Simon van Norden

A well-documented property of the Beveridge–Nelson trend–cycle decomposition is the perfect negative correlation between trend and cycle innovations. We show how this may be consistent with a structural model where permanent innovations enter the cycle or transitory innovations enter the trend, and that identification restrictions are necessary to make this structural distinction. A reduced-form unrestricted version is compatible with either option, but cannot distinguish which is relevant. We discuss economic interpretations and implications using U.S. real GDP data.

The surge in fiscal deficits since 2008 has put a renewed focus on our understanding of fiscal policy. The interaction of fiscal and monetary policy during this

This article places the data revision model of Jacobs and van Norden ( 2011) Jacobs, J. P. A. M. and van Norden, S. 2011. Modelling data revisions: measurement error and dynamics of “true” values. Journal of Econometrics, 161: 101–9. [Crossref], [Web of Science ®] [Google Scholar] within a class of trend-cycle decompositions relating directly to the Beveridge–Nelson decomposition. In both these approaches, identifying restrictions on the covariance matrix under simple and realistic conditions may produce a smoothed estimate of the underlying series, which is more volatile than the observed series.

Data revisions in macroeconomic time series are typically studied in isolation ignoring the joint behaviour of revisions across different series. This ignores (

Speculative efficiency often requires that future changes in a series cannot be forecast. In contrast, series with a cyclical component would seem to be forecastable with decreases, possibly relative to a trend, during the upper part of the cycle and increases during the lower part. On the basis of autoregressive model (AR) estimates, it is considered that there is strong evidence of cycles in insurance underwriting performance as measured by the premium‐to‐loss ratio. Indeed, a large literature attempts to explain this documented cyclicality. First, we show that the parameter estimates from AR models do not lead to any such inference and that in the contrary, the evidence in the data is consistent with no cyclicality at all. Second, we show that a number of different filters lead to the same conclusion: that there is no evidence of in‐sample or out‐of‐sample predictability in annual insurance underwriting performance in the United States.