Converting Matlab Code to Vba

[Hamond]

Hi,

I have some matlab modelling code that I want to convert into VBA so I can do the analysis in excel. Does anyone know a website that I could potentially post it to?

Thanks,

Hamong

[Kenneth Hobs]

I guess that we would need to see the code to give advise. Obviously, if you use matlab dll's then others need them as well unless you want to skip matlab altogether.

[Hamond]

Hi Kenneth,

I don't have access to Matlab at all so I would want to do the analysis soley usinf Excel/VBA. The matlab code is below and been told it should be translatable into VBA:

Rgds,

Hamond

function [F,c_v] = granger_cause(x,y,alpha,max_lag)
% [F,c_v] = granger_cause(x,y,alpha,max_lag)
% Granger Causality test
% Does Y Granger Cause X?
%
% User-Specified Inputs:
% x -- A column vector of data
% y -- A column vector of data
% alpha -- the significance level specified by the user
% max_lag -- the maximum number of lags to be considered
% User-requested Output:
% F -- The value of the F-statistic
% c_v -- The critical value from the F-distribution
%
% The lag length selection is chosen using the Bayesian information
% Criterion
% Note that if F > c_v we reject the null hypothesis that y does not
% Granger Cause x

% Chandler Lutz, UCR 2009
% Questions/Comments: chandler.lutz@email.ucr.edu
% $Revision: 1.0.0 $ $Date: 09/30/2009 $
% $Revision: 1.0.1 $ $Date: 10/20/2009 $

% References:
% [1] Granger, C.W.J., 1969. "Investigating causal relations by econometric
% models and cross-spectral methods". Econometrica 37 (3), 424?438.

%Make sure x & y are the same length
if (length(x) ~= length(y))
error('x and y must be the same length');
end

%Make sure x is a column vector
[a,b] = size(x);
if (b>a)
%x is a row vector -- fix this
x = x';
end

%Make sure y is a column vector
[a,b] = size(y);
if (b>a)
%y is a row vector -- fix this
y = y';
end



%Make sure max_lag is >= 1
if max_lag < 1
error('max_lag must be greater than or equal to one');
end

%First find the proper model specification using the Bayesian Information
%Criterion for the number of lags of x

T = length(x);

BIC = zeros(max_lag,1);

%Specify a matrix for the restricted RSS
RSS_R = zeros(max_lag,1);

i = 1;
while i <= max_lag
ystar = x(i+1:T,:);
xstar = [ones(T-i,1) zeros(T-i,i)];
%Populate the xstar matrix with the corresponding vectors of lags
j = 1;
while j <= i
xstar(:,j+1) = x(i+1-j:T-j);
j = j+1;
end
%Apply the regress function. b = betahat, bint corresponds to the 95%
%confidence intervals for the regression coefficients and r = residuals
[b,bint,r] = regress(ystar,xstar);

%Find the bayesian information criterion
BIC(i,:) = T*log(r'*r/T) + (i+1)*log(T);

%Put the restricted residual sum of squares in the RSS_R vector
RSS_R(i,:) = r'*r;

i = i+1;

end

x_lag = find(min(BIC));

%First find the proper model specification using the Bayesian Information
%Criterion for the number of lags of y

BIC = zeros(max_lag,1);

%Specify a matrix for the unrestricted RSS
RSS_U = zeros(max_lag,1);

i = 1;
while i <= max_lag

ystar = x(i+x_lag+1:T,:);
xstar = [ones(T-(i+x_lag),1) zeros(T-(i+x_lag),x_lag+i)];
%Populate the xstar matrix with the corresponding vectors of lags of x
j = 1;
while j <= x_lag
xstar(:,j+1) = x(i+x_lag+1-j:T-j,:);
j = j+1;
end
%Populate the xstar matrix with the corresponding vectors of lags of y
j = 1;
while j <= i
xstar(:,x_lag+j+1) = y(i+x_lag+1-j:T-j,:);
j = j+1;
end
%Apply the regress function. b = betahat, bint corresponds to the 95%
%confidence intervals for the regression coefficients and r = residuals
[b,bint,r] = regress(ystar,xstar);

%Find the bayesian information criterion
BIC(i,:) = T*log(r'*r/T) + (i+1)*log(T);

RSS_U(i,:) = r'*r;

i = i+1;

end

y_lag = find(min(BIC));

%The numerator of the F-statistic
F_num = ((RSS_R(x_lag,:) - RSS_U(y_lag,:))/y_lag);

%The denominator of the F-statistic
F_den = RSS_U(y_lag,:)/(T-(x_lag+y_lag+1));

%The F-Statistic
F = F_num/F_den;

c_v = finv(1-alpha,y_lag,(T-(x_lag+y_lag+1)));