A multivariate unobserved components model of cyclical activity

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Alasdair Scott
This paper presents results from the estimation of a multivariate unobserved components model of cyclical activity. The model is motivated by a desire to let the data speak as much as possible, and hence to avoid imposing ad hoc and unjustifiable assumptions about trends and cycles. Estimated over the period 1970:1 to 1999:3 via the Kalman filter and maximum likelihood, the model identifies a common, trend-reverting component to real output, unemployment and capacity utilisation. The structure of the model allows an interesting factor interpretation to be put on the estimate of the output gap. These estimates are consistent with priors, but there is no consistent match to any one simple smoother such as the HP filter.