SMPLSplit_est function

Estimation of sub-sampled data

A function for estimating the subsampled data.

latin1

SMPLSplit_est(data,dep,indep,th,plot,h=1,nonpar=2)

Arguments

• data: the data in either data.frame or matrix.
• dep: the name of dependent variable.
• indep: the name(s) of independent variable(s).
• th: the name of threshold variable.
• plot: =1, plot; =0, do not plot.
• h: h=1, heteroskedasticity-consistent covariance; h=0, homoskedastic case.
• nonpar: Indicator for non-parametric method used to estimate nuisance scale in the presence of heteroskedasticity (only relevant if h=1).Set nonpar=1 to estimate regressions using a quadratic.Set nonpar=2 to estimate regressions using an Epanechnikov kernel with automatic bandwidth.

Returns

• threshold: values of threshold estimates.

• est0: coefficient estimates of global data.

• est.low: coefficient estimates of low regime.

• est.high: coefficient estimates of high regime.

• est0.info: additional information of global data.

• est.joint.info: additional information of joint threshods.

• est.low.info: additional information of est.low.

• est.high.info: additional information of est.high.

Details

This code estimates the parameters of sub-sampled data. It generalizes the simple code of Dr. Hansen, allowing White Corrected Heteroskedastic Errors.

Note

Original code offered by Dr. B. E.Hansen (http://www.ssc.wisc.edu/~bhansen/).

References

Hanse B. E. (2000) Sample Splitting and Threshold Estimation. Econometrica, 68, 575-603.

Examples

## Not run, becasue of bootstrap replicaiton takes time. Users may unmark # and run. 
data("dur_john")
rep <- 500
trim_per <- 0.15
dep <- "gdpGrowth"
indep <- colnames(dur_john)[c(2,3,4,5)]

SMPLSplit_est(data=dur_john,dep,indep,th="GDP60",plot=0,h=1,nonpar=2)