Bootstrapped Jiao-Pretis-Schwarz Outlier Distortion Test

Implements the Jiao-Pretis-Schwarz bootstrap test for coefficient distortion due to outliers by comparing coefficient estimates obtained using OLS to estimates obtained using the robust IIS estimator implemented using isat. Three bootstrap schemes are available - using the original sample (not recommended), the clean (outlier-removed) data, and using the clean (outlier-removed) sample with scaled cut-offs used to detect outliers in IIS implemented using isat. See the referenced Jiao-Pretis-Schwarz Paper below for more information.

Usage

distorttestboot(x, nboot, clean.sample = TRUE, parametric = FALSE, scale.t.pval = 1, 
parallel.options = NULL, quantiles = c(0.90, 0.95, 0.99), ...)

##S3 printing method for objects of class 'distorttestboot':
# S3 method for class 'distorttestboot'
print(x, print.proportion = FALSE, ...)

Arguments

x: object of class isat or the output of the distorttest function.
nboot: numeric. Number of bootstrap replications. A high number of replications are recommended for final estimation (more than 200 at least).
clean.sample: logical. Whether the outlier-removed sample should be used in resampling.
parametric: logical. Whether to use a parametric bootstrap. Default is non-parametric (FALSE). Parametric currently not implemented for autoregressive models.
scale.t.pval: numeric. Scaled target p-value (for selection) relative to the initial p-value used in isat. Default is 1. E.g. a value of 0.5 would scale an initial target p-value of 0.05 to 0.025.
parallel.options: NULL (Default) or an integer, i.e. the number of cores/threads to be used for parallel computing (implemented w/makeCluster and parLapply).
print.proportion: logical. Should the bootstraped Jiao-Pretis Outlier Proportion Test be printed. Default is FALSE.
quantiles: numeric vector. Quantiles to be shown based on the bootstrapped results. Default is c(0.90, 0.95, 0.99).
...: Further arguments passed to isat.

Value

A list including an object of class h-test.

References

Xiyu Jiao, Felix Pretis,and Moritz Schwarz. Testing for Coefficient Distortion due to Outliers with an Application to the Economic Impacts of Climate Change. Available at SSRN: https://www.ssrn.com/abstract=3915040 or doi:10.2139/ssrn.3915040

Author

Xiyu Jiao https://sites.google.com/view/xiyujiao

Felix Pretis http://www.felixpretis.org

Moritz Schwarz https://moritzschwarz.org

Examples

  if (FALSE) { # \dontrun{
  data(Nile)
  nile <- isat(Nile, sis=FALSE, iis=TRUE, plot=TRUE, t.pval=0.01)
  
  distorttest(nile)
  # bootstrap (with nboot = 5 to save time. Higher replications are recommended)
  distorttestboot(nile, nboot = 5)
  
  data("hpdata")
  # Another example with co-variates
  dat <- hpdata[,c("GD", "GNPQ", "FSDJ")]
  Y <- ts(dat$GD,start = 1959, frequency = 4)
  mxreg <- ts(dat[,c("GNPQ","FSDJ")],start = 1959, frequency = 4)
  m1 <- isat(y = Y, mc = TRUE, sis = FALSE, iis = TRUE)
  m2 <- isat(y = Y, mc = TRUE, sis = FALSE, iis = TRUE, ar = 1)
  m3 <- isat(y = Y, mxreg = mxreg, mc = TRUE, sis = FALSE, iis = TRUE)
  m4 <- isat(y = Y, mxreg = mxreg, mc = TRUE, sis = FALSE, iis = TRUE, ar = 1, t.pval = 0.01)
  distorttest(m1, coef = "all")
  distorttest(m2, coef = "all")
  distorttest(m3, coef = "GNPQ")
  distorttest(m4, coef = c("ar1", "FSDJ"))
  
  # bootstrap (with nboot = 5 to save time. Higher replications are recommended)
  distorttestboot(m1, nboot = 5)
  distorttestboot(m2, nboot = 5)
  distorttestboot(m3, nboot = 5)
  distorttestboot(m4, nboot = 5)
  distorttestboot(m4, nboot = 5, parametric = TRUE, scale.t.pval = 0.5)

  } # }