A “grid” bootstrap method is proposed for confidence-interval construction, which has improved performance over conventional bootstrap methods when the sampling distribution depends upon the parameter of interest. The basic idea is to calculate the bootstrap distribution over a grid of values of the parameter of interest and form the confidence interval by the no-rejection principle. Our primary motivation is given by autoregressive models, where it is known that conventional bootstrap methods fail to provide correct first-order asymptotic coverage when an autoregressive root is close to unity. In contrast, the grid bootstrap is first-order correct globally in the parameter space. Simulation results verify these insights, suggesting that the grid bootstrap provides an important improvement over conventional methods. Gauss code that calculates the grid bootstrap intervals-and replicates the empirical work reported in this paper'is available from the author's Web page at www.ssc.wisc.edu˜bhansen

We update the evidence on changes in job stability through the mid‐1990s, using recently released Current Population Survey data for 1995 that parallel earlier job tenure supplements. In the aggregate, job stability declined modestly in the first half of the 1990s. Moreover, the relatively small aggregate changes mask rather sharp declines in stability for workers with more than a few years of tenure. Nonetheless, the data available to this point do not support the conclusion that the downward shift in job stability for more tenured workers, and the more modest decline in aggregate job stability, reflect long‐term trends.

“… I suppose there exists an extremely powerful, and, if I may so speak, malignant being, whose whole endeavours are directed toward deceiving me.” Rene Descartes, Meditations, II.1