Center for Financial Studies (CFS)
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In this study, we perform a quantitative assessment of the role of money as an indicator variable for monetary policy in the euro area. We document the magnitude of revisions to euro area-wide data on output, prices, and money, and find that monetary aggregates have a potentially significant role in providing information about current real output. We then proceed to analyze the information content of money in a forward-looking model in which monetary policy is optimally determined subject to incomplete information about the true state of the economy. We show that monetary aggregates may have substantial information content in an environment with high variability of output measurement errors, low variability of money demand shocks, and a strong contemporaneous linkage between money demand and real output. As a practical matter, however, we conclude that money has fairly limited information content as an indicator of contemporaneous aggregate demand in the euro area.
This paper examines to what extent the build-up of "global imbalances" since the mid-1990s can be explained in a purely real open-economy DSGE model in which agents’ perceptions of long-run growth are based on filtering observed changes in productivity. We show that long-run growth estimates based on filtering U.S. productivity data comove strongly with long-horizon survey expectations. By simulating the model in which agents filter data on U.S. productivity growth, we closely match the U.S. current account evolution. Moreover, with household preferences that control the wealth effect on labor supply, we can generate output movements in line with the data. JEL Classification: E13, E32, D83, O40
he predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations consistent Kalman filter in the process of achieving this objective. As an empirical application, we analyze euro area data and compare the density forecast performance of a DSGE model to DSGE-VARs and reduced-form linear Gaussian models.