MatLab Conversion to VBA

[normanbox]

Hello group,

Ultimately I'm trying to complete a Granger Causality analysis in Excel. Because I'm only slightly familiar with VBA, I need all the help I can get. I found another post on a different forum with a MatLab code and in the comments, the person said that it will easily convert to VBA; however, I have no idea how to do it. Below is the code and if someone, anyone, could convert the code, I would be very appreciative. If the Matlab code doesn't convert, could somebody provide me with code to complete a Granger Causality analysis? As mentioned, that's all I really need to do.

Thank you in advance!

 

[ranman256]

There is no code here, so... not really sure what to do.

 

[normanbox]

I can't believe I forgot to actually post the code!

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)
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;
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)));

 

[normanbox]

I know the code is a little long, but doesn't anybody have any help they can provide? Maybe to resource so I can convert the code myself?