In my previous try on ARDL cointegrating bounds using Microfit here, Eviews here and here, and using STATA here. The comments and suggestions I received for them were very helpful. With my current experience, I would recommend using Microfit or Eviews for ARDL, but one must be cautious with calculation glitches when they are using the crack version of Eviews.
This blog is illustrating the Non-linear ARDL cointegrating bounds which is also called Asymmetric Effects ARDL (NARDL) proposed by (Shin, Yu & Greenwood-Nimmo, 2014). The idea behind this model is questioning the standard assumption of symmetric estimates, by which the effect of increasing of a variable is equal and opposite to the decreasing of the same variable. There are few cases mentioned in the above study like creation and destruction of jobs in boom and recession.
In the example below the nardl_data is unemployment (dependent variable) and industrial production index (independent variable). You can import this data into Stata by simply copying and pasting in data editor (tutorial).
Once imported, you have to indicate Stata that data is time series for this following command is used
This way all the time series command will become functional. In order to estimate the NARDL following files must be downloaded, uncompressed, and paste Stata/ado/base/n folder where ever it is installed, it will then work in Stata. Following is the command
In the command below p() and q() are the number of lags of dependent and independent variable used. You can identify optimal lag by using ‘varsoc’ command in Stata, illustrated here.
nardl un ip, p(2) q(2)
Above table is standard one step ECM, the first coefficient is the convergence coefficient. and x1 is the first independent variable where x1p is the increasing portion of x1 and x1n is the decreasing portion of x1.
Below is the F bounds test, here it is 2.22, its critical values are same as the simple ARDL cointegrating bounds. Can be seen from the following paper. Currently, it is smaller than critical values.
Below table shows the long run increasing and decreasing effect of independent variable on the dependent variable. When the independent variable increases it decreases unemployment by 14.71% but when independent variable decreases, it increases unemployment by 48.69%.
After estimating the model, there are four types of diagnostics reported, since all of them are insignificant, so there is no autocorrelation, heteroscedasticity, misspecification and non-normality respectively.
We can also generate the graph by adding the ‘plot’ option in command and further confidence interval by using bootstrap and level option. The horizon option will identify how many years the graph will be constructed.
nardl un ip, p(2) q(4) plot horizon(40) bootstrap(100) level(95)
in the above figure, we can see that decrease in IP(industrial production) has a positive effect on UN(unemployment) shown by red line. While increasing IP has a temporary negative effect on UN shown by the green line. And the blue line showing the increasing trend of asymmetry with time.
Your comments and suggestions are welcome.