FMINUNC cannot continue...variable missing , although variable was included to be estimated in sub-function

I am trying to execute the following command.

      load('F:\OneDrive - University of Tasmania\Mardi Meetings\Meeting 22\Testing\MyTesting.mat');
      CP1=MyTesting;
      CP1(:,14)=[];
       CP1(:,32:36)=[];
       SAU = CP1(:,16);
       ISR = CP1(:,18);
       IRQ = CP1(:,15);
       SLK = CP1(:,10);
       NIG = CP1(:,12);
       VEN = CP1(:,13);
       CP1=[SAU ISR IRQ SLK NIG VEN];
       CPNew = log(CP1); 
      CPNew= 100*(trimr(CPNew,1,0)-trimr(CPNew,0,1));
      window_Size = 2;
      delta = (1/window_Size)*ones(1,window_Size);
      gama=1;
      filt=filter(delta,gama,CPNew);
      [nRow, nCol] = size(filt);
      windowSize=100;

          y = filt;     
          z = bsxfun(@rdivide,bsxfun(@minus,y,mean(y)),std(y));

          %disp('    Mean      Median    Max       Min       Std.dev.  Skew      Kurt')
          %disp( [ mean(y)' median(y)' max(y)' min(y)' std(y)' mean(z.^3)' mean(z.^4)'] )
          x = [ones(length(y)-2,1)   trimr(y,1,1)   trimr(y,0,2) ];
          b = x\trimr(y,2,0);

          [rcnt, ccnt] = size(b);
          mu  = trimr(b,0,rcnt-1)';  % Vector of intercepts                                              
          phi1 = trimr(b,1,ccnt)'; % Lag 1 parameter estimates   
          phi2 = trimr(b,ccnt+1,0)'; % Lag 2 parameter estimates    

           % Invert A(1) matrix needed for the MLE
          a1inv = inv(phi2);           

          bstart = [
                      0.001855486206481
                      0.068693371469487
                      0.023880592273368
                      0.001989809184491
                      0.013648672676092
                      0.000281803828058
                     -0.121999669995451
                     -0.002888991832387
                      0.002650363820455
                      0.217906436715095
                      0.001195294344330
                      0.008613191798914
                      0.175400639477836
                      1.103000246244063
                      0.440791629588056
                      0.495605700916872
                      0.365097267442524
                     -0.011572135693424
                      0.650892298805934
                     -1.176100342250688
                     -0.342100219212525
                     -1.059396487450620
                     -0.545902063123713
                     -0.729900334997059
                     -0.402000678739595
                      1.013901399027622
                     -1.601300249119193
                      1.018606365787122
                      0.791195906556810
                      0.818998254746848
                      0.338395871264844
                      0.465287945621782
                     -1.145684521466255
                  ];
      % bstart = rand(1,33);
        options = optimset(   'Display',              'iter', ...
                              'MaxIter',              20000,   ...
                              'MaxFunEvals',          20000,   ...
                              'TolX',                 1e-8,   ...
                              'TolFun',               1e-4  );       

          [ b,fval ]  = fminunc(@(b) neglogl(b,v),bstart,options );     

          disp(' Log-likelihood function ');
          disp( fval );
          disp( b );

      %
      %--------------------------- Functions ----------------------------------
      % 
      %--------------------------------------------------------------------------
      % Wrapper function for log-liklihood
      %--------------------------------------------------------------------------
      function lf = neglogl( b,v )

          lf = - mean( loglt( b,v ) );
      end

      %--------------------------------------------------------------------------
      % Log-likelihood function for SVAR
      %--------------------------------------------------------------------------

      function lf = loglt( b,v )

          % Unpack parameter vector
          delta = b(16:21).^2;
          alpha = normcdf( b(22:27) );
          beta  = normcdf( b(28:33) );

          [ t,n ] = size( v );
          lf      = zeros( t,1 );

          binv  = [   1        0      0       0     0     0  
                      b(1)     1      0       0     0     0
                      b(2)   b(6)     1       0     0     0  
                      b(3)   b(7)   b(10)     1     0     0  
                      b(4)   b(8)   b(11)  b(13)    1     0  
                      b(5)   b(9)   b(12)  b(14)  b(15)   1  ];

          u = ( inv(binv)*v' )';

          for i = 1:t;

              if i == 1;

                  d = delta + alpha.*(std(u).^2)' + beta.*(std(u).^2)';
              else

                  d = delta + alpha.*(u(i-1,:).^2)' + beta.*d;
              end

              omega = binv*diag(d)*binv';

              lf(i) = -0.5*n*log(2*pi) - 0.5*log(det(omega)) - 0.5*v(i,:)*inv(omega)*v(i,:)';
          end

      end
      %--------------------------------------------------------------------------
      %   Return matrix to compute time-varying covariances
      %--------------------------------------------------------------------------
      function S = getSmatrix( b,v )

          % Unpack parameter vector
          delta = b(16:21).^2;
          alpha = normcdf( b(22:27) );
          beta  = normcdf( b(28:33) );

          [ t,n ] = size( v );
          lf      = zeros( t,1 );

          binv  = [   1        0      0       0     0     0  
                      b(1)     1      0       0     0     0
                      b(2)   b(6)     1       0     0     0  
                      b(3)   b(7)   b(10)     1     0     0  
                      b(4)   b(8)   b(11)  b(13)    1     0  
                      b(5)   b(9)   b(12)  b(14)  b(15)   1  ];

          u = (inv(binv)*v')';

          for i = 1:t;

              if i == 1;

                  d = delta + alpha.*(std(u).^2)' + beta.*(std(u).^2)';
              else

                  d = delta + alpha.*(u(i-1,:).^2)' + beta.*d;
              end

              tmp    = binv*diag( sqrt(d) ); 

              S(i,:) = vec( tmp )';

          end

      end

      %--------------------------------------------------------------------------
      % Reshape a matrix to agree with GAUSS reshape command
      %--------------------------------------------------------------------------
      function X = reshapeg(Y,r,c)

               tmp = reshape(Y',c,r);
               X   = tmp';

      end
Getting error"

Undefined function or variable 'v'.

Error in GHVD2C>@(b)neglogl(b,v)

Error in fminunc (line 292)
        f = feval(funfcn{3},x,varargin{:});

Error in GHVD2C (line 82)
    [ b,fval ]  = fminunc(@(b) neglogl(b,v),bstart,options );

Caused by:
    Failure in initial objective function evaluation. FMINUNC cannot continue."
How to fix this?

[Stephan]

Where exactly do you define 'v' ? Is it in the .mat-file you didnt give to us?

[Raisul Islam]

Did not define v anywhere. How to define v?

Answer 1

[Stephan]

Hi,

you use v as an input to your neglogl function. There should be at least one line in your code that associates v with a value, a vector, a matrix... (Since v' is multiplied to a matrix, i think it is a vector.)

This is what the error message is telling you.

Since i have no insight in the details of your problem, i can not say how to define v in detail.

I recommend to find out the meaning of v, and define it or get it from another function or .mat-file.

Best regards

Stephan