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ciTarThd: Threshold Selection for Threshold Cointegration Regression

Description

Select the best threshold for threshold cointegration regression by sum of squared errors

Usage

ciTarThd(y, x, model = c("tar", "mtar"), lag, th.range = 0.15, 
    digits = 3, ...)

Arguments

y
dependent or left-side variable for the long-run regression.
x
independent or right-side variable for the long-run regression.
model
a choice of two models, either tar or mtar.
lag
number of lags.
th.range
the percentage of observations to be excluded from the search.
digits
number of digits used in rounding outputs.
...
additional arguments to be passed.

Value

  • Return a list object of class "ciTarThd" with the following components:
  • modelmodel choice
  • lagnumber of lags
  • th.rangethe percentage of observations excluded
  • th.finalthe best threshold value
  • ssefthe best (i.e., lowest) value of SSE
  • obs.tottotal number of observations in the raw data
  • obs.CInumber of observations used in the threshold cointegration regression
  • basica brief summary of the major outputs
  • patha data frame of the search record (number of regression, threshold value, SSE, AIC, and BIC values).

Details

The best threshold is determined by fitting the regression for possible threshold values, sorting the results by sum of squared errors (SSE), and selecting the best with the lowest SSE. To have sufficient observations on either side of the threshold value, certain percentage of observations on the top and bottoms are excluded from the search path. This is usually set as 0.15 by the th.range (Chan 1993).

References

Chan, K.S. 1993. Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. The Annals of Statistics 21(1):520-533. 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.

See Also

ciTarFit; and ciTarLag

Examples

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