thr_test_hom: Threshold Test under Homoskedasticity
In mlkremer/thrreg: Threshold Regression Model

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/thr_test_hom.R

Computes a test for a threshold in linear regression under homoskedasticity.

thr_test_hom(
  df,
  yi,
  xi,
  qi,
  var.names = colnames(df),
  trim_per = 0.15,
  rep = 1000,
  cr = 0.95,
  graph = TRUE,
  quick = 1
)

`df`	Data frame.
`yi`	Integer or character; index or column name of dependent (y) variable in `df`.
`xi`	Integer or character vector; indexes or column names of independent (x) variables in `df`.
`qi`	Integer or character; index or column name of threshold (q) variable in `df`.
`var.names`	Character vector; variable names with `length(var.names) == ncol(df)` corresponding to columns in `df` to be used in threshold regression table. Default is `colnames(df)`.
`trim_per`	Numeric; percentage of sample to trim from ends. Default is `trim_per = .15`.
`rep`	Integer; number of bootstrap replications. Default is `rep = 1000`.
`cr`	Numeric; confidence level used to plot the critical value in the graph. It is not used elsewhere in the analysis. Default is `cr = .95`.
`graph`	Logical; graph indicator. Set `TRUE` (default) to view the graph of the likelihood; set `FALSE` otherwise.
`quick`	Integer; indicator of method used for bootstrap. Set `quick = 1` (default) for efficient, quick computation; set `quick = 0` if memory is limited (perhaps for large data sets).

Do not include a constant in the independent variables; the function automatically adds an intercept to the regression.
The function stores the sequential design matrices in order to speed up the bootstrap computations. It is possible that if your dataset is very large, this will exceed your computer RAM memory. If so, function will crash, and the message Error: allocMatrix: too many elements specified will be displayed. If more RAM is not available, switch to quick = 0. The switch quick = 0 requires the bootstrap to re-calculate the design matrices for each bootstrap replication, which requires less memory, but somewhat more computer time.

A list with components:

f_test: the value of Maximal (Quandt) F-statistic.
p_value: the bootstrap p-value.

Marcel Kremer, marcel.kremer@uni-due.de

Bruce E. Hansen, behansen@wisc.edu

Hansen, B. E. (2000). Sample splitting and threshold estimation. Econometrica, 68(3):575–603. https://doi.org/10.1111/1468-0262.00124. https://www.ssc.wisc.edu/~bhansen/papers/ecnmt_00.pdf.

thr_test_het for threshold test under heteroskedasticity, thr_est for threshold estimation.

## Performs part of the empirical work reported in Hansen (2000)
data <- dur_john
output <- thr_test_hom(data, 1, 2:5, 6)

output$f_test
output$p_value