Dear Johannes,
Could I ask you, in Bayesian estimation, if parameters to be estimated appear in measurement equation, would that be any problem?
Thanks in advance.
Kind regards,
Huan
Dear Johannes,
Could I ask you, in Bayesian estimation, if parameters to be estimated appear in measurement equation, would that be any problem?
Thanks in advance.
Kind regards,
Huan
Dear Huan,
why should that be a problem? A case like this occurs whenever you want to estimate the mean growth rate in an observation equation using first differences.
[quote=“jpfeifer”]Dear Huan,
why should that be a problem? A case like this occurs whenever you want to estimate the mean growth rate in an observation equation using first differences.[/quote]
Many thanks Johannes.
In my model , I have financial friction but do Not have a good corresponding financial data. If I use linear detrended interest rate spread data as an “instrument” , and set a new parameter measuring the volatility difference between data and model variable in measurement equation , maybe also put an measurement error there, shown as belowinterest rate spread_obs= (constant x )* model financial friction variable + measurement error
,
(model is log-linearized)
Then estimate the constant x (only appears in measurement equation) and standard deviation of measurement error, would that be a problem?
Kind regards,
Huan
This is hard to tell. It looks good to me. But to be on the safe side, I would test identification, if you add measurement error.
Dear Johannes,
Happy new year!
Could I ask further about testing identification?
identification;
after estimation?Kind regards,
Huan
varobs
statement and an estimated_params
block