The experience of monetary policy making in an uncertain environment has encouraged increased attention to the concept of model uncertainty, that is, uncertainty as to which is the best model. Econ Lett 171:63â71, Gabauer D, Gupta R (2020) Spillovers across macroeconomic, financial and real estate uncertainties: a time-varying approach. Central bankers do not know what determines inflation, though few will admit it. The within connectedness (WTH in the BK tables) shows the spillovers within the frequency band and the frequency connectedness (ABS in the BK tables) splits the DY connectedness measure into the different frequency bands. As our sample of countries is small due to data availability, it would be interesting to see how the results change when a greater number of developing countries were included. Miranda-Agrippino et al. \(\widetilde{\vartheta }_{ij} (H)\) provides a measurement of pairwise spillovers from variables j to i at horizon H. This can be aggregated to calculate the total spillover index \(C\left( H\right) \), which is defined as the share of variance in the forecasts contributed by errors other than its own, i.e., shocks to \(Y_j\), for \(i, j=1,2,\ldots , N,\ {\text{and}}\ i\ne j\). only looked at EPU, while Gabauer and Gupta (2018) looked at monetary, fiscal, currency and trade uncertainty. Their findings support the arguments made by Rey (2015), as the large amounts of dollar debt in other countries, and the close link between monetary policy and exchange rate, influence their monetary policy discretion. First, end the health crisis. (2020) found financial uncertainty transmits the shocks that drive economic and real estate uncertainty. That is why we need continued strong policy action to combat continued uncertainty. $$\begin{aligned} Y_t= \sum _{i=1}^p \Psi _i Y_{t-i}+\epsilon _t \end{aligned}$$, $$\begin{aligned} Y_t=\sum _{i=0}^{\infty }A_i\varepsilon _{t-i} \end{aligned}$$, \(A_i=\Psi _1A_{i-1}+\Psi _2A_{i-2}+...\Psi _pA_{i-p}\), $$\begin{aligned} \vartheta _{ij}\left( H\right) =\frac{\sigma _{jj}^{-1}\sum _{h=0}^{H-1} \left( e_i^\prime A_h {\Omega }e_j\right) ^2}{\sum _{h=0}^{H-1} \left( \ e_i^\prime A_h {\Omega }{{A^\prime }_he}_i\right) } \end{aligned}$$, $$\begin{aligned} {{\widetilde{\vartheta }}}_{ij} \left( H\right) =\frac{\vartheta _{ij} \left( H\right) }{\sum _{j=1}^{N}{\vartheta _{ij} \left( H\right) }} \end{aligned}$$, \(\sum _{j=1}^{N}{{{\widetilde{\vartheta }}}_{ij} (H)}=1\), \(\sum _{i,j=1}^{N}{{\widetilde{\vartheta }}_{ij} (H)}=N\), \(i, j=1,2,\ldots , N,\ {\text{and}}\ i\ne j\), $$\begin{aligned} C(H) = \frac{\sum ^N_{\begin{array}{c} i,j = 1 \\ i\ne j \end{array}}{{\widetilde{\vartheta }}_{ij}(H)}}{\sum ^N_{i,j = 1}{{\widetilde{\vartheta }}_{ij}(H)}} \times 100 = \frac{\sum ^N_{\begin{array}{c} i,j = 1 \\ i\ne j \end{array}}{{\widetilde{\vartheta }}_{ij}(H)}}{N}\times 100 \end{aligned}$$, $$\begin{aligned} DS_{i\leftarrow j}(H) = \frac{\sum ^N_{\begin{array}{c} j = 1 \\ i\ne j \end{array}}{{\widetilde{\vartheta }}_{ij}(H)}}{N} \times 100 \end{aligned}$$, $$\begin{aligned} DS_{i\rightarrow j}(H) = \frac{\sum ^N_{\begin{array}{c} j = 1 \\ i\ne j \end{array}}{{\widetilde{\vartheta }}_{ji}(H)}}{N} \times 100. Policy uncertainty (also called regime uncertainty) is a class of economic risk where the future path of government policy is uncertain, raising risk premia and leading businesses and individuals to delay spending and investment until this uncertainty has been resolved. of high uncertainty, monetary policy shocks have smaller e ects on the yield curve in VAR models (Tillmann,2019) and on the macroeconomy (Aastveit et al.,2017).

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