Description Usage Arguments Details Value Methods Author(s) References See Also Examples
Fit a threshold cointegration regression between two time series.
1 2 
y 
dependent or leftside variable for the longrun model; must be a time series object. 
x 
independent or rightside variable for the longrun model; must be a time series object. 
model 
a choice of two models: 
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

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.
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 longrun regression 
data.CI 
data used in the threshold cointegration regression 
z 
residual from the longrun regression 
lz 
lagged residual from the longrun regression 
ldz 
lagged residual with 1st difference from longrun model 
LR 
longrun 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. 
One method is defined as follows:
print
:Four main outputs from threshold cointegration regression are shown: longrun regression between the two price variables, threshold cointegration regression, hypothesis test of no cointegration, and hypothesis test of no asymmetric adjustment.
Changyou Sun ([email protected])
Balke, N.S., and T. Fomby. 1997. Threshold cointegration. International Economic Review 38(3):627645.
Enders, W., and C.W.J. Granger. 1998. Unitroot tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics 16(3):304311.
Enders, W., and P.L. Siklos. 2001. Cointegration and threshold adjustment. Journal of Business and Economic Statistics 19(2):166176.
summary.ciTarFit
;
ciTarLag
for lag selection; and
ciTarThd
for threshold selection.
1  # see example at daVich

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