Nonlinear time series models have become fashionable tools to describe and forecast a variety of economic time series. A closer look at reported empirical studies, however, reveals that these models apparently fit well in-sample, but rarely show a substantial improvement in out-of-sample...

We discuss a method to estimate the confidence bounds for average economic growth, which is robust to misspecification of the unit root property of a given time series. We derive asymptotic theory for the consequences of such misspecification. Our empirical method amounts to an implementation of...

In this paper, the authors analyze a model selection strategy for periodic autoregressive time-series processes. It involves autoregressive order selection and tests for unit roots at the (non-)seasonal frequencies. The strategy is evaluated using Monte Carlo replications and it is applied to...

Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model-based methods is that these can provide confidence intervals around the seasonally...

Augmenting a first-order dynamic regression model by adding particular redundant regressors gives a least-squares estimator of the lagged-dependent variable coefficient that is independent of nuisance parameters under a null hypothesis. This estimator and its t ratio have finite sample null...