Super Exogeneity (Parameter Invariance) Test

This function tests for super exogeneity (parameter invariance) of the model. Parameter invariance is a key component of Super Exogeneity, which was first introduced in Engle, Hendry, and Richard (1983; Econometrica). This test runs an indicator saturation model for each independent variable that is present in the initial.model object - these individual models are called the marginal models. If any outliers or step-shifts are detected using the isat function from the gets package in the marginal models, then these indicators are added to the initial model, called the conditional model. Any pre-existing indicators from the initial model are removed, as otherwise the power of the test would be reduced. The conditional model is then used to run a linear regression and to obtain an F-Stat statistic to determine whether the shocks detected in the marginal models affect the conditional model.

Usage

super.exogeneity(initial.model, saturation.tpval = 0.01, quiet = FALSE)

Arguments

initial.model: An object of class isat (i.e. from the gets package) that contains the initial model
saturation.tpval: The target p-value of the saturation methods (e.g. SIS and IIS, see the 'isat' function in the 'gets' package). Default is 0.01.
quiet: Logical. Should messages about the forecast procedure be suppressed?

Value

An h-test object with the results of the super exogeneity test

References

Engle, R. F., Hendry, D. F., & Richard, J. F. (1983). Exogeneity. Econometrica: Journal of the Econometric Society, 73-85.

Engle, R. F., & Hendry, D. F. (1993). Testing superexogeneity and invariance in regression models. Journal of Econometrics, 56(1-2), 119-139.

Hendry, D. F., & Santos, C., 'An Automatic Test of Super Exogeneity', in Bollerslev, T., Russell, J., & Watson, M. (Eds.). (2010). Volatility and time series econometrics: essays in honor of Robert Engle. OUP oxford.

Castle, J. L., Hendry, D. F., & Martinez, A. B. (2017). Evaluating forecasts, narratives and policy using a test of invariance. Econometrics, 5(3), 39.

Examples

#load Hoover and Perez (1999) data:
data(hpdata, package = "gets")

##run isat with step impulse saturation on two lags and a constant 1 percent significance level:
is.model <- gets::isat(
  y = hpdata$GCQ,
  mxreg = hpdata[,"GYDQ", drop = FALSE],
  ar = 1:2,
  sis = TRUE,
  t.pval = 0.01
)
#> 
#> NOTE: quantmod::as.zoo.data.frame() is deprecated
#>   Use as.zoo(x, order.by = as.Date(rownames(x))) instead.
#>   This note is printed once. To see it for every call, set
#>   options(quantmod.deprecate.as.zoo.data.frame = TRUE)
#> Error in charToDate(x): character string is not in a standard unambiguous format

super.exogeneity(is.model)
#> Error in eval(expr, envir, enclos): object 'is.model' not found