[R] best analysis method : for time series ans cross sectional data

[Spencer Graves]

      It looks to me like what you want is "intervention analysis" in 
the time series literature.  Have you considered the arima function, 
especially the example in the documentation using the xreg argument?  
Also, have you looked at ch. 14 in Venables and Ripley (2002) Modern 
Applied Statistics with S, 4th ed. (Springer)? 

      There are other time series packages available, e.g, dse, fSeries, 
its, GeneTS, msm, pastecs, splancs, tseries, urca, uroot, but I haven't 
used them and so can't comment further on them. 

      hope this helps.  spencer graves

Kum-Hoe Hwang wrote:

>Howdy
>
>What I 'd like to analyze with a large data on building permits is to find
>time series effect of urban policy on buildings as well as
>cross-sectional effects in any. In 1990 the specialZone urban policy
>was introduced. I guess that the effects of this specialZone policy
>would be different from countys. There are counties that do not
>welcome this specialZone forced to design it.
>
>One of the important aims is to find 1) time series effect using Dummy
>variable,  2) cross-sectional effects using specialZones variable
>below.
>
>The data has items like year(1970-2000), floorSpace, county,
>specialZones agianst permitting large buildings. specialZones have
>been designed after 1990.
>(Dummy = 1 after 1990, Dummy =0 before 1990)
>
>I have tried three methods, such as
> lm(floorSpace ~ county, specialZones, Dummy), 
> glm(floorSpace ~ county, specialZones, Dummy),
> aov(floorSpace ~ county, specialZones, Dummy).
>
>What I am focusing on is best method among lm, glm, aov or others not
>siginificant results.
>
>I have wasted  too  much time for this. I welcome your comments.
>
>Thanks a lot,
>
>  
>