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 = \(T (0.01 + 1.8/\sqrt{T})\), where \(T\) denotes the sample size), |
| 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.