Abstract
We undertake Monte Carlo simulation experiments to examine the effect of changing the frequency of observations and the data span on the Phillips, P. C. B., S. Shi, and J. Yu. 2015. “Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500.” International Economic Review 56 (4): 1043–78 generalised supremum ADF (GSADF) test for explosive behaviour via Monte Carlo simulations. We find that when a series is characterised by multiple bubbles (periodically collapsing), decreasing the frequency of observations is associated with profound power losses for the test. We illustrate the effects of temporal aggregation by examining two real house price data bases, namely the S&P Case–Shiller real house prices and the international real house price indices available at the Federal Reserve Bank of Dallas.
Acknowledgement
We are grateful to an anonymous reviewer for useful comments.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
Appendix A: S&P Case-Shiller data
The price indices of Chicago, Los Angeles and New York have been deflated by the corresponding local CPI. For the other cities we used the CPI of the corresponding region as follows: Boston (North East); Denver, Las Vegas, San Diego and San Francisco (West); Miami and Washington DC (South).
S&P Case-Shiller home real price indices (1987m1 – 2020m6).
(continued)
Appendix B: Federal Reserve Bank of Dallas data
Real house price indexes in selected countries(1975q1 - 2019q4)
(continued)
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