Forecasting the US unemployment rate

• Authors: Tommaso Proietti
• Venue: Computational Statistics & Data Analysis - Special issue: Computational econometrics
• Year: 2003

The primary interest is in out-of-sample forecasting of the US monthly unemployment rate. Several linear unobserved components models are fitted and their comparative forecasting accuracy is assessed by means of an extensive rolling-origin procedure using a test period that covers the last two decades. An attempt is made to link forecasting performance to the time domain properties of the models and the evidence is that highly persistent models perform better. Deletion diagnostics and normality tests, along with documenting possible departures from linearity and Gaussianity attributable to business cycle and turning point asymmetries, foster the conclusion that these are mostly concentrated in the pre-forecast period (1948-1980). A search is made for plausible nonlinear extensions capable of accounting for dynamic asymmetries in unemployment rates, leading to the specification of a cyclical trend model with smooth transition in the underlying parameters that improves forecast accuracy at short lead times and at the end of the sample period; as expected, though significant, the gains are not exceptionally large. The generalised impulse response function casts some light on the interpretation of the results. In particular, the main evidence is that persistence is not a stable feature over the business cycle.