Thanks! Another question, are the lecture slides from your course that you referred to in this topic available somewhere online? Or is it possible to get them from you…?
No. If you send me an email, I can send the respective chapter to you.
@AS90: You need to be more precise. What are
Are the IRFs to temporary shocks estimated on non-stationary data? Or are the IRFs non-stationary, i.e. they show permanent effects of a temporary shock?
Thanks, sorry for the hiatus.
Yes, the first one you wrote. Non-stationary data IRFs: the IRFs are estimated on non-stationary data.
Hence, is one to de-trend his DSGE model anyways if willing on reproducing said IRFs theoretically?
In that case, you have to work with a detrended model and make sure the data are correctly matched to the stationary model variables. For example, you can use an observation equation with first differences. See Pfeifer(2013): “A Guide to Specifying Observation Equations for the Estimation of DSGE Models”.
Thank you. That paper is very useful, you had already notified it to me. But, right because of the indications therein, what I asked was: what if you do not want to render the data stationary? That is, what if you wish to reproduce IRFs estimated on non-stationary data: should you still de-trend your DSGE model or not? By logical rigour you shouldn’t, right?
In a sense, the data is still non-stationary if you use first differences as you are using an invertible transformation. But if you follow Canova’s most recent arguments, you should not be treating your data. However, that massively restricts what you can do in estimation. The reason you need to stationarize your model is that you need to have a well-defined approximation point for log-linearization/Taylor approximation. Thus, you have to use a mapping of the non-stationary data to the stationary model. If you find a way to use a non-stationary model, you might be able to get around this, but I am not aware of any decent way to achieve this.
True and elucidating. Thanks.
Is this also correct when the model is nonlinear?or we should compare the HP-filtered data moments with the HP-filtered theoretical moments.
thanks
When i use HP filter command the computed(THEORETICAL) mean of one of the variables will be equal to zero and the others have negative values(-0.1609, -0.2561, -0.1609 , 0.0000) .Could this be correct?
thanks
Please see
Thank you dear professor
The truth is, I’m still a beginner. would you please take a look at my file and tell me what i should to do?
thanks.
nkcc.mod (8.3 KB) .
What is the problem you are experiencing?
I added log of some variables and hp filter command to the code but the values of mean for some of them is not zero.What’s wrong with the code?
thanks
nkcc.mod (8.3 KB)
The mean reported by Dynare is for the unfiltered variables. For the filtered ones, it is trivially 0.
1 Like