Replication of FRBNY DSGE model (Del Negro et al. 2013)
Posted: Fri May 08, 2015 1:39 pm
Hi everyone!
I am trying to replicate with Dynare the DSGE of the NY Fed, as published in Del Negro et al. (2013)(http://www.econstor.eu/bitstream/10419/93628/1/771940254.pdf). I am referring to the loglinearised version of the model, so in the model block I use the option 'linear'. For the parameters values and the steady state quantities I am using the information in the Matlab code the authors published (the code doesn't run properly with the recent versions of Matlab apparently): some parameters come from an optimisation routine therefore. At first, I tried to play with the parameters but the only way to get around this issue was to change the time index of capital (kbar) in two equations so to turn one equation into a forward looking one (so, 9 eigenvalues >1 and 9 eqs).
At this point, the problem is the results of the IRFs, that turn out to be clearly wrong. Could you help me skimming through the code to check for some errors I cannot see?
I also tried to go on with the Bayesian estimation and get the following error: "Warning: Matrix is close to singular or badly scaled, results may be inaccurate" during the MCMC iterations.
Thank you!
Chiara
I am trying to replicate with Dynare the DSGE of the NY Fed, as published in Del Negro et al. (2013)(http://www.econstor.eu/bitstream/10419/93628/1/771940254.pdf). I am referring to the loglinearised version of the model, so in the model block I use the option 'linear'. For the parameters values and the steady state quantities I am using the information in the Matlab code the authors published (the code doesn't run properly with the recent versions of Matlab apparently): some parameters come from an optimisation routine therefore. At first, I tried to play with the parameters but the only way to get around this issue was to change the time index of capital (kbar) in two equations so to turn one equation into a forward looking one (so, 9 eigenvalues >1 and 9 eqs).
At this point, the problem is the results of the IRFs, that turn out to be clearly wrong. Could you help me skimming through the code to check for some errors I cannot see?
I also tried to go on with the Bayesian estimation and get the following error: "Warning: Matrix is close to singular or badly scaled, results may be inaccurate" during the MCMC iterations.
Thank you!
Chiara