psymonitor (version 0.0.2)

cvPSYwmboot: Conduct the new composite bootstrapping for the PSY test.

Description

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).

Usage

cvPSYwmboot(y, swindow0, IC = 0, adflag = 0, Tb, nboot = 199,
  useParallel = TRUE, nCores)

Arguments

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 useParallel=TRUE, use multi core computation.

nCores

A positive integer. Optional. If useParallel=TRUE, the number of cores defaults to all but one.

Value

A matrix. BSADF bootstrap critical value sequence at the 90, 95 and 99 percent level.

References

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.

Examples

# NOT RUN {
y <- rnorm(80)
cv <- cvPSYwmboot(y, IC = 0, adflag = 1, Tb = 30, nboot = 99, nCores = 1)

# }