dosequa {mbreaks}

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 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 structural changes allowed. If not specify, m will be set to default value matching eps1 input

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 = int(eps1*T) (T is total sample size).

• 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 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 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

signif

significance level used to sequential test to select number of breaks.

• 4: 1% level

• 3: 2.5% level

• 2: 5% level

• 1: 10% level

Value

out A list of model class with number of breaks selected by sequential tests

Examples

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