psymonitor provides an accessible implementation of the popular real-time monitoring strategy proposed by Phillips, Shi and Yu (2015a,b;PSY), along with a new bootstrap procedure designed to mitigate the potential impact of heteroskedasticity and to effect family-wise size control in recursive testing algorithms (Phillips and Shi, forthcoming). This methodology has been shown effective for bubble and crisis detection (PSY, 2015a,b; Phillips and Shi, 2017) and is now widely used by academic researchers, central bank economists, and fiscal regulators.
You can install the stable version from CRAN
You can install the development version from GitHub
For the illustration purposes we will use data on the credit risk in the European sovereign sector, that is proxied by an index constructed as a GDP weighted 10-year government bond yield of the GIIPS (Greece, Ireland, Italy, Portugal, and Spain) countries, and comes with the ‘psymonitor’ package.
Let’s walk through some basics. First load the
psymonitor package and get data on GIIPS spread.
Next, define a few parameters for the test and the simulation.
y <- spread$value obs <- length(y) swindow0 <- floor(obs * (0.01 + 1.8 / sqrt(obs))) # set minimal window size IC <- 2 # use BIC to select the number of lags adflag <- 6 # set the maximum nuber of lags to 6 yr <- 2 Tb <- 12*yr + swindow0 - 1 # Set the control sample size nboot <- 99 # set the number of replications for the bootstrap
bsadf <- PSY(y, swindow0 = swindow0, IC = IC, adflag = adflag) # estimate the PSY test statistics sequence quantilesBsadf <- cvPSYwmboot(y, swindow0 = swindow0, IC = IC, adflag = adflag, Tb = Tb, nboot = 99, nCores = 2) # simulate critical values via wild bootstrap. Note that the number of cores is arbitrarily set to 2.
Next, identify crisis periods, defined as periods where the test statistic is above its corresponding critical value, using the
Finally, print a table that holds the identified crisis periods with the help of the