Numgrad m - Revision history

Rb at 08:36, 8 February 2022

2022-02-08T08:36:47Z

Rb at 08:36, 8 February 2022

2022-02-08T08:36:06Z

Rb: Created page with " %------------------------------------------------------------------------- % Computes numerical gradient at each observation %-------------------------------------..."

2022-02-08T08:35:21Z

Created page with "<source> %------------------------------------------------------------------------- % Computes numerical gradient at each observation %-------------------------------------..."

New page

<source>
%-------------------------------------------------------------------------
% Computes numerical gradient at each observation
%-------------------------------------------------------------------------
function G = numgrad( f,x,varargin )

f0 = feval( f,x,varargin{:} ); % n by 1
n = length( f0 );
k = length( x );
fdf = zeros( n,k );

% Compute step size
dx = sqrt( eps )*( abs( x ) + eps );
xh = x + dx;
dx = xh - x;
ind = dx < sqrt(eps);
dx(ind) = sqrt(eps);

% Compute gradient
xdx = bsxfun( @plus, diag( dx ), x );
for i=1:k;

fdf(:,i) = feval( f, xdx(:,i), varargin{:} );
end

G0 = repmat( f0, 1, k ); % n by k
G1 = repmat( dx', n, 1 );
G = ( fdf-G0 )./G1;

end

<\source>

← Older revision		Revision as of 08:36, 8 February 2022
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		+	Save the code below as numgrad.m in your working folder. This is required to make the GMM code work.
		+
	<source>		<source>
	%-------------------------------------------------------------------------		%-------------------------------------------------------------------------

← Older revision		Revision as of 08:36, 8 February 2022
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	end		end

−	<\source>	+	</source>