I think I'm finished with Durbin-Watson for the moment: there's now a --dw-pval option to OLS and fixed effects estimation, which tells gretl to compute the p-value for the D-W statistic (using the Imhof method), hence making this statistic more useful (albeit at the cost of computations which could be quite time-consuming for big datasets). In the course of developing this I added 3 functions (and documented 2 of them). But I wonder if it's worth keeping any of them and I'd like to hear people's preferences. (To my mind, we don't want to multiply functions unnecessarily, since this makes it harder to find what you're looking for.) The functions in question are: * dwmat (takes one scalar argument, n): produces a tridiagonal Durbin-Watson matrix of order n. This can be done with standard matrix functions but it's a bit fiddly. Would anyone like to see this kept as a public function in "genr"? * dwpval (two matrix arguments, u and X): computes the p-value for the Durbin watson statistic given a column vector of residuals, u, and a regressor matrix, X. Probably too specialized to keep, if we now have the --dw-pval option? * imhof (matrix m, scalar x): computes Prob(u'Au < x) for a quadratic form in standard normal variates, u. If the argument m is a square matrix it is taken to be 'A', otherwise if it's a column vector it is taken to be the precomputed eigenvalues of A (otherwise error). This is not documented at present, but it's more general than the other 2 functions and maybe is worth documenting and keeping? Allin