Panel variance tatio tests based on Maximum Absloute Value, Sum of Squares, and Mean of each cross-sectional units
Panel.VR(dat, nboot = 500)
the statistic based on the maximum absolute value of individual statistics
the statistic based on the sum of squared value of individual statistics
the statistic based on the mean value of individual statistics
the wild bootstrap pvalue based on the maximum absolute value of individual statistics
the wild bootstrap pvalue based on the sum of squared value of individual statistics
the wild bootstrap pvalue based on the mean value of individual statistics
a T by K matrix of asset returns, K is the munber of cross sectional units and T is length of time series
the number of wild bootstrap iterations, the default is set to 500
Jae H. Kim
The component statistics are based on the automatic variance ratio test The set of returns are wild bootstrapped to conserve cross-sectional dependency
Kim, J. H., & Shamsuddin, A. (2015). A closer look at return predictability of the US stock market: evidence from new panel variance ratio tests. Quantitative Finance, 15(9), 1501-1514.
ret=matrix(rnorm(200),nrow=100)
Panel.VR(ret)
Run the code above in your browser using DataLab