February 17, 2020

Estimation of the Financial Cycle with a Rank-Reduced Multivariate State-Space Model

We propose a model-based method to estimate a unique financial cycle based on a rank-restricted multivariate state-space model. This permits us to use mixed-frequency data, allowing for longer sample periods. In our model the financial cycle dynamics are captured by an unobserved trigonometric cycle component.
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We identify a single financial cycle from the multiple time series by imposing rank reduction on this cycle component. The rank reduction can be justified based on a principal components argument. The model also includes unobserved components to capture the business cycle, time-varying seasonality, trends, and growth rates in the data. In this way we can control for these effects when estimating the financial cycle. We apply our model to US and Dutch data and conclude that a bivariate model of credit and house prices is sufficient to estimate the financial cycle.

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Foto Rob Luginbuhl
Rob Luginbuhl +31 6 11301400 Read more