ciTarFit: Fitting Threshold Cointegration
Description
Fit a threshold cointegration regression between two time series.Usage
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.
Value
- Return a list object of class
"ciTarFit" with these components: - ydependend variable
- xindependent variable
- modelmodel choice
- lagnumber of lags
- threshthreshold value
- data.LRdata used in the long-run regression
- data.CIdata used in the threshold cointegration regression
- zresidual from the long-run regression
- lzlagged residual from the long-run regression
- ldzlagged residual with 1st difference from long-run model
- LRlong-run regression
- CIthreshold cointegration regression
- f.phitest with a null hypothesis of no threshold cointegration
- f.apttest with a null hypothesis of no asymmetric price
transmission in the long run
- ssevalue of sum of squared errors
- aicvalue of Akaike Information Criterion
- bicvalue of Bayesian Information Criterion.
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.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.