No abstract

We analyse the effect of labour taxes on euro area unemployment. Making use of the Blanchard and Katz (1999) [Wage dynamics: reconciling theory and evidence. American Economic Review 89(2), 69-74.] NAIRU model, we represent structural unemployment as that made up of two elements: an observed part related to labour taxes plus a non-stationary unobservable component related to the reservation wage and to the wage mark-up. Contrary to empirical estimates available so far, this modelling explicitly acknowledges the existence of non-measurable factors responsible for the unemployment non-stationarity. For the period 1970-2004, we obtain a tax effect that lies in the middle of the estimates that can be found in the literature. This study gives support to the view that lowering labour taxes can help to reduce unemployment in continental Europe.

SUMMARYPotential output plays a central role in monetary policy and short-term macroeconomic policy making. Yet, characterizing the output gap involves a trend-cycle decomposition, and unobserved component estimates are typically subject to a large uncertainty at the sample end. An important consequence is that output gap estimates can be quite inaccurate in real time, as recently highlighted by Orphanides and van Norden (2002), and this causes a serious problem for policy makers. For the cases of the US, EU-11 and two EU countries, we evaluate the benefits of using inflation data for improving the accuracy of real-time estimates.

The paper deals with the problem of identifying stochastic unobserved twocomponent models, as in seasonal adjustment or trend-cycle decompositions. Solutions based on the properties of the component estimation errors are con sidered, and analytical expressions for the variances and covariances of the dif ferent types of estimation errors (errors in the final, preliminary, and concurrent estimator and in the forecast) are obtained for any admissible decomposition. These expressions are relatively simple and straightforwardly derived from the A r im a model for the observed series. It is shown that, in all cases, the estimation error variance is minimized at a canonical decomposition (i.e., at a decomposition with one of the components noninvertible), and a procedure to determine that decomposition is presented. On occasion, however, the most precise final estimator is obtained at a canoni cal decomposition different from the one that yields the most precise concurrent estimator. Three examples illustrate the results and the computational algorithms. The first and second examples are based on the so-called Structural Time Series Model and A r im a Model Based approaches, respectively. The third example is a class of models often encountered in actual time series.

No abstract