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Franck Sédillot

21 September 2021
The financing structure of the euro area economy has evolved since the global financial crisis with non-bank financial intermediation taking a more prominent role. This shift affects the transmission of monetary policy. Compared with banks, non-bank financial intermediaries are more responsive to monetary policy measures that influence longer-term interest rates, such as asset purchases. The increasing role of debt securities in the financing structure of firms also leads to a stronger transmission of long-rate shocks. At the same time, short-term policy rates remain an effective tool to steer economic outcomes in the euro area, which is still highly reliant on bank loans. Amid a low interest rate environment, the growth of market-based finance has been accompanied by increased credit, liquidity and duration risk in the non-bank sector. Interconnections in the financial system can amplify contagion and impair the smooth transmission of monetary policy in periods of market distress. The growing importance of non-bank financial intermediaries has implications for the functioning of financial market segments relevant for monetary policy transmission, in particular the money markets and the bond markets.
JEL Code
E4 : Macroeconomics and Monetary Economics→Money and Interest Rates
E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
G2 : Financial Economics→Financial Institutions and Services
G38 : Financial Economics→Corporate Finance and Governance→Government Policy and Regulation
1 September 2003
The first official data releases of quarterly real GDP for the euro area are published about eight weeks after the end of the reference quarters. Meanwhile, ongoing economic developments must be assessed from various, more readily available, monthly indicators. We examine in the context of univariate forecasting equations to what extent monthly indicators provide useful information for predicting euro area real GDP growth over the current and the next quarter. In particular, we investigate the performance of the equations under the case that the monthly indicators are only partially available within the quarter. For this purpose, we use time series models to forecast the missing observations of monthly indicators. We then examine GDP forecasts under different amounts of monthly information. We find that already a limited amount of monthly information improves the predictions for current-quarter GDP growth to a considerable extent, compared with ARIMA forecasts.
JEL Code
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods