Pitfalls in bootstrapping spurious regression
Pitfalls in bootstrapping spurious regression
The bootstrap is shown to be inconsistent in spurious regression. The failure of the bootstrap is spectacular in that the bootstrap effectively turns a spurious regression into a cointegrating regression. In particular, the serial correlation coefficient of the residuals in the bootstrap regression does not converge to unity, so the bootstrap is not even first order consistent. The block bootstrap serial correlation coefficient does converge to unity and is therefore first order consistent, but has a slower rate of convergence and a different limit distribution from that of the sample data serial correlation coefficient. The analysis covers spurious regressions involving both deterministic trends and stochastic trends. Methods are developed for analyzing the asymptotic behavior of bootstrap techniques with nonstationary time series and the results reinforce longstanding warnings about routine use of the bootstrap with dependent data.
163–217
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Phillips, Peter Charles Bonest
(2021)
Pitfalls in bootstrapping spurious regression.
Journal of Quantitative Economics, 19 (S1), .
(doi:10.1007/s40953-021-00268-6).
Abstract
The bootstrap is shown to be inconsistent in spurious regression. The failure of the bootstrap is spectacular in that the bootstrap effectively turns a spurious regression into a cointegrating regression. In particular, the serial correlation coefficient of the residuals in the bootstrap regression does not converge to unity, so the bootstrap is not even first order consistent. The block bootstrap serial correlation coefficient does converge to unity and is therefore first order consistent, but has a slower rate of convergence and a different limit distribution from that of the sample data serial correlation coefficient. The analysis covers spurious regressions involving both deterministic trends and stochastic trends. Methods are developed for analyzing the asymptotic behavior of bootstrap techniques with nonstationary time series and the results reinforce longstanding warnings about routine use of the bootstrap with dependent data.
Text
Btstrapr_2021_B5
- Accepted Manuscript
More information
Accepted/In Press date: 6 September 2021
e-pub ahead of print date: 3 December 2021
Identifiers
Local EPrints ID: 451541
URI: http://eprints.soton.ac.uk/id/eprint/451541
ISSN: 0971-1554
PURE UUID: 89487c69-0efa-4dbc-b427-7515024b215f
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Date deposited: 06 Oct 2021 19:11
Last modified: 17 Mar 2024 06:50
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