Dear Statalist users,
I am using repeated cross sectional data from the World Value Survey for my analysis. Down below is an example of how the data ist structured in case this is needed. I am planning to do fixed effects models on country level, i have 35 countries and around 7 waves.
for example-> "reg political_interest year_survey age i.cow"
My question is, what regression diagnostics are useful for repeated cross sectional data. I know how to the usual commands like estat vif, rvpplot etc. for normal OLS regression but most of these are not applicable for time series data. Is it enough to time demean the data and then use the usual tests? I coldn't find substitutes in STATA.
Thank you for your help!
Sarah
I am using repeated cross sectional data from the World Value Survey for my analysis. Down below is an example of how the data ist structured in case this is needed. I am planning to do fixed effects models on country level, i have 35 countries and around 7 waves.
for example-> "reg political_interest year_survey age i.cow"
My question is, what regression diagnostics are useful for repeated cross sectional data. I know how to the usual commands like estat vif, rvpplot etc. for normal OLS regression but most of these are not applicable for time series data. Is it enough to time demean the data and then use the usual tests? I coldn't find substitutes in STATA.
Thank you for your help!
Sarah
Code:
* Example generated by -dataex-. For more info, type help dataex clear input int ysurv double pid int cow_num byte(polinterest sex) int age 1981 360120001 900 . 1 39 1981 360120002 900 . 1 34 1981 360120003 900 . 1 33 1981 360120004 900 . 2 33 1981 360120005 900 . 2 42 1981 360120006 900 . 1 33 1981 360120007 900 . 1 51 1981 360120008 900 . 2 36 1981 360120009 900 . 1 35 1981 360120010 900 . 2 18 1981 360120011 900 . 1 34 1981 360120012 900 . 2 49 1981 360120013 900 . 2 42 1981 360120014 900 . 2 22 1981 360120015 900 . 1 61 1981 360120016 900 . 2 46 1981 360120017 900 . 2 41 1981 360120018 900 . 1 62 1981 360120019 900 . 2 45 1981 360120020 900 . 1 45 end label values ysurv S020 label def S020 1981 " 1981", modify label values cow_num COW_NUM label def COW_NUM 900 "900. Australia", modify label values polinterest polin1 label values sex X001 label def X001 1 "Male", modify label def X001 2 "Female", modify label values age X003
Or do I need to explain something further?
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