ABSTRACT
The bootstrap is typically less reliable in the context of time-series models with serial correlation of unknown form than when regularity conditions for the conventional IID bootstrap apply. It is, therefore, useful to have diagnostic techniques capable of evaluating bootstrap performance in specific cases. Those suggested in this paper are closely related to the fast double bootstrap (FDB) and are not computationally intensive. They can also be used to gauge the performance of the FDB itself. Examples of bootstrapping time series are presented, which illustrate the diagnostic procedures, and show how the results can cast light on bootstrap performance.
Acknowledgments
This research was supported by the Canada Research Chair program (Chair in Economics, McGill University) and by a grant from the Fonds de Recherche du Québec - Société et Culture. I am grateful to two anonymous referees, seminar participants at Emory University, the Universities of Aarhus, York, and Durham for helpful comments, and especially to James MacKinnon.
Notes
1The actual running time was only between two and two and a half minutes, because 50 cores were used in parallel.
2The constant estimates the expectation of τ* minus that of τ multiplied by the correlation between the two.