doorder() estimates the number of breaks
using one of the following information criteria:
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
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
)A list of class model that contains one of the following:
change model with number of breaks selected by BIC
change model with number of breaks selected by LWZ
change model with number of breaks selected by KT
name of dependent variable in the data set
name of independent variables in the data set which coefficients are allowed to change
across regimes. default is vector of 1 (Mean-shift model)
name of independent variables in the data set which coefficients are constant across
regimes. default is NULL
name of data set used
maximum number of breaks
convergence criterion for iterative recursive computation
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
maximum number of iterations
option to use fixed initial input \(\beta\). If 1,
the model will use values given in betaini. If 0, betaini is skipped
Initial \(beta_0\) to use in estimation
Print option for model estimation. default = 0, to
suppress intermediate outputs printing to console
indicator which information criterion is used in selecting number of breaks:
{'KT','BIC','LWZ'}. The default value is 'KT'
indicates whether the regression model include an intercept changing across regimes. Default value is 1
Minimum segment length of regime considered in estimation. If users want to specify a particular value, please set eps1=0
set to 1 to apply AR(1) prewhitening prior to estimating
the long run covariance matrix.
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.
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
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)
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)
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.
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.
doorder('rate',data=real,ic=c('BIC'))
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