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You are misrepresenting what I said. To estimate model parameters using Bayesian techniques, you must not use two-sided filters. However, when comparing moments from the estimated model to the data, you are not restricted by such considerations. You just need to be consistent in comparing processed data to the same object from the model. The particular processing choice is up to you. The most common one is to compute growth rates in the data to growth rates from the model or HP-filtered data to HP-filtered variables from the model.

Sidenote: the filtered_variables command does not conduct filtering of the data, but provides you with one-step ahead forecasts.

If you are willing to not consider the full posterior distribution, you can use

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stoch_simul(order=1, hp_filter=1600);

after estimation and compare the moments to HP-filtered data.

jpfeifer wrote:You are misrepresenting what I said. To estimate model parameters using Bayesian techniques, you must not use two-sided filters. However, when comparing moments from the estimated model to the data, you are not restricted by such considerations. You just need to be consistent in comparing processed data to the same object from the model. The particular processing choice is up to you. The most common one is to compute growth rates in the data to growth rates from the model or HP-filtered data to HP-filtered variables from the model.

Sidenote: the filtered_variables command does not conduct filtering of the data, but provides you with one-step ahead forecasts.

If you are willing to not consider the full posterior distribution, you can use

Code: Select all

stoch_simul(order=1, hp_filter=1600);

after estimation and compare the moments to HP-filtered data.

Many thanks. If I understand you correctly, firstly I use one-sided hp filtered data to estimate the model, then use

Code: Select all

stoch_simul(order=1, hp_filter=1600);

after estimation and compare the model moments(presented in the command window) to the moments of two-side HP data ,instead of moments of one_sided hp data?

I would usually prefer using first differences for estimating the model, but what you describe should work. But given that you want to use the one-sided HP-filter for estimation, there is no reason for not using the one-sided HP-filter for the moment comparison.

jpfeifer wrote:1. This refers to the fact that the endogenous prior option updates the normal prior you specify in the estimated_params-block using the data moments. For that reason you need to have both a full estimated_params-block and

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estimation(endogenous_prior, ...

2. There is no general rule to this. If you consider the high variance share of the measurement error a priori as unlikely, I would use an informative prior for it. Do not use an upper bound but use e.g. an inverse gamma distribution for the measurement error.

Dear Johannes,

If using inverse gamma distribution for the measurement error, and the estimation results show this measurement error account for 40% variance of observable variable(there is highly misspecification in the model actually). Is it too large to be acceptable ?

Many thanks,
Huan

Hard to say. You should look at standard deviations, which usually have a more natural interpretation. But to me this looks too large.