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The New Keynesian Phillips Curve, as a structural model of inflation dynamics, has mostly been used to explain past inflation developments, but has hardly been used for forecasting purposes. We propose a method of forecasting inflation based on the present-value formulation of the hybrid New Keynesian Phillips Curve. To evaluate the forecasting performance of this model we compare it with forecasts generated from time series models at different forecast horizons. As state-of-the-art time series models used in inflation forecasting we employ a Bayesian VAR, a traditional VAR and a simple autoregressive model. We find that the New Keynesian Phillips Curve delivers relatively more accurate forecasts compared to the other models for longer forecast horizons (more than 3 months) while they are outperformed by the time series models only for the very short forecast horizon. This is consistent with the finding in the literature that structural models are able to outperform time series models only for longer horizons.

We analyze 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.

The purpose of this paper is to model both loans to households and to non-financial corporations as well as their relation to interest rates and demand variables for Austria, Germany, the Netherlands and the United Kingdom. Credit aggregates are modeled using a Markov-switching vector autoregressive model, which allows testing as to whether shocks to the economy have stronger effects during tight credit regimes or economic downturns. The analysis of the above-mentioned countries makes it possible to assess the differences in the amplifying and asymmetric effects of credit aggregates between marketbased and bank-based financial systems.