Theoretical Questions Related to Bayesian Estimation


#1

The purpose of this thread is to (hopefully) compile in one place all theory related questions with respect to Bayesian Estimation. Dynare does a lot of the work in the background, which is both convenient and dangerous. Hence, if you have a theory related question with respect to your work please post it here.

Here are a couple of general references to start with:

Chapter 9 of the Handbook of Macroeconomics: Solution and Estimation Methods for DSGE Models by Fernandez-Villaverde, Rubio-Ramirez and Schorfheide

“The Econometrics of DSGE Models” by Fernandez-Villaverde


#2

I have a specific question related to the choice of observable variables to use and how to pick the additional shocks in order to identify them. For instance, I have a New Keynesian model with financial frictions and have the same 7 standard observables (and shocks) as in Smets and Wouter (2007). I also have data on 2 financial variables and would also like to include data on bond holdings. (Bonds are not neutral in my model) .However, I am not sure if that would make sense within the scope of the estimation. Also, how do we generally choose shocks in a model?

Thanks


#3

Generally, there is no good guidance. It is mostly trial and error. See also the section on selecting observables in Pfeifer(2013): “A Guide to Specifying Observation Equations for the Estimation of DSGE Models”


#4

Thanks Johannes. I wasn’t aware you had written such an extensive guide yourself. Another paper I found with respect to choosing variables is Canova et al. (2014)


#5

On a separate note, suppose one wants to introduce a new observable into a model. How does one also introduce the additional shock into the system? Should the shock be related somehow to the observable (or what might be affecting it)?


#6

Usually, theory will guide you. It is a matter of debate whether full information estimation is sensible if you need to add a new structural shock for each variable you want to consider. One obvious way out is to assume measurement error.