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Sylvia Kaufmann

1 December 2001
This paper investigates both cross-sectional asymmetry (related to bank-specific characteristics like size and liquidity) and asymmetries over time (potentially related to the overall state of the economy) in Austrian bank lending reaction to monetary policy. The first type of asymmetry is accounted for by including interaction terms, and the second type is captured by latent state-dependent parameters. Estimation is cast into a Bayesian framework, and the posterior inference is obtained using Markov chain Monte Carlo simulation methods. The results document a significant asymmetric effect of interest rate changes over time on bank lending. During economic recovery, lagged interest rate changes have no significant effect on lending. Where the effects are significant, liquidity emerges as the bank characteristic that determines cross-sectional asymmetry.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C23 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Panel Data Models, Spatio-temporal Models
E51 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Money Supply, Credit, Money Multipliers
Eurosystem Monetary Transmission Network
27 September 2007
We analyse the interaction between credit and asset prices in the transmission of shocks to the real economy. We estimate a Markov switching VAR for the euro area and the US, including additionally GDP, CPI and a short-term interest rate. We find evidence for two distinct states in both regions. For the euro area, we find a regime which is correlated to the business cycle and which captures periods of very low real credit growth at the end of recessions. However, during this regime credit markets and asset price markets do not impede economic recovery. In the other regime, we do find a procyclical effect of credit and asset price shocks on GDP. Shocks in both variables explain each about 20% of GDP's forecast error variance after four years. Credit shocks have a positive effect on inflation and explain about 35% of the forecast error variance, which confirms that credit aggregates contain information about the monetary stance. The effect of asset price shocks on inflation is insignificant and their share in explaining the forecast error variance negligible. For the US, regime 1 captures periods of stable GDP growth, and low and stable inflation, combined with accelerating asset prices. We find procyclical effects of credit and asset price shocks on GDP only in regime 2. Shocks in both variables explain about the same share (20%) of GDP forecast error variance, whereby the share explained by asset price shocks is about two and a half times larger than in regime 1. Shocks to credit and asset prices have no significant effect on CPI and explain each about 10% of its forecast error variance in both regimes. This is consistent with the view that monetary policy may achieve price stability without necessarily achieving financial stability.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy