distorttestboot: Bootstrapped Jiao-Pretis-Schwarz Outlier Distortion Test

In gets: General-to-Specific (GETS) Modelling and Indicator Saturation Methods

Bootstrapped Jiao-Pretis-Schwarz Outlier Distortion Test

Description

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.

Author(s)

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

Felix Pretis https://felixpretis.climateeconometrics.org/

Moritz Schwarz https://moritzschwarz.org

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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.3915040")}

See Also

isat, distorttest

Examples

  ## Not run: 
  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)

  
## End(Not run)

gets documentation built on Sept. 11, 2024, 9:03 p.m.