December 1st, 2012 by Stephan Smeekes
On the Applicability of the Sieve Bootstrap in Time Series Panels
Stephan Smeekes and Jean-Pierre Urbain
In this paper we investigate the validity of the univariate autoregressive sieve bootstrap applied to time series panels characterized by general forms of cross-sectional dependence, including but not restricted to cointegration. Using the final equations approach we show that while it is possible to write such a panel as a collection of infinite order autoregressive equations, the innovations of these equations are not vector white noise. This causes the univariate autoregressive sieve bootstrap to be invalid in such panels. We illustrate this result with a small numerical example using a simple bivariate system for which the sieve bootstrap is invalid, and show that the extent of the invalidity depends on the value of specific parameters. We also show that Monte Carlo simulations in small samples can be misleading about the validity of the univariate autoregressive sieve bootstrap. The results in this paper serve as a warning about the practical use of the autoregressive sieve bootstrap in panels where cross-sectional dependence of general from may be present.