[moving this to the devel list] Am 13.04.2018 um 21:50 schrieb Sven Schreiber: I'm reproducing the two private functions autocovar() and longrunvar() from the package below. The question is whether it might make sense to add these to the extra package, or whether it could be considered to generalize the built-in lrvar() to multivariate (like it seems to be the case in R, for example). What do you think? (I'm also open for suggestions to increase efficiency, but that's not very pressing right now. I think I read about fast algorithms that use the FFT for example.) thanks, sven ---- <from_CommonTrendsTest> function matrix autocovar (const matrix indatamat, int lag[0::0], bool demeaned[0]) /* Computes the autocovariance at lag "lag" for a multivariate time series. Returns a NxN covariance matrix */ # N = cols(indatamat) T = rows(indatamat) matrix datademeaned = demeaned ? indatamat : cdemean(indatamat) matrix result = datademeaned[lag+1 : T,]'datademeaned[1 : T-lag,] result /= T /* should hopefully be equivalent to the following loop code: matrix result = zeros(N,N) loop for tindex=lag..T matrix dev1 = datademeaned[tindex,] matrix dev2 = datademeaned[tindex-lag,] matrix result += dev1'dev2 endloop matrix result /= T */ return result end function function matrix longrunvar (const matrix indatamat, bool demeaned[0], int lagtrunc[1::4]) /* indatamat is a TxN time series matrix Bartlett window returns an NxN matrix (spectral density at frequency zero) */ N = cols(indatamat) matrix result = zeros(N,N) loop tau = 1..lagtrunc -q matrix Gamma = autocovar(indatamat, tau, demeaned) # positive and negative tau range together: result += (1 - tau / (lagtrunc + 1)) * (Gamma + Gamma') endloop # add the tau=0 part: result += autocovar(indatamat, 0, demeaned) return result end function </from_CommonTrendsTest>