Select the best lag for threshold cointegration regression by AIC and BIC
1 2 
y 
dependent or leftside variable for the longrun regression. 
x 
independent or rightside variable for the longrun regression. 
model 
a choice of two models, either tar or mtar. 
maxlag 
maximum number of lags allowed in the search process. 
thresh 
a threshold value. 
adjust 
logical value (default of TRUE) of whether to adjust the window widths so all regressions by lag have the same number of observations 
Estimate the threshold cointegration regressions by lag and then select the best regression by AIC or BIC value. The longer the lag, the smaller the number of observations availabe for estimation. If the windows of regressions by lag are not ajusted, the maximum lag is usually the best lag by AIC or BIC. Theorectially, AIC and BIC from different models should be compared on the basis of the same observation numbers (Ender 2004). adjust
shows the effect of this adjustment on the estimation window. By default, the value of adjust
should be TRUE.
Return a list object of class "ciTarLag" with the following components:
path 
a data frame of model criterion values by lag, including 
out 
a data frame of the final model selection, including the values of model, maximum lag, threshold value, best lag by AIC, best lag by BIC 
Two methods are defined as follows:
print
: This shows the out
component in the returned list.
plot
:This demonstrates the trend of AIC and BIC changes of threshold cointegration regressions by lag. It facilitates the selection of the best lag for a threshold cointegration model.
Changyou Sun (cs258@msstate.edu)
Enders, W. 2004. Applied Econometric Time Series. John Wiley & Sons, Inc., New York. 480 P.
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.
ciTarFit
; and ciTarThd
;
1  # see example at daVich

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