.5 | 1.3 | 1.4 | 0.0 | 0.1 | 2.3 | 0.0 | −0.2 | 0.7 | 5.5 | 0.3 | 41.9 |
JAP | 1.2 | 2.3 | 0.3 | −0.2 | 8.1 | 4.3 | 1.4 | 0.5 | 69.0 | 1.1 | 0.6 | 1.3 | -0.1 | 0.1 | 0.4 | 3.1 | −0.2 | 0.0 | 6.8 | 0.1 | 31.0 |
KOR | 0.7 | 2.4 | 0.3 | 0.5 | 8.3 | 2.8 | 1.7 | 1.1 | 3.0 | 66.3 | 1.3 | 1.5 | 0.0 | 0.1 | 0.9 | 6.1 | 0.2 | 0.6 | 2.3 | 0.0 | 33.7 |
LAM | −0.1 | 18.9 | −0.1 | −0.4 | 4.4 | 1.4 | −0.3 | 0.6 | 0.1 | −0.2 | 73.0 | 0.1 | 0.0 | 0.2 | 0.2 | −0.4 | −0.2 | −0.5 | 3.0 | 0.1 | 27.0 |
MEX | 0.7 | 5.7 | 0.2 | 0.4 | 4.7 | 1.1 | 1.1 | 0.9 | 0.7 | 1.2 | 0.4 | 75.0 | 0.1 | 0.1 | 0.6 | −0.8 | 0.1 | −0.1 | 7.9 | −0.1 | 25.0 |
NOR | 0.5 | 3.5 | 0.0 | 0.5 | 6.6 | 19.3 | 2.9 | 0.6 | 2.4 | 0.1 | 0.9 | 0.7 | 56.2 | 0.1 | 0.1 | 1.7 | 0.4 | 0.4 | 3.1 | 0.1 | 43.8 |
NZL | 4.2 | 2.9 | 0.5 | 0.0 | 10.2 | 3.0 | 1.1 | 0.5 | 2.3 | 0.8 | 1.2 | 1.3 | 0.2 | 62.7 | 0.4 | 6.7 | −0.1 | 0.2 | 1.6 | 0.2 | 37.3 |
SAU | 0.3 | 3.5 | 0.6 | 0.5 | 10.0 | 3.6 | 1.4 | 1.1 | 5.1 | 1.0 | 0.8 | 0.4 | 0.1 | 0.0 | 69.8 | −2.0 | −0.2 | 1.1 | 2.8 | 0.1 | 30.2 |
SEA | 0.1 | 1.9 | 1.1 | −0.3 | 4.9 | 2.2 | 1.2 | 0.2 | 2.8 | 3.1 | 1.4 | 0.9 | 0.1 | 0.1 | 0.3 | 77.9 | −0.1 | 0.7 | 1.4 | 0.0 | 22.1 |
SWE | 0.9 | 13.0 | 0.4 | 0.3 | 11.9 | 19.6 | 4.6 | 0.9 | 3.9 | 1.0 | 1.3 | 2.6 | 0.6 | 0.3 | 1.2 | 2.5 | 27.6 | 0.6 | 6.7 | 0.2 | 72.4 |
TUR | 0.2 | 4.2 | −0.1 | 0.6 | 8.1 | 17.1 | 2.2 | 1.5 | 2.9 | 0.6 | 0.3 | 1.2 | −0.1 | 0.1 | 1.4 | 0.2 | 0.0 | 56.1 | 3.1 | 0.5 | 43.9 |
USA | 1.4 | 2.1 | 0.9 | 0.1 | 8.9 | 4.2 | 1.7 | 1.3 | 1.5 | 1.9 | 0.9 | 6.2 | 0.1 | 0.3 | 0.9 | 3.0 | 0.0 | 0.2 | 64.3 | 0.1 | 35.7 |
ZAF | 0.6 | 2.7 | 0.1 | 0.2 | 8.6 | 1.9 | 1.3 | 0.6 | 4.2 | 0.1 | 1.0 | 0.6 | 0.0 | 0.1 | 0.8 | 4.9 | −0.1 | 0.0 | 2.3 | 70.0 | 30.0 |
To | 0.7 | 5.4 | 0.3 | 0.1 | 9.1 | 4.0 | 1.7 | 0.7 | 2.4 | 1.2 | 0.9 | 2.1 | 0.0 | 0.1 | 0.8 | 2.2 | −0.2 | 0.3 | 5.7 | 0.1 | 39.3 |
AUS | BRA | CAN | CHE | CHN | EUR | GBR | IND | JAP | KOR | LAM | MEX | NOR | NZL | SEA | SWE | USA | From | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AUS | 16.5 | 6.6 | 5.2 | 10.3 | 2.8 | 15.7 | 4.6 | 2.6 | 1.4 | 4.4 | 1.2 | 2.0 | 3.2 | 7.2 | 1.9 | 3.5 | 11.1 | 83.5 |
BRA | 7.3 | 22.4 | 3.5 | 4.5 | 8.3 | 12.8 | 5.3 | 5.9 | 1.8 | 4.0 | 2.8 | 2.6 | 0.9 | 2.8 | 3.5 | 2.5 | 9.0 | 77.6 |
CAN | 14.3 | 4.3 | 9.8 | 5.4 | 7.1 | 18.2 | 5.4 | 3.6 | 0.9 | 2.6 | 3.1 | 3.5 | 1.6 | 3.0 | 4.0 | 1.7 | 11.5 | 90.2 |
CHE | 18.7 | 3.6 | 6.2 | 19.8 | 3.5 | 13.6 | 2.7 | 5.6 | 1.3 | 1.0 | 1.3 | 1.1 | 3.9 | 3.7 | 2.5 | 1.5 | 9.9 | 80.2 |
CHN | 15.1 | 5.7 | 3.4 | 16.6 | 17.5 | 10.3 | 3.0 | 2.1 | 1.7 | 3.3 | 1.4 | 2.3 | 2.3 | 2.4 | 2.9 | 2.6 | 7.7 | 82.5 |
EUR | 14.4 | 4.9 | 4.7 | 6.8 | 3.0 | 33.4 | 2.7 | 2.8 | 1.0 | 3.0 | 2.1 | 0.9 | 3.0 | 3.6 | 1.6 | 4.9 | 7.3 | 66.6 |
GBR | 9.6 | 3.6 | 7.7 | 5.4 | 4.0 | 21.6 | 14.4 | 2.2 | 1.6 | 1.7 | 1.1 | 1.2 | 1.3 | 6.5 | 3.0 | 2.1 | 13.0 | 85.6 |
IND | 7.5 | 8.6 | 2.7 | 11.2 | 4.0 | 6.5 | 2.0 | 37.5 | 3.8 | 2.9 | 0.6 | 0.7 | 1.8 | 1.9 | 2.9 | 2.6 | 2.9 | 62.5 |
JAP | 5.1 | 6.5 | 2.9 | 9.3 | 2.3 | 10.8 | 2.3 | 5.3 | 18.4 | 3.9 | 0.9 | 1.8 | 3.3 | 3.2 | 2.5 | 2.1 | 19.5 | 81.6 |
KOR | 9.4 | 3.9 | 2.6 | 15.0 | 3.5 | 8.1 | 2.4 | 3.1 | 6.0 | 14.0 | 0.6 | 2.2 | 2.5 | 6.8 | 2.2 | 5.2 | 12.3 | 85.9 |
LAM | 10.5 | 10.9 | 1.3 | 1.5 | 1.4 | 9.8 | 2.0 | 1.3 | 1.0 | 1.2 | 32.2 | 9.2 | 4.5 | 0.9 | 1.4 | 1.3 | 9.6 | 67.8 |
MEX | 8.4 | 4.1 | 3.3 | 5.2 | 9.5 | 19.8 | 3.3 | 4.8 | 2.6 | 3.1 | 5.8 | 13.4 | 1.3 | 2.2 | 2.9 | 2.0 | 8.3 | 86.6 |
NOR | 5.7 | 4.3 | 1.7 | 16.0 | 1.4 | 7.6 | 4.5 | 7.4 | 4.5 | 1.3 | 1.4 | 1.4 | 28.2 | 3.6 | 1.0 | 7.7 | 2.4 | 71.8 |
NZL | 8.7 | 2.1 | 7.6 | 8.8 | 2.6 | 11.1 | 5.0 | 6.0 | 5.0 | 4.6 | 0.7 | 1.6 | 2.0 | 13.9 | 2.6 | 2.4 | 15.1 | 86.1 |
SEA | 11.1 | 6.0 | 7.1 | 8.9 | 6.0 | 18.2 | 3.1 | 3.7 | 1.4 | 5.0 | 1.8 | 2.5 | 1.8 | 3.0 | 10.2 | 2.3 | 7.8 | 89.8 |
SWE | 7.2 | 1.4 | 2.9 | 18.2 | 0.9 | 14.5 | 2.1 | 7.3 | 5.2 | 1.4 | 1.2 | 3.0 | 5.6 | 8.5 | 3.1 | 13.9 | 3.6 | 86.1 |
USA | 18.3 | 3.9 | 4.1 | 5.6 | 1.1 | 16.1 | 3.0 | 3.5 | 3.6 | 1.1 | 1.0 | 2.0 | 1.3 | 4.4 | 5.4 | 2.0 | 23.8 | 76.2 |
To | 13.1 | 5.5 | 4.0 | 9.8 | 3.4 | 12.9 | 2.9 | 3.2 | 2.3 | 2.8 | 1.5 | 2.0 | 2.1 | 3.3 | 3.2 | 2.7 | 8.4 | 80.0 |
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Moramarco, G. Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers. Econometrics 2023, 11, 2. https://doi.org/10.3390/econometrics11010002
Moramarco G. Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers. Econometrics. 2023; 11(1):2. https://doi.org/10.3390/econometrics11010002
Chicago/Turabian StyleMoramarco, Graziano. 2023. "Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers" Econometrics 11, no. 1: 2. https://doi.org/10.3390/econometrics11010002
APA StyleMoramarco, G. (2023). Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers. Econometrics, 11(1), 2. https://doi.org/10.3390/econometrics11010002