I have read the previous posts on Adolfsen et al (2007) and the reference there to 'Ramses II-Model description (2013)’. In both model, imported and domestically produced goods are aggregated using CES function. However, in the observation equation for Consumption (and Investment etc) in ‘Ramses II-Model description (2013)’, model variable plus trend technology are matched to c_obs, whereas in Adolfsen et al (2007) c_obs is matched to c_model plus technology plus some price ratios. Is anyone working on them and any idea why observation equations are different in the two papers?
I have adopted the open economy features of the Adolfsen et al (2007) in my work but I use Cobb-Douglas functional form to aggregate imported and domestic goods in total consumption and I have avoided the permanent (trend) technology for simplicity. After log-linearising the observation equation, I do not find the price ratio term, only c_model exists and is matched to c_obs. I guess the functional form of aggregation has a role in it.
Is the exogenous foreign economy VAR in that paper estimated separately as an unrestricted VAR model and then use those parameters as calibrated while doing Bayesian estimation of the DSGE model?
Hi, regarding my first question, I have found some clarification from the authors. The difference in observation equations between the two papers that I mentioned comes from the nature of the data used there, not anything related to modelling.
But I still have the other questions in mind. Should I expect large difference in result due to using Cobb-Douglas aggregation instead of CES? I think I should not, as the later is just a generalization of the former (assuming elasticity of substitution 1).
Second, instead of doing foreign VAR as in Adolfsen(2007), I can assume the process of each foreign variables as an individual AR process? I guess the choice depends on their ability to help estimating other shocks. Any discussion on those is much appreciated. Thanks.
My questions may be too naive, so apology for that.
What does the choice between calibrations or estimation of shock parameters depends on in estimated DSGE model? Especially for foreign shocks, I see they can be calibrated or estimated. For example, Adolfsen et al (2007) do not estimate the persistence and volatility of foreign output, inflation and interest rate shocks. Instead they are estimated as VAR (and standard deviation of shock is assumed 1 for IRFs?). Can I estimate them within the model just like the other shocks? I am assuming AR process for each of them, instead of VAR. In Medina and Soto (2007), these same shocks are estimated but oil price and commodity shocks are calibrated. I do not see good reasons, why is this choice?
If I calibrate, by estimating an AR(1) process, say for Ystar, get standard error of the regression as 0.064 and coefficient of lagged term 0.90 (persistence). Now when bringing them in mod file this means in shocks part, stderr is 0.064 (in percent) NOT 6.4% since I multiplied the hp-filtered Ystar by 100 before estimating AR process. Is it correct? I write: [shocks; var e_Ystar; stderr 0.064; end;]
I used unstable versions only recently and found that FEVDC are printed as output in a nice tabular way. It saves our time. Thanks for developing this.
Many thanks for clarifying this.
In my recent estimation, after extending the base model with a particular finance sector, I see that the median covariances inside posterior theoretical moments for all variables are zero. However the mean covariances are there, although over predicted. I understand something must be wrong in the way I am brining the finance sector into the model. Without seeing the mod file, can you make a guess what sort of problem does this indicate?
Are you using the unstable version? The zeros you report suggest a bug in that part of the code. The overpredicte covariances may generally just signal model misspecification.
I used version 4.4.3 when found that median covariance are all zero. I estimated in unstable version also and median covariances are there. That’s really strange. I expected it to be opposite. Variances are far from actual data and estimated measurement errors are high. I guess there are many misspecification problems I need to look for. Many thanks.