ivisat {ivgets}

Indicator saturation modeling for 2SLS models

Description

Indicator saturation modeling for 2SLS models

Usage

ivisat(
  formula,
  data,
  iis = TRUE,
  sis = FALSE,
  tis = FALSE,
  uis = FALSE,
  blocks = NULL,
  ratio.threshold = 0.8,
  max.block.size = 30,
  t.pval = 1/NROW(data),
  wald.pval = t.pval,
  do.pet = FALSE,
  ar.LjungB = NULL,
  arch.LjungB = NULL,
  normality.JarqueB = NULL,
  info.method = c("sc", "aic", "hq"),
  include.1cut = FALSE,
  include.empty = FALSE,
  max.paths = NULL,
  parallel.options = NULL,
  turbo = FALSE,
  tol = 1e-07,
  max.regs = NULL,
  print.searchinfo = TRUE,
  plot = NULL,
  alarm = FALSE,
  overid = NULL,
  weak = NULL,
  fast = FALSE
)

Arguments

formula

A formula in the format y ~ x1 + x2 | z1 + z2.

data

A data frame with all necessary variables y, x, and z.

iis

logical. If TRUE, impulse indicator saturation is performed.

sis

logical. If TRUE, step indicator saturation is performed.

tis

logical. If TRUE, trend indicator saturation is performed.

uis

a matrix of regressors, or a list of matrices. If a list, the matrices must have named columns that should not overlap with column names of any other matrices in the list.

blocks

NULL (default), an integer (the number of blocks) or a user-specified list that indicates how blocks should be put together. If NULL, then the number of blocks is determined automatically

ratio.threshold

Minimum ratio of variables in each block to total observations to determine the block size, default=0.8. Only relevant if blocks = NULL

max.block.size

Maximum size of block of variables to be selected over, default=30. Block size used is the maximum of given by either the ratio.threshold and max.block.size

t.pval

numeric value between 0 and 1. The significance level used for the two-sided regressor significance t-tests

wald.pval

numeric value between 0 and 1. The significance level used for the Parsimonious Encompassing Tests (PETs)

do.pet

logical. If TRUE, then a Parsimonious Encompassing Test (PET) against the GUM is undertaken at each regressor removal for the joint significance of all the deleted regressors along the current path. If FALSE (default), then a PET is not undertaken at each regressor removal. By default, the numeric value is the same as that of t.pval

ar.LjungB

a two-item list with names lag and pval, or NULL (default). In the former case lag contains the order of the Ljung and Box (1979) test for serial correlation in the standardised residuals, and pval contains the significance level. If lag=NULL (default), then the order used is that of the estimated 'arx' object. If ar.Ljungb=NULL, then the standardised residuals are not checked for serial correlation

arch.LjungB

a two-item list with names lag and pval, or NULL (default). In the former case, lag contains the order of the Ljung and Box (1979) test for serial correlation in the squared standardised residuals, and pval contains the significance level. If lag=NULL (default), then the order used is that of the estimated 'arx' object. If arch.Ljungb=NULL, then the standardised residuals are not checked for ARCH

normality.JarqueB

NULL (the default) or a value between 0 and 1. In the latter case, a test for non-normality is conducted using a significance level equal to normality.JarqueB. If NULL, then no test for non-normality is conducted

info.method

character string, "sc" (default), "aic" or "hq", which determines the information criterion to be used when selecting among terminal models. The abbreviations are short for the Schwarz or Bayesian information criterion (sc), the Akaike information criterion (aic) and the Hannan-Quinn (hq) information criterion

include.1cut

logical. If TRUE, then the 1-cut model is included among the terminal models, if it passes the diagnostic tests, even if it is not equal to one of the terminals. If FALSE (default), then the 1-cut model is not included (unless it is one of the terminals)

include.empty

logical. If TRUE, then an empty model is included among the terminal models, if it passes the diagnostic tests, even if it is not equal to one of the terminals. If FALSE (default), then the empty model is not included (unless it is one of the terminals)

max.paths

NULL (default) or an integer indicating the maximum number of paths to search

parallel.options

NULL or an integer, i.e. the number of cores/threads to be used for parallel computing (implemented w/makeCluster and parLapply)

turbo

logical. If TRUE, then (parts of) paths are not searched twice (or more) unnecessarily, thus yielding a significant potential for speed-gain. However, the checking of whether the search has arrived at a point it has already been comes with a slight computational overhead. Accordingly, if turbo=TRUE, then the total search time might in fact be higher than if turbo=FALSE. This happens if estimation is very fast, say, less than quarter of a second. Hence the default is FALSE

tol

numeric value (default = 1e-07). The tolerance for detecting linear dependencies in the columns of the regressors (see qr function). Only used if LAPACK is FALSE (default)

max.regs

integer. The maximum number of regressions along a deletion path. It is not recommended that this is altered

print.searchinfo

logical. If TRUE (default), then a print is returned whenever simiplification along a new path is started, and whenever regressors are dropped due to exact multicolinearity

plot

NULL or logical. If TRUE, then the fitted values and the residuals of the final model are plotted after model selection. If NULL (default), then the value set by options determines whether a plot is produced or not.

alarm

logical. If TRUE, then a sound is emitted (in order to alert the user) when the model selection ends

overid

NULL if no Sargan test of overidentifying restrictions should be used as a diagnostic check for model selection or a numeric value between 0 and 1. In the latter case, the test is conducted using this value as the significance level.

weak

NULL if no weak instrument F-test on the first stage should be used as a diagnostic check for model selection or a numeric value between 0 and 1. In the latter case, the test is conducted using this value as the significance level.

fast

A logical value indicating whether to speed up the 2SLS estimation but providing less details. Requires overid == NULL and weak == NULL.

Value

Returns a list of class "ivisat" with two named elements. $selection stores the selection results from isat (including paths, terminal models, and best specification). $final stores the ivreg model object of the best specification or NULL if the GUM does not pass all diagnostics.