Hi Prof. Pfeifer ,
I have a linear model with stationary variables. I evaluate it using the Bayesian method. I use the same HP filter for the data and for the model. The reviewer of my article says that the HP filter has not been used for a long time and refers me to the article “Estimation of DSGE models when the data are persistent”(2010), (Yuriy Gorodnichenko, Serena Ng ).
But as far as I understand, this article talks about filtering variables in non-linear models.
Help me please
Thank you,
Leonid.
Can you please explain how exactly you proceeded in your paper?
Hi Prof. Pfeifer ,
I worked in the standard way
For the time series of data, the trend components were removed using a one-sided Hodrick-Prescott filter. Then the linear model with stationary variables was estimated in a standard way by the Bayesian method using the obtained detrended data.
What did I do wrong?
Help me please
Thank you,
Leonid.
You did not say “one-sided” before. Using a two-sided HP filter would clearly be problematic. The one-sided one is a more defensible choice. It has for example been used in the context of Bayesian estimation in
https://doi.org/10.3982/QE1297
See also
That being said, pretty much any filtering device will be controversial as you need to (implicitly or explicitly) take a stand on the underlying DGP, where your prior may be different from the referee’s. See also
https://www.sciencedirect.com/science/article/abs/pii/S030439321400083X
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Dear, Pfeifer,
Can i use filter FD (first differences) to filter data and model variables of linear model with stationary variables. Then compare the received moments with the one-sided Hodrick-Prescott filter moments and choose the best one.
Thank you,
Leonid.