Measuring Global Macroeconomic Uncertainty and CrossCountry Uncertainty Spillovers
Abstract
:1. Introduction
2. The Econometric Framework
2.1. The GVAR Model
2.2. TimeVarying 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 ${T}_{0}$, then the sample is extended by onequarter increments up to $[1,{T}_{max}]$, where ${T}_{max}$ identifies the last observation in the dataset. To estimate the countryspecific VECX* models on each window, windowspecific 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 ${T}_{w}$, the maximumlikelihood estimate of the GVAR obtained using actual data is expressed as:$${\mathbf{x}}_{t}={\widehat{\mathbf{c}}}_{\mathbf{0}}^{\left(w\right)}+{\widehat{\mathbf{c}}}_{\mathbf{1}}^{\left(w\right)}t+{\widehat{\mathbf{F}}}_{1}^{\left(w\right)}{\mathbf{x}}_{t1}+{\widehat{\mathbf{F}}}_{2}^{\left(w\right)}{\mathbf{x}}_{t2}+{\widehat{\mathit{\epsilon}}}_{t}^{\left(w\right)}$$
 (ii)
 In each window, we perform a nonparametric 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 maximumlikelihood GVAR (10) and the empirical distribution of residuals. Then, we reestimate the model on the simulated time series.More specifically, in window w:
 (a)
 The windowspecific maximumlikelihood GVAR estimate (10) produces a $k\times {T}_{w}$ matrix of global residuals ${\widehat{\mathcal{E}}}^{\left(w\right)}=\left(\right)open="("\; close=")">{\widehat{\mathit{\epsilon}}}_{1}^{\left(w\right)},{\widehat{\mathit{\epsilon}}}_{2}^{\left(w\right)},\cdots ,{\widehat{\mathit{\epsilon}}}_{{T}_{w}}^{\left(w\right)}$.
 (b)
 In the generic bth bootstrap iteration, with $b=1,\cdots ,B$, the ${T}_{w}$ columns of matrix ${\widehat{\mathcal{E}}}^{\left(w\right)}$ 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 $(w,b)$, let ${\mathit{\epsilon}}_{t}^{(w,b)}$ be the bootstrap shocks, generated by randomly drawing columns from ${\widehat{\mathcal{E}}}^{\left(w\right)}$ (thereby preserving the crosssectional covariances) with replacement. The simulated time series are given by:$${\mathbf{x}}_{t}^{(w,b)}={\widehat{\mathbf{c}}}_{\mathbf{0}}^{\left(w\right)}+{\widehat{\mathbf{c}}}_{\mathbf{1}}^{\left(w\right)}t+{\widehat{\mathbf{F}}}_{1}^{\left(w\right)}{\mathbf{x}}_{t1}^{(w,b)}+{\widehat{\mathbf{F}}}_{2}^{\left(w\right)}{\mathbf{x}}_{t2}^{(w,b)}+{\mathit{\epsilon}}_{t}^{(w,b)}$$Iterationspecific foreign variables ${\mathbf{x}}_{it}^{*(w,b)}$ are then constructed using the windowspecific trade weight matrix ${\mathbf{W}}_{i}^{\left(w\right)}$ for every i.
 (c)
 In each bootstrap iteration, all the VECX* models are reestimated on the simulated data. Following Dées et al. (2007a, 2007b), the estimated model is:$$\begin{array}{c}\hfill \begin{array}{cc}\hfill \Delta {\mathbf{x}}_{it}^{(w,b)}& ={\widehat{\overline{\mathbf{a}}}}_{\mathbf{0}i}^{(w,b)}{\widehat{\mathbf{\alpha}}}_{i}^{(w,b)}{\widehat{EC}}_{i,t1}^{(w,b)}{\widehat{\mathsf{\Phi}}}_{2i}^{(w,b)}\Delta {\mathbf{x}}_{i,t1}^{(w,b)}+{\widehat{\mathbf{\Lambda}}}_{0i}^{(w,b)}\Delta {\mathbf{x}}_{it}^{*(w,b)}+\hfill \\ & \phantom{\rule{1.em}{0ex}}{\widehat{\mathbf{\Lambda}}}_{2i}^{(w,b)}\Delta {\mathbf{x}}_{i,t1}^{*(w,b)}+{\widehat{\mathit{\nu}}}_{it}^{(w,b)}\hfill \end{array}\end{array}$$
As a result, we obtain B different estimates of the GVAR model for each quarter from ${T}_{0}$ to ${T}_{max}$, denoted as:$${\mathbf{x}}_{t}^{(w,b)}={\widehat{\mathbf{c}}}_{\mathbf{0}}^{(w,b)}+{\widehat{\mathbf{c}}}_{\mathbf{1}}^{(w,b)}t+{\widehat{\mathbf{F}}}_{1}^{(w,b)}{\mathbf{x}}_{t1}^{(w,b)}+{\widehat{\mathbf{F}}}_{2}^{(w,b)}{\mathbf{x}}_{t2}^{(w,b)}+{\widehat{\mathit{\epsilon}}}_{t}^{(w,b)}$$  (iii)
 Each of the B windowspecific GVAR estimates is used to produce pseudooutofsample 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 ${\mathbf{x}}_{{T}_{w}+h}^{\left(f\right)\left(b\right)}$ denote the hstepahead 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  

$\mathit{\beta}$  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)  
$\mathit{\alpha}$  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  

$\mathit{\beta}$  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)  
$\mathit{\alpha}$  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
Notes
1  Moreover, as mentioned by Bhattarai et al. (2020), this kind of twostep estimation procedure is generally subject to the socalled 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 countryspecific 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 ${\widehat{r}}_{i}^{\left(w\right)}$ 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 ${\widehat{\tilde{\mathbf{F}}}}_{w,b}$. 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 iterationspecific 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 firstorder Taylor series approximation of the variance. To get an intuition, consider that the variance of a nonlinear function $g(X,Y)$ of two random variables X and Y can be approximated as:
$$\begin{array}{c}\hfill Var\left(\right)open="("\; close=")">g(X,Y)\\ \approx {\left(\right)}^{\frac{\partial g({\mu}_{X},{\mu}_{Y})}{\partial {\mu}_{X}}}2Var\left(X\right)+{\left(\right)}^{\frac{\partial g({\mu}_{X},{\mu}_{Y})}{\partial {\mu}_{Y}}}2\hfill & Var\left(Y\right)\end{array}$$

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 nonorthogonalized 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 CesaBianchi et al. (2014), real GDP, exchange rates and equity indices are transformed to logs, while each interest rate is transformed to $0.25\left(\right)open="["\; close="]">1+ln({R}_{t}/100)$, where ${R}_{t}$ 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 nonstationary variables used in the GVAR by first differencing. Once uncertainty is calculated for each variable, we first average variablespecific uncertainties within each country (with equal weights), then calculate global uncertainty as the PPP GDPweighted 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 “uncertaintyimporting” 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 preCOVID19 sample 1979Q42019Q4 (2 cointegration relationships for both), instead of the ranks estimated over the full sample 1979Q42020Q4 (1 and 3 cointegration relationships, respectively): this adjustment provides more reasonable longrun properties, while having almost no effect on our uncertainty measure (in particular, a rank of 1 for GBR would result in unrealistically large longrun coefficients linking GDP and inflation, while a rank of 3 for LAM would imply that all variables, including GDP, are trendstationary). 
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 countryspecific uncertainty shocks generate significant crossborder 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 CrossCountry Uncertainty Spillovers. Econometrics 2023, 11, 2. https://doi.org/10.3390/econometrics11010002
Moramarco G. Measuring Global Macroeconomic Uncertainty and CrossCountry Uncertainty Spillovers. Econometrics. 2023; 11(1):2. https://doi.org/10.3390/econometrics11010002
Chicago/Turabian StyleMoramarco, Graziano. 2023. "Measuring Global Macroeconomic Uncertainty and CrossCountry Uncertainty Spillovers" Econometrics 11, no. 1: 2. https://doi.org/10.3390/econometrics11010002