dosequa: Estimating number of breaks using sequential tests

In mbreaks: Estimation and Inference for Structural Breaks in Linear Regression Models

Estimating number of breaks using sequential tests

Description

'dosequa()' sequentially increases the number of breaks from '1' to 'm' until the sequential tests reject and estimate the structural change model with corresponding estimated breaks. The procedure is proposed by Bai and Perron, 1998.

Usage

dosequa(
  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,
  signif = 2
)

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 value is vector of 1 (Mean-shift model).
x_name	name of independent variables in the data set, which coefficients are constant across regimes. Default value is NULL.
data	name of the data set used
m	maximum number of breaks
eps	convergence criterion for iterative recursive computation
eps1	value of trimming (in percentage) for the construction and critical values. Minimal segment length 'h' will be set at default value = 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 = 0.15 Maximal value of m = 5. eps1 = 0.20 Maximal value of m = 3. eps1 = 0.25 Maximal value of m = 2. eps1 = 0 This option is not allowed.
maxi	maximum number of iterations
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
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 you 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 full 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 includes an intercept changing across regimes. Default value is 1
signif	significance level used in the sequential test to select number of breaks. 4: 1% level 3: 2.5% level 2: 5% level 1: 10% level

Value

A list of 'model' class with the number of breaks selected by sequential tests.

Examples

dosequa('rate', data = real, signif = 1)

mbreaks documentation built on April 3, 2025, 7:38 p.m.