mbreaks (version 1.0.0)

doseqtests: Sequential Sup F tests

Description

doseqtests() computes the sequential sup F tests of l versus l+1 for l from 1 to m with each corresponding null hypothesis of maximum number of break is l and alternative hypothesis is l+1. The l breaks under the null hypothesis are taken from the global minimization estimation

Usage

doseqtests(
  y_name,
  z_name = NULL,
  x_name = NULL,
  data,
  m = 5,
  eps = 1e-05,
  eps1 = 0.15,
  maxi = 10,
  fixb = 0,
  betaini = 0,
  printd = 0,
  prewhit = 1,
  robust = 1,
  hetdat = 1,
  hetvar = 1,
  hetq = 1,
  hetomega = 1,
  const = 1
)

Value

A list that contains following:

• supfl: SupF(l+1|l) test statistics

• cv: Critical values for SupF(l+1|l) test

Arguments

y_name

name of dependent variable in the data set

z_name

name of independent variables in the data set which coefficients are allowed to change across regimes. default is vector of 1 (Mean-shift model)

x_name

name of independent variables in the data set which coefficients are constant across regimes. default is NULL

data

name of data set used

m

maximum number of breaks

eps

convergence criterion for recursive calculations (For partial change model ONLY)

eps1

value of trimming (in percentage) for the construction and critical values. Minimal segment length h will be set at default = int(eps1*T) (T is total sample size). There are five options:

• eps1=0.05 Maximal value of m = 10

• eps1=0.10 Maximal value of m = 8

• eps1=.15 Maximal value of m = 5

• eps1=.20 Maximal value of m = 3

• eps1=.25 Maximal value of m = 2

• eps1=0 is not allowed. The test is undefined for no trimming level

maxi

number of maximum iterations for recursive calculations of finding global minimizers.default = 10 (For partial change model ONLY)

fixb

option to use fixed initial input \(\beta\). If 1, the model will use values given in betaini. If 0, betaini is skipped

betaini

Initial \(beta_0\) to use in estimation (Must be a p x 1 matrix, where p is number of x variables)

printd

Print option for model estimation. default = 0, to suppress intermediate outputs printing to console

prewhit

set to 1 to apply AR(1) prewhitening prior to estimating the long run covariance matrix.

robust

set to 1 to allow for heterogeneity and autocorrelation in the residuals, 0 otherwise. The method used is Andrews(1991) automatic bandwidth with AR(1) approximation with quadratic kernel. Note: Do not set to 1 if lagged dependent variables are included as regressors.

hetdat

option for the construction of the F tests. Set to 1 if want to allow different moment matrices of the regressors across segments. If hetdat = 0, the same moment matrices are assumed for each segment and estimated from the ful sample. It is recommended to set hetdat=1 if number of regressors x > 0.

hetvar

option for the construction of the F tests.Set to 1 if users want to allow for the variance of the residuals to be different across segments. If hetvar=0, the variance of the residuals is assumed constant across segments and constructed from the full sample. hetvar=1 when robust =1)

hetq

used in the construction of the confidence intervals for the break dates. If hetq=0, the moment matrix of the data is assumed identical across segments

hetomega

used in the construction of the confidence intervals for the break dates. If hetomega=0, the long run covariance matrix of zu is assumed identical across segments (the variance of the errors u if robust=0)

const

indicates whether the regression model include an intercept changing across regimes. Default value is 1

Examples

doseqtests('inf',c('inflag','lbs','inffut'),data=nkpc,prewhit=0)