Uncertainty and business cycle synchronization in Europe

Figures & data

Figure 1. Business cycle synchronization in EMU: Cross-country standard deviation of HP-filtered GDP (1990Q1-2019Q2) and 3-year moving average.

Table 1. Panel regression results: determinants of business cycle synchronization in EMU

	(1)	(2)	(3)	(4)
Uncertainty	−0.0302 ${ }^{\ast }$	−0.0214 ${ }^{\ast }$	−0.0171 ${ }^{\ast }$	0.0331 ${ }^{\ast }$
	(0.0151)	(0.0103)	(0.00903)	(0.0159)
Lagged synchronization		0.532 ${ }^{\ast \ast \ast }$	0.504 ${ }^{\ast \ast \ast }$	0.419 ${ }^{\ast \ast \ast }$
		(0.0234)	(0.0234)	(0.0167)
Exports to EMU			0.0291 ${ }^{\ast \ast }$	0.0187 ${ }^{\ast }$
			(0.0123)	(0.00952)
Government balance			−0.0478	−0.0418
			(0.0856)	(0.0643)
Uncertainty$\times I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}\mathbf{h}\mathbf{r}{\mathbf{o}}_{it}<0)$				−0.285 ${ }^{\ast \ast \ast }$
				(0.0447)
$N$	1230	1215	1183	1183
$R^2$	0.024	0.304	0.299	0.413
adj. $R^2$	−0.046	0.254	0.245	0.368

Robust standard errors in parentheses, ${ }^{\ast }$ $p<0.10$, ${ }^{\ast \ast }$ $p<0.05$, ${ }^{\ast \ast \ast }$ $p<0.01$

Dependent variable is the synchronization measure in equation (2.1)(2.1) $\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}=log(s_{it})-log(s_t),$(2.1) .

Country and time fixed effects in all specifications.

Table 2. Bayesian model averaging results

	(1) Fixed country and time effects			(2) Standard BMA			(3) Strong heredity
Variable	PIP	Post. Mean	Post. SD	PIP	Post. Mean	Post. SD	PIP	Post. Mean	Post. SD
$\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{t-1}$	1.000	0.334	0.028	1.000	0.364	0.028	1.000	0.363	0.028
$\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{t-2}$	0.003	0.000	0.002	0.001	0.000	0.001	0.001	0.000	0.001
$\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{t-3}$	0.999	0.175	0.031	0.999	0.193	0.029	0.999	0.192	0.029
$\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{t-4}$	0.111	0.009	0.028	0.050	0.005	0.021	0.057	0.005	0.022
$\mathbf{u}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{r}{\mathbf{t}}_{t-1}$	0.003	0.000	0.001	0.001	0.000	0.000	0.976	0.003	0.012
$\mathbf{u}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{r}{\mathbf{t}}_{t-2}$	0.003	0.000	0.001	0.001	0.000	0.001	0.001	0.000	0.001
$\mathbf{u}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{r}{\mathbf{t}}_{t-3}$	0.003	0.000	0.001	0.000	0.000	0.000	0.001	0.000	0.000
$\mathbf{u}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{r}{\mathbf{t}}_{t-4}$	0.003	0.000	0.001	0.000	0.000	0.000	0.001	0.000	0.000
Exports${ }_{t-1}$	0.012	0.000	0.007	0.001	0.000	0.000	0.001	0.000	0.000
Exports${ }_{t-2}$	0.017	0.001	0.007	0.001	0.000	0.000	0.001	0.000	0.000
Exports${ }_{t-3}$	0.019	0.001	0.005	0.001	0.000	0.000	0.001	0.000	0.000
Exports${ }_{t-4}$	0.018	0.000	0.004	0.001	0.000	0.000	0.001	0.000	0.000
Gov.Bal.${ }_{t-1}$	0.003	0.000	0.003	0.001	0.000	0.001	0.001	0.000	0.001
Gov.Bal.${ }_{t-2}$	0.003	0.000	0.003	0.001	0.000	0.002	0.001	0.000	0.001
Gov.Bal.${ }_{t-3}$	0.003	0.000	0.003	0.001	0.000	0.002	0.001	0.000	0.001
Gov.Bal.${ }_{t-4}$	0.002	0.000	0.003	0.001	0.000	0.002	0.001	0.000	0.002
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$	0.973	−0.128	0.078	0.854	−0.037	0.020	1.000	−0.042	0.007
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$$\mathbf{u}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{r}{\mathbf{t}}_{t-1}$	1.000	−0.162	0.027	1.000	−0.163	0.026	0.986	−0.162	0.034
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$$\mathbf{u}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{r}{\mathbf{t}}_{t-2}$	0.020	0.001	0.009	0.007	0.000	0.005	0.000	0.000	0.000
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$$\mathbf{u}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{r}{\mathbf{t}}_{t-3}$	0.003	0.000	0.002	0.001	0.000	0.001	0.000	0.000	0.000
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$$\mathbf{u}\mathbf{n}\mathbf{c}\mathbf{e}\mathbf{r}{\mathbf{t}}_{t-4}$	0.002	0.000	0.001	0.000	0.000	0.001	0.000	0.000	0.000
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$Gov.Bal.${ }_{t-1}$	0.003	0.000	0.006	0.001	0.000	0.003	0.000	0.000	0.000
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$Gov.Bal.${ }_{t-2}$	0.004	0.000	0.008	0.001	0.000	0.005	0.000	0.000	0.000
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$Gov.Bal.${ }_{t-3}$	0.004	0.000	0.007	0.001	0.000	0.004	0.000	0.000	0.000
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$Gov.Bal.${ }_{t-4}$	0.008	0.001	0.012	0.002	0.000	0.007	0.000	0.000	0.001
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$Exports${ }_{t-1}$	0.143	0.001	0.009	0.049	0.000	0.002	0.000	0.000	0.000
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$Exports${ }_{t-2}$	0.157	0.002	0.010	0.040	0.000	0.002	0.000	0.000	0.000
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$Exports${ }_{t-3}$	0.161	0.003	0.020	0.034	0.000	0.004	0.000	0.000	0.000
$I(\mathbf{s}\mathbf{y}\mathbf{n}\mathbf{c}{\mathbf{h}}_{it}<0)$$\times$Exports${ }_{t-4}$	0.136	0.001	0.017	0.042	0.000	0.003	0.000	0.000	0.000

PIP stands for ‘Posterior inclusion probability’. Results based on 10,000,000 MCMC steps after a burn-in phase of 10,000 steps.

Strong heredity prior in setting 3 based on Crespo -Cuaresma (2011).

Crespo -Cuaresma, J. 2011. “How Different Is Africa? A Comment on Masanjala and Papageorgiou.” Journal of Applied Econometrics 26: 1041–1047. doi:https://doi.org/10.1002/jae.1180.

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