Monetary shocks and production network in the G7 countries

Abstract

Abstract Understanding the structure and properties of production networks is essential to identify the transmission channels from monetary shocks. While growingly studied, this literature keeps displaying critical caveats from which the investigation of G-7 economies is not spared. To fill this gap, this paper applies a version of Time-Varying Parameters Bayesian Vector-Autoregressions models (TVP-VAR) and investigates the responses of production networks (upstream and downstream dynamics) to endogeneous monetary shocks on key macro-level indicators (GDP, GDP deflator, exchange rate, short-term and long-term interest rates). Two distinct time-lengths are considered: a test (i.e., 2000–2014) and a treated period (i.e., 2007–2009,”the Great Recession”). Prior, key statistical conditions are checked using a stepwise stationary testing framework including the Kwiatkowski–Phillips–Schmidt–Shin (Kapetanios et al. in J Economet 112(2):359–379, 2003—KPSS) and panel Breitung (Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, London, 2001) unit root tests; followed by the Pesaran (General diagnostic tests for cross section dependence in panels, 2004) Cross-sectional Dependence (CD) test; and the Im–Pesaran–Shin (Im et al. in J Economet 115(1):53–74, 2003—IPS) test for unit root in the presence of heterogenous slope coefficients. Panel Auto-Regressive Distributed Lag Mean Group estimates (PARDL-MG) offer interesting short- and long-run monetary shocks-production networks response functions, stratified by country and sector. Findings clearly indicate that upstreamness forces dominated downstremness dynamics during the period 2000–2014, whereas the financial sector ermeges as the clear transmission channel through which monetary shocks affected the productive economy during the Great Recession. In general, we conclude that the prioduction structure influences the transmission of monetary shocks in the G-7 economies. Adequate policy implications are supplied, along with a methodological note on the forecasting potential of TVP-VAR methodologies when dealing with series exhibiting structural breaks.

Keywords

• Production network
• Monetary policy shocks
• Panel ARDL model
• Bayesian VAR model