ciTarFit: Fitting Threshold Cointegration

Description Usage Arguments Details Value Methods Author(s) References See Also Examples

View source: R/ciTarFit.R

Description

Fit a threshold cointegration regression between two time series.

Usage

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  ciTarFit(y, x, model = c('tar','mtar'), lag = 1, thresh = 0,
    small.win = NULL)

Arguments

y

dependent or left-side variable for the long-run model; must be a time series object.

x

independent or right-side variable for the long-run model; must be a time series object.

model

a choice of two models: tar or mtar; the default is tar.

lag

number of lags for the threshold cointegration regression.

thresh

a threshold value (default of zero).

small.win

value of a small window for fitting the threshold cointegration regression; used mainly for lag selection in ciTarLag.

Details

This is the main function for threshold autoregression regression (TAR) in assessing the nonlinear threshold relation between two time series variables. It can be used to estimate four types of threshold cointegration regressions. These four types are TAR with a threshold value of zero; consistent TAR with a nonzero threshold; MTAR (momentum TAR) with a threshold value of zero; and consistent MTAR with a nonzero threshold. The option of small window will be used in lag selection because a comparison of AIC and BIC values should be based on the same number of regression observations.

Value

Return a list object of class "ciTarFit" with these components:

y

dependend variable

x

independent variable

model

model choice

lag

number of lags

thresh

threshold value

data.LR

data used in the long-run regression

data.CI

data used in the threshold cointegration regression

z

residual from the long-run regression

lz

lagged residual from the long-run regression

ldz

lagged residual with 1st difference from long-run model

LR

long-run regression

CI

threshold cointegration regression

f.phi

test with a null hypothesis of no threshold cointegration

f.apt

test with a null hypothesis of no asymmetric price transmission in the long run

sse

value of sum of squared errors

aic

value of Akaike Information Criterion

bic

value of Bayesian Information Criterion.

Methods

One method is defined as follows:

print:

Four main outputs from threshold cointegration regression are shown: long-run regression between the two price variables, threshold cointegration regression, hypothesis test of no cointegration, and hypothesis test of no asymmetric adjustment.

Author(s)

Changyou Sun ([email protected])

References

Balke, N.S., and T. Fomby. 1997. Threshold cointegration. International Economic Review 38(3):627-645.

Enders, W., and C.W.J. Granger. 1998. Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics 16(3):304-311.

Enders, W., and P.L. Siklos. 2001. Cointegration and threshold adjustment. Journal of Business and Economic Statistics 19(2):166-176.

See Also

summary.ciTarFit; ciTarLag for lag selection; and ciTarThd for threshold selection.

Examples

1
# see example at daVich

Example output

Loading required package: erer
Loading required package: lmtest
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: gWidgets

apt documentation built on May 30, 2017, 6:07 a.m.