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From |
"Claus D. Hansen" <claushansen@pc.dk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: FW: How can I analyse a timeseries dataset appropriately? |

Date |
Thu, 17 Feb 2011 19:33:36 +0100 |

Dear list-members, I have a dataset I have assembled myself but I am having trouble figuring out just how to analyse it properly. I have a dataset consisting of a variable indicating how big a share of the total articles in a newspaper sickness absence has had for that specific year (this is a continuous variable). I have this information for 5 newspapers in the period 1991-2010 which means I have 100 observations all in all. For each year I have some information ? i.e. what government is ruling in that year (dummy variable), how big unemployment was, the average sickness absence rate and how many public money was used on sickness absence (last three are all continuous variables) ? these I want to know which of the independent variables predict the share of articles about sickness absence ? and I want to know if there is a ?time effect?, i.e. I want to know if there is an association between year and the share of articles on sickness absence when adjusting for the above mentioned independent variables ? and if the ?time effect? disappears I want to know which of the independent variables are ?responsible? for explaining how the ?time effect? disappears. I know that it will be wrong to use standard OLS ? because it?s a time series the observations will not be independent - another problem is that my independent variables are not specific to each of the 5 observations on the share of sickness absence articles. I suspect I should be using some kind of random effects modeling but I might be wrong ? can anyone point me to the right way of analyzing this? Thank you in advance, Claus D. Hansen Assistant professor Aalborg University Denmark My dataset looks like this ? and so on avis year sickleave sickab government mwork wwork mwcol wwcol sickexpenditure laws 1 1991 7.1 .6924009 1 11 13.1 4 6.9 5268250 0 2 1991 3.51 .54030064 1 11 13.1 4 6.9 5268250 0 3 1991 4.55 .48229296 1 11 13.1 4 6.9 5268250 0 4 1991 1.83 .26102845 1 11 13.1 4 6.9 5268250 0 5 1991 5.17 .5799623 1 11 13.1 4 6.9 5268250 0 1 1992 4.04 .49 1 10.8 15 4 6.7 5391442 2 2 1992 3.35 .41 1 10.8 15 4 6.7 5391442 2 3 1992 2.84 .26 1 10.8 15 4 6.7 5391442 2 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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