remove files of the form *.old

MatlabFiles/ERRORS.OLD

-function [vd,str,imf] = errors(Bh,swish,nn)
-% Computing variance decompositions and impulse functions with
-%                [vd,str,imf] = errors(Bh,swish,nn)
-%   where imf and vd is of the same format as in RATS, that is to say:
-%                Column: nvar responses to 1st shock, 
-%                            nvar responses to 2nd shock, and so on.  
-%                Row:  steps of impulse responses. 
-%         vd:  variance decompositions
-%         str: standard errors of each variable, steps-by-nvar
-%         imf: impulse response functions
-%         Bh is the estimated reduced form coefficient in the form 
-%              Y(T*nvar) = XB + U, X: T*k, B: k*nvar.  The matrix
-%              form or dimension is the same as "Bh" from the function "sye";
-%         swish is the inv(A0) in the structural model A(L)y(t) = e(t).
-%         nn is the numbers of inputs [nvar,lags,# of impulse responses].
-
-nvar = nn(1);
-lags = nn(2);
-imstep = nn(3);   % number of steps for impulse responses
-
-Ah = Bh';        
-% Row: nvar equations 
-% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
-
-imf = zeros(imstep,nvar*nvar);        
-vd = imf;
-% Column: nvar responses to 1st shock, nvar responses to 2nd shock, and so on.  
-% Row:  steps of impulse responses. 
-str = zeros(imstep,nvar);    % initializing standard errors of each equation
-M = zeros(nvar*(lags+1),nvar);
-% Stack M0;M1;M2;...;Mlags
-M(1:nvar,:) = swish;
-Mtem = M(1:nvar,:);    % temporary M -- impulse responses.  
-%
-Mvd = Mtem.^2;     % saved for the cumulative sum later
-Mvds = (sum(Mvd'))';
-str(1,:) = sqrt(Mvds');    % standard errors of each equation
-Mvds = Mvds(:,ones(size(Mvds,1),1));
-Mvdtem = (100.0*Mvd) ./ Mvds;     % tempoary Mvd -- variance decompositions
-% first or initial responses to 
-%            one standard deviation shock (or forecast errors).  
-%   Row: responses; Column: shocks
-%
-% * put in the form of "imf"
-imf(1,:) = Mtem(:)';
-vd(1,:) = Mvdtem(:)';
-
-t = 1;
-ims1 = min([imstep-1 lags]);
-while t <= ims1
-   Mtem = zeros(nvar,nvar);
-   for k = 1:t
-      Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(t-k)+1:nvar*(t-k+1),:) + Mtem;
-      % Row: nvar equations, each for the nvar variables at tth lag
-   end
-   % ** impulse response functions
-   M(nvar*t+1:nvar*(t+1),:) = Mtem;
-   imf(t+1,:) = Mtem(:)';   
-   % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.  
-   % ** variance decompositions
-   Mvd = Mvd + Mtem.^2;         % saved for the cumulative sum later
-   Mvds = (sum(Mvd'))';
-   str(t+1,:) = sqrt(Mvds');    % standard errors of each equation
-   Mvds = Mvds(:,ones(size(Mvds,1),1));
-   Mvdtem = (100.0*Mvd) ./ Mvds;   % tempoary Mvd -- variance decompositions
-   vd(t+1,:) = Mvdtem(:)';   
-   % stack vd with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.  
-   t= t+1;
-end
-
-for t = lags+1:imstep-1
-   M(1:nvar*lags,:) = M(nvar+1:nvar*(lags+1),:);
-   Mtem = zeros(nvar,nvar);
-   for k = 1:lags
-      Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(lags-k)+1:nvar*(lags-k+1),:) + Mtem;
-      % Row: nvar equations, each for the nvar variables at tth lag
-   end
-   % ** impulse response functions
-   M(nvar*lags+1:nvar*(lags+1),:) = Mtem;
-   imf(t+1,:) = Mtem(:)';   
-   % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
-   % ** variance decompositions
-   Mvd = Mvd + Mtem.^2;         % saved for the cumulative sum later
-   Mvds = (sum(Mvd'))';
-   str(t+1,:) = sqrt(Mvds');    % standard errors of each equation
-   Mvds = Mvds(:,ones(size(Mvds,1),1));
-   Mvdtem = (100.0*Mvd) ./ Mvds;   % tempoary Mvd -- variance decompositions
-   vd(t+1,:) = Mvdtem(:)';   
-   % stack vd with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.  
-end
-
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MatlabFiles/IMPULSE.OLD

-function imf = impulse(Bh,swish,nn)
-% Computing impulse functions with
-%                imf = impulse(Bh,swish,nn)
-%   where imf is in a format that is the SAME as in RATS.
-%                Column: nvar responses to 1st shock, 
-%                            nvar responses to 2nd shock, and so on.  
-%                Row:  steps of impulse responses. 
-%         Bh is the estimated reduced form coefficient in the form 
-%              Y(T*nvar) = XB + U, X: T*k, B: k*nvar.  The matrix
-%              form or dimension is the same as "Bh" from the function "sye";
-%         swish is the inv(A0) in the structural model A(L)y(t) = e(t).
-%         nn is the numbers of inputs [nvar,lags,# of impulse responses].
-
-nvar = nn(1);
-lags = nn(2);
-imstep = nn(3);   % number of steps for impulse responses
-
-Ah = Bh';        
-% Row: nvar equations 
-% Column: 1st lag (with nvar variables) to lags (with nvar variables) + const = k.
-
-imf = zeros(imstep,nvar*nvar);        
-% Column: nvar responses to 1st shock, nvar responses to 2nd shock, and so on.  
-% Row:  steps of impulse responses. 
-M = zeros(nvar*(lags+1),nvar);
-% Stack M0;M1;M2;...;Mlags
-M(1:nvar,:) = swish;
-Mtem = M(1:nvar,:);    % temporary M.  
-% first (initial) responses to 1 standard deviation shock.  Row: responses; Column: shocks
-% * put in the form of "imf"
-imf(1,:) = Mtem(:)';
-
-t = 1;
-ims1 = min([imstep-1 lags]);
-while t <= ims1
-   Mtem = zeros(nvar,nvar);
-   for k = 1:t
-      Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(t-k)+1:nvar*(t-k+1),:) + Mtem;
-      % Row: nvar equations, each for the nvar variables at tth lag
-   end
-   M(nvar*t+1:nvar*(t+1),:) = Mtem;
-   imf(t+1,:) = Mtem(:)';   
-   % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.  
-   t= t+1;
-end
-
-for t = lags+1:imstep-1
-   M(1:nvar*lags,:) = M(nvar+1:nvar*(lags+1),:);
-   Mtem = zeros(nvar,nvar);
-   for k = 1:lags
-      Mtem = Ah(:,nvar*(k-1)+1:nvar*k)*M(nvar*(lags-k)+1:nvar*(lags-k+1),:) + Mtem;
-      % Row: nvar equations, each for the nvar variables at tth lag
-   end
-   M(nvar*lags+1:nvar*(lags+1),:) = Mtem;
-   imf(t+1,:) = Mtem(:)';   
-   % stack imf with each step, Row: 6 var to 1st shock, 6 var to 2nd shock, etc.
-end
-
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