doorder {mbreaks}

Estimating number of breaks via information criterion

Description

doorder() estimates the number of breaks using one of the following information criteria:

• modified Bayesian information criterion by Kurozumi and Tuvaandorj, 2011,

• modified Schwarz information criterion by Liu, Wu and Zidek, 1997,

• Bayesian information criterion by Yao, 1988

and the structural break model corresponding to estimated number of breaks

Usage

doorder(
  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,
  ic = "KT",
  const = 1,
  h = NULL,
  prewhit = 1,
  hetdat = 1,
  hetq = 1,
  hetomega = 1,
  hetvar = 1,
  robust = 1
)

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 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 allows users to explicitly specify minimum segment length h parameters

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

ic

indicator which information criterion is used in selecting number of breaks: ⁠{'KT','BIC','LWZ'}⁠. The default value is 'KT'

const

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

h

Minimum segment length of regime considered in estimation. If users want to specify a particular value, please set eps1=0

prewhit

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

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.

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)

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)

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.

Value

A list of class model that contains one of the following:

mBIC

change model with number of breaks selected by BIC

mLWZ

change model with number of breaks selected by LWZ

mKT

change model with number of breaks selected by KT

References

Liu J, Wu S, Zidek JV (1997). "On Segmented Multivariate Regressions", Statistica Sinica, 7, 497-525. Yao YC (1988). "Estimating the Number of Change-points via Schwartz Criterion", Statistics and Probability Letters, 6, 181-189. Kurozumi E, Tuvaandorj P (2011). "Model Selection Criteria in Multivariate Models with Multiple Structural Changes", Journal of Econometrics 164, 218-238.

Examples

doorder('rate',data=real,ic=c('BIC'))