Measuring the Effects of a Shock to Monetary Policy - Humboldt ...

94 Bayesian FAVARs with Agnostic Identification
F=F*ql;
% need identity in the first K columns of Lf, call them A for now
A=lam_f(:,1:input.specification.model.K);
lam_f=[eye(input.specification.model.K),inv(A)*lam_f(:,...
(input.specification.model.K+1):input.specification.dim.N)]’;
F=F*A;
% obtain R:
e=X_st-Y_st*lam_y’-F*lam_f’;
R=e’*e ./ input.specification.dim.T;
R=diag(diag(R));
% run a VAR in [F,Y], obtain initial B and Q
[Phi_lags,Bc,v,Q,invFYFY]=estvar([F,input.specification.y],input.specification.model.d,[]);
%----------------------------------------------------------------------------------------%
startingvalues.F = F;
startingvalues.lam_f = lam_f;
startingvalues.lam_y = lam_y;
startingvalues.R = R;
startingvalues.Phi_lags = Phi_lags;
startingvalues.Q = Q;
%----------------------------------------------------------------------------------------%
%%%%%%**********************************************************%%%%%
%%%%%% Bayesian FAVAR Code August 26th %%%%%
%%%%%%**********************************************************%%%%%
%%%%%% DO_CALCULATION_SETMODEL %%%%%
%%%%%% see Sequence Diagram Block B.1 %%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%% Initializes calculation.stateSpaceStructure
%%%%%%
%%%%%% stateSpaceStructure.XX = XX;
%%%%%% stateSpaceStructure.Lam = Lam;
%%%%%% stateSpaceStructure.Xsi_in = Xsi_in;
%%%%%% stateSpaceStructure.P_in = P_in;
%%%%%% stateSpaceStructure.Phi_bar = Phi_bar;
%%%%%% stateSpaceStructure.QQ_bar = QQ_bar;
%%%%%% stateSpaceStructure.RR = RR;
%%%%%% stateSpaceStructure.F_bar = F_bar;
function DO_CALCULATION_SETMODEL (input)
%function DO_CALCULATION_SETMODEL (input)
global calculation;
specM = input.specification.dim.M;
specK = input.specification.model.K;
specd = input.specification.model.d;
specT = input.specification.dim.T;
XX = [input.xdata, input.specification.y];
FF = [input.startingvalues.F, input.specification.y];
% initialize Factors its covarianvce matrix for Bayesian Kalman Filter & Smoother
F_bar = [FF zeros(specT,((specd-1)*(specK+specM)))];
Xsi_in = zeros((specK+specM)*specd,1);
P_in = eye((specK+specM)*specd); % for Kalman Filter&Smoother
Lam = [input.startingvalues.lam_f input.startingvalues.lam_y; zeros(specM,specK) eye(specM)];
Lam_bar = [Lam zeros((input.specification.dim.N+specM),((specd-1)*(specK+specM)))];