A mean-difference test based on self-normalization for alternating regime index data sets

Bo Gyeong Kim, Dong Wan Shin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We are interested in testing regime mean difference in some recently developed indexes which try to characterize alternating regimes: uncertainty indexes for economic expansion and recession and volatility spillover indexes for financial crisis and non-crisis. To account for strong serial correlation and conditional heteroskedasticity apparent in the index data sets, we consider the Kiefer–Vogelsang–Bunzell (KVB) self-normalization method for normalization of the estimated mean difference to construct a t-test. The limiting null distribution of the proposed test is shown to be different from the distribution derived by Kiefer–Vogelsang–Bunzel for a standard regression model. The proposed test is shown to have better finite sample size than the conventional t-test based on the Newey–West HAC standard error. Using the proposed test, we show a statistically significant counter-cyclical feature of uncertainty index and sensitivity of volatility spillover index to financial crisis.

Original languageEnglish
Article number108334
JournalEconomics Letters
StatePublished - Aug 2020

Bibliographical note

Funding Information:
A meaningful improvement is made by constructive comments of an anonymous referee. A grant from the National Research Foundation of Korea ( 2019R1A2C1004697 ) is appreciated. Appendix A

Publisher Copyright:
© 2019


  • Financial crisis
  • Recession
  • Self-normalization
  • Uncertainty index
  • Volatility spillover index


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