Initial value of the log posterior (or likelihood)

[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”