As you probably know, gretl supports matrix "left division" (A \ B) and "right division" (A / B). I'll take left division as my example here but everything applies to right division mutatis mutandis. If A is square, A \ B gives the matrix X that solves AX = B, but if A has more rows than columns it gives the least squares solution. So if you just want the coefficients from an OLS regression you can do betahat = X \ y rather than using mols(), if you wish. The result has good accuracy; it uses QR decomposition via lapack. So far so good, but I recently noticed that it doesn't play very nicely if A (m x n, m > n) happens to be of less than full rank: you get an answer, but in general it's _not_ actually the least-squares solution. In git, therefore, I've now replaced the lapack function we were calling before (dgels) with dgelsy, which uses column pivoting and handles rank deficiency correctly. Here's a little example that illustrates how things now work. <hansl> set verbose off scalar T = 50 scalar k = 4 matrix X = ones(T,1) ~ mnormal(T,k) matrix y = mnormal(T,1) # regular matrix OLS b = mols(y, X) uh = y - X*b SSR = uh'uh s2 = SSR/(T-cols(X)) se = sqrt(diag(s2 * inv(X'X))) printf "regular OLS (coeff, se):\n\n%12.6g\n", b~se printf "SSR = %g, s2 = %g\n\n", SSR, s2 # add a perfectly collinear column and use # left-division (QR with column pivoting) X ~= ones(T,1) b = X \ y uh = y - X*b SSR = uh'uh s2 = SSR/(T-rank(X)) se = sqrt(diag(s2 * ginv(X'X))) printf "Left-division, rank-deficient (coeff, se):\n\n%12.6g\n", b~se printf "SSR = %g, s2 = %g\n\n", SSR, s2 </hansl> The SSR will now be the same in the two cases: mols() with an X matrix of full rank, and left division with a redundant column added to X. In the latter case you get an extra coefficient, but X*b produces the same result. Allin