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

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  • Kim, Bo Gyeong
  • Shin, Dong Wan

Abstract

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.

Suggested Citation

  • Kim, Bo Gyeong & Shin, Dong Wan, 2020. "A mean-difference test based on self-normalization for alternating regime index data sets," Economics Letters, Elsevier, vol. 193(C).
  • Handle: RePEc:eee:ecolet:v:193:y:2020:i:c:s0165176519300072
    DOI: 10.1016/j.econlet.2019.01.007
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    2. Christis Katsouris, 2023. "Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models," Papers 2308.13915, arXiv.org.

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    More about this item

    Keywords

    Financial crisis; Recession; Self-normalization; Uncertainty index; Volatility spillover index;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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