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Hello,

I am working with EViews 13, coding to simulate dynamic effects from an ARDL model. When I incorporate the ARDL equation into an EViews model to perform stochastic simulations similar to those I do with a VAR model, the trajectories for the median and the confidence interval bounds in the ARDL model are identical, without variation. With a VAR, I receive appropriate confidence intervals through stochastic simulations. Is there a specific configuration or option that I should adjust to obtain valid confidence intervals in ARDL simulations?

Thank you for your help!

Could you provide an example?

To be more precise, there are no differences in the scenario gaps (see the last chart from my sample program.

Hello,

I believe you're seeing correct behavior. The only difference between the two scenarios is a one unit shock to exogenous variable X1 on date 2019m01. Given that the model is entirely linear, any difference between variable trajectories in the two scenarios will be a function of solely your explicit shock, not the random shocks simulated at every repetition. The individual trajectories do vary by repetition of course, as summarized by the Y_0[HLM] and Y_SC1[HLM] series. However, since the same sequence of random shocks is used in both scenarios within a repetition, and the model is linear, in the difference those random shocks effectively cancel out. There will be no variation between repetitions, as summarized by the Y_SC1_0[HLM] series. This is basically the same idea behind VAR IRFs, where only a single round of calculations are needed since the residuals don't matter.

Hello Matt,
Thank you for the clarifications. Indeed, in a linear model, when the residuals are identical across scenarios (the EViews procedure), it's normal not to see variation in the simulated gaps. Perhaps a feature to vary residuals could be added in a future update. In the meantime, I could consider trying to code a bootstrapping procedure for this purpose. IMPORTANTLY: I've noticed the option that allows for integrating statistical uncertainty on coefficients (ardl_mod.stochastic(c=t)). It's not the uncertainty on the residuals, but it's just as important, and in my case, it does yield confidence intervals.