C51 Model Construction and Estimation

April 24, 2012

This paper uses a data-set including time series data on macroeconomic variables, loans, deposits and interest rates for the euro area in order to study the features of financial intermediation over the business cycle. We find that stylized facts for aggregate monetary and real variables are re- markably similar to what has been found for the US by many studies while we uncover new facts on disaggregated loans and deposits. During the crisis the cyclical behavior of short term interest rates, loans and deposits remain stable but we identify unusual dynamics of longer term loans, deposits and longer term interest rates.

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March 1, 2009

In this paper, we perform a structural Bayesian estimation of the contribution of anticipated shocks to business cycles in the postwar United States. Our theoretical framework is a real-business-cycle model augmented with four real rigidities: investment adjustment costs, variable capacity utilization, habit formation in consumption, and habit formation in leisure. Business cycles are assumed to be driven by permanent and stationary neutral productivity shocks, permanent investment-specific shocks, and government spending shocks. Each of these driving forces is buffeted by four types of structural innovations: unanticipated innovations and innovations anticipated one, two, and three quarters in advance. We find that anticipated shocks account for more than two thirds of predicted aggregate fluctuations.

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February 1, 2006

We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over first forecasting the disaggregates and then aggregating those forecasts, or, alternatively, over using only lagged aggregate information in forecasting the aggregate. We show theoretically that the first method of forecasting the aggregate should outperform the alternative methods in population. We investigate whether this theoretical prediction can explain our empirical findings and analyse why forecasting the aggregate using information on its disaggregate components improves forecast accuracy of the aggregate forecast of euro area and US inflation in some situations, but not in others.

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October 1, 2005

We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast from the predictive likelihood rather than the standard marginal likelihood. The use of predictive measures of fit offers greater protection against in-sample overfitting and improves forecast performance. For the predictive likelihood we show analytically that the forecast weights have good large and small sample properties. This is confirmed in a simulation study and an application to forecasts of the Swedish inflation rate where forecast combination using the predictive likelihood outperforms standard Bayesian model averaging using the marginal likelihood.

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