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Estimating DSGE Models with Forward Guidance

Topics: Kalman Filtering, Models
Nov 21, 2016 at 6:04 AM
Edited Jan 3 at 8:58 PM
I am replicating FRBNY model in IRIS and, in order to implement zero lower bound, I need to estimate the time-varying standard deviation of anticipated monetary policy shock in two samples: before and after the financial crisis. Following Jaromir's simple SPBC tutorial on running Kalman filter with time varying standard deviations of some shocks (more_on_kalman_filter.m), I created J database where I set the anticipated monetary policy shock to zero before 2008q4 and to 0.2 after that. But since this is hard coded as input to 'vary=' filter option to estimate command, I can not estimate this time varying parameter. Would it be possible for IRIS estimation algorithm to use time-varying dummy so that the standard deviation of some shock is not fixed?

The files are available at

The model requires my modified IRIS fork:

Thank you,
Feb 15 at 7:28 PM
I found how to solve this problem by setting standard deviation to zero in J database to 'vary=' option for the entire period until 2008q3:
J = struct;
for v=sprintfc('std_rm_sh%d',1:o.nant)
filterOpt = {'relative=',false,'objRange=',startHist+2:endHist,'vary=',J};
optimSet = {'MaxFunEvals=',10000,'TolFun=',1e-16};
[est,pos,C,H,mest] = estimate(m,d,startHist:endHist,E,'filter=',filterOpt,'optimSet=',optimSet,'sstate=',true,'nosolution=','penalty');
Marked as answer by ikarib on 2/15/2017 at 11:29 AM