On Jan 27, 4:45 pm, Dick Startz <@> wrote: > On Thu, 26 Jan 2012 19:36:42 -0800 (PST), Eric Goodwin > > > > > > > > > > <eogood...@gmail.com> wrote: > >Hmm, thanks Paul, yes that's a good point. I do indeed hope to > >quantify relative influence, rather than simply successfully predict > >(or hindcast) measured values. Ultimately I hope to estimate what > >conditions would have been like, had driver values been different. > > >I had come across the ARIMA method in my reading around, but I hadn't > >even been sure that it could handle an independent variable distinct > >from its dependent variable. I need things really dumbed down before > >I can take them in. > > >I'm thinking probably econometrics is going to include methods pretty > >close to what I want to achieve, but wondered whether there might be > >anything in climate modelling that was statistical rather than > >numerical (mechanistic, PDE whatever you want to call it) in nature. > > >Further complexity is that the integration period for an independent > >variable I1 may vary over time, and may be dependent on the value of > >another independent variable I2. There may by lags between > >independent variables and the dependent variable, these lags might be > >different for different IVs, and they may vary over time, dependent on > >values of IVs. > > >As a first stab I divided the data in several contrasting sets, with
> >driver values greater or less than an arbitrary threshold, and tested > >for a difference between the dependent values of those two sets. > > >It's such a comprehensive dataset, I really feel like I owe it a > >thorough investigation! > > >Cheers, > > >Eric > > In econometrics it's pretty standard to run a multiple regression > where the error terms are serially correlated. Any econometrics > package will handle this. > -Dick Startz
Thanks Dick. Can you refer me to just one? (Preferably in R). I tried the Farnsworth package, but that does not appear to support multiple regression of time series.