Initial value of the log posterior (or likelihood)

[seddiki]

Greetings to all researchers and professors
Initial value of the log posterior (or likelihood): -192299404.6211
Is it possible to explain this value, is it useful to be large or small, and what is its meaning?
Please kindly advise

[jpfeifer]

No, values that large typically indicate a problem like a wrong observation equation.

[vaheed]

hi dear professor,
i have the same issue my codes calculate all the graph but at the end i have this :Initial value of the log posterior (or likelihood): -Inf
Total computing time : 0h00m13s
Note: warning(s) encountered in MATLAB/Octave code

data.mat (2.2 KB)
Newcodes.mod (10.1 KB)
can you please help ?

[jpfeifer]

1. Your model features stochastic singularity as you are observing all components of the Taylor rule.
2. Your data is still trending. You need to properly define an observation equation and handle the trend.

[vaheed]

can you explain a little bit more, please?

[jpfeifer]

1. See e.g.

Issue with the Kalman filter

The problem is known as “stochastic singularity”, which happens if a linearized model implies an exact linear combination between observables. That in turn means that the density of observing that variables is either 1 (the exact linear combination in the model holds in the data; rarely the case) or 0 (the exact linear combination does not hold; the typical case). As you can imagine with a log density of minus infinity, estimation will not work. You have in your model exp(y_tilde) = exp(k_tild…

2. See e.g.

Errors in Bayesian estimation(matrix must be a positive definite)

Your observation equation is wrong. You are trying to match trending data to stationary model variables. Please read Pfeifer(2013): “A Guide to Specifying Observation Equations for the Estimation of DSGE Models”