Spatial asymmetries in monetary policy effectiveness in Italian regions

ABSTRACT

Pivoting on the idea that differences in production and financial systems may affect monetary transmission mechanisms, a global vector autoregressive (GVAR) model is built and the effectiveness of monetary policy on the real economy in the Italian regions in the period 2000–16 is tested, also taking interaction effects into account. The results show that regional gross domestic product responds positively, but asymmetrically to an expansionary monetary policy, while prices fall in the short run. It is also shown that short- and long-term interest rates react in accordance with theory.

KEYWORDS:

• monetary policy effectiveness
• global vector autoregressive (GVAR) models
• regional asymmetries

JEL:

• C31
• E52
• R10

ACKNOWLEDGEMENTS

The authors are particularly grateful to the editor in chief, the guest editor and two anonymous referees for most helpful, constructive comments. Thanks are also due to Alessandro Galesi, Bank of Spain, for technical support concerning the GVAR Matlab toolbox. Any remaining errors are the sole responsibility of the authors.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1 As in Ong and Sato (2018), we assume that the idiosyncratic shocks ${\eta }_{it}$ are serially uncorrelated with mean zero and non-singular covariance matrix ${\mathrm{\Sigma }}_{ii}$. The idiosyncratic shocks are denoted as ${\eta }_{it}\approx iid$ (0,${\mathrm{\Sigma }}_{ii}$).

2 The spatial dependence refers to the extent of similarity (or diversity) of the observed data in space and can be measured by spatial autocorrelation (Haining, 2003).

3 See Pesaran et al. (2004), Chen et al. (2017) and Burriel and Galesi (2018) for more details on the GVAR model solution.

4 We use the ISTAT classification: northern regions: Piedmont (pie), Valle D’Aosta (val), Liguria (lig), Lombardy (lom), Trentino-Alto Adige (tre), Veneto (ven), Friuli-Venezia Giulia (fri) and Emilia Romagna (emi); central regions: Tuscany (tos), Umbria (umb), Marche (mar) and Lazio (laz); and southern regions: Abruzzo (abr), Molise (mol), Campania (cam), Puglia (pug), Basilicata (bas), Calabria (cal), Sicily (sic) and Sardinia (sar).

5 The SSR is modelled] as:$SS{R}_t={\alpha }_1{\mathit{F}}_{1,\mathit{t}}+{\alpha }_2{\mathit{F}}_{2,\mathit{t}}+{\epsilon }_t$where $F_{1,t}$ is the first extracted component referring to the term premium and $F_{2,t}$ is the expectations component. Both components are weighted by the relative percentage of fit (α), while ${\epsilon }_t$ represents the error term. Only the first two components are considered when calculating SSR.

6 The model was estimated under the specification of GVAR Toolbox 2.0 (Smith & Galesi, 2014).

7 We include in the estimation other MP measures to check the robustness of INFL's responses. The results, which are available from the authors upon request, do not substantially change.

Additional information

Funding

This work was financed by the University of Naples ‘Parthenope’ within the Competitive Research Project ‘Institutional and Economic Imbalances in the Eurozone and the European Union’.