> > Dear R People: > > > > Suppose we have a regression model that we will call > > y.lm > > > > We run the Durbin Watson test for autocorrelation > > and we find that there is positive autocorrelation, > > and phi = 0.72, say. > > > > What is our next step, please? > > Look at the residuals more closely, e.g. look at the acf. > > > Do we calculate the following > > yprime_t = y_t - 0.72y_t-1, > > x1prime_t = x1_t - 0.72x1_t-1, > > > > and so on, and re-fit the linear mode?
Hello Erin, beside the points mentioned by Prof. Ripley, you might also want to consider test for order of integration of y and x and if cointegration exists between these variables. A high R2 and a low DW is often a hindsight for a spurious relationship, which needs to be investigated further. There is the contributed package 'urca' available. Incidentally, a package update will be put on CRAN shortly. If you want to receive it now, pls. contact me offline and name your OS (i.e. zip or tar.gz). HTH, Bernhard > > Better to use arima with AR residuals and an xreg matrix. > > -- > Brian D. Ripley, [EMAIL PROTECTED] > Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ > University of Oxford, Tel: +44 1865 272861 (self) > 1 South Parks Road, +44 1865 272866 (PA) > Oxford OX1 3TG, UK Fax: +44 1865 272595 > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -------------------------------------------------------------------------------- The information contained herein is confidential and is inte...{{dropped}} ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html