Autoregressions of quarterly or annual aggregate time series provide evidence of trend-reverting output growth and of short-term dynamic adjustment that appears to be governed by complex eigenvalues. This finding is at odds with the predictions of reasonably parameterized, convex one-sector...

In this paper the class of admissable tests for unit roots in panel data sets of autoregressive, Gaussian time series will be partially characterized. Using this characterization, several recently suggested tests are shown to be inadmissable. Since the sufficient statistic for this testing...

Estimates of the speed of convergence vary widely and depend on the methodology employed. While cross-sectional regressions typically find slow convergence, time series estimates suggest that incomes converge rapidly. This paper uses panel methods to combine cross-sectional and time series...

Aggregate time series provide evidence of short term dynamic adjustment that appears to be governed by complex or negative real eigenvalues. This finding is at odds with the predictions of reasonably parameterized, convex one-sector growth models with complete markets. We study life cycle...

Many economic applications call for simultaneous equations VAR modeling. We show that the existing importance sampler can be prohibitively inefficient for this type of models. We develop a Gibbs simulator that works for both simultaneous and recursive VAR models with a much broader range of...

This paper demonstrates two advantages of well-known block variants of standard algorithms for solving nonlinear systems. First, if a problem is suf­ficiently close to block-diagonal, block algorithms may offer significant speed advantages on a single processor. Second, block Jacobi algorithms...

This paper develops a new approach to change-point modeling that allows for an unknown number of change points in the observed sample. Our model assumes that regime durations have a Poisson distribution. The model approximately nests the two most common approaches: the time-varying parameter...