| cvPSYwmboot | R Documentation | 
cvPSYwmboot implements the new bootstrap procedure designed
to detect bubbles and crisis periods while mitigating the potential impact
of heteroskedasticity and to effect family-wise size control in recursive
testing algorithms (Phillips and Shi, forthcoming).
cvPSYwmboot(y, swindow0, IC = 0, adflag = 0, Tb, nboot = 199,
  useParallel = TRUE, nCores)
| y | A vector. The data. | 
| swindow0 | A positive integer. Minimum window size (default =  | 
| IC | An integer. 0 for fixed lag order (default), 1 for AIC and 2 for BIC (default = 0). | 
| adflag | An integer, lag order when IC=0; maximum number of lags when IC>0 (default = 0). | 
| Tb | A positive integer. The simulated sample size (swindow0+ controlling). | 
| nboot | A positive integer. Number of bootstrap replications (default = 199). | 
| useParallel | Logical. If  | 
| nCores | A positive integer. Optional. If  | 
A matrix. BSADF bootstrap critical value sequence at the 90, 95 and 99 percent level.
Phillips, P. C. B., Shi, S., & Yu, J. (2015a). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International Economic Review, 56(4), 1034–1078.
Phillips, P. C. B., Shi, S., & Yu, J. (2015b). Testing for multiple bubbles: Limit Theory for Real-Time Detectors. International Economic Review, 56(4), 1079–1134.
Phillips, P. C. B., & Shi, S.(forthcoming). Real time monitoring of asset markets: Bubbles and crisis. In Hrishikesh D. Vinod and C.R. Rao (Eds.), Handbook of Statistics Volume 41 - Econometrics Using R.
y <- rnorm(80)
cv <- cvPSYwmboot(y, IC = 0, adflag = 1, Tb = 30, nboot = 99, nCores = 1)
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