Dear members
I am using the staggered DID in my data analysis and would like to control for industry*Year fixed effects together in my model. How can I create this variable? I also would like to identify firm-year observations that do not have financial data in my dataset so that I can drop them before doing my analysis.
Thanks for your kind help.
here is an example of my dataset:
input str44 Country int Year float SIC_2digit byte Civil_lib float Firm_id
"Argentina" 2014 . 2 9
"Australia" 2011 10 1 183
"Austria" 2011 43 1 37
"Bangladesh" 2019 32 5 196
"Belgium" 2006 38 1 203
"Brazil" 2013 . 2 448
"Canada" 2019 13 1 687
"Sri Lanka" 2018 21 4 3422
"Chile" 2006 49 1 792
"China" 2016 12 6 875
"Colombia" 2014 32 4 1105
"Croatia" 2019 29 2 1930
"Denmark" 2020 . 1 1235
"Finland" 2012 . 1 1342
"France" 2018 36 2 1448
"Germany" 2005 28 1 1208
"Hungary" 2009 48 1 1931
"Iceland" 2019 38 1 2189
"India" 2018 15 3 2187
"Indonesia" 2016 45 4 1970
"Ireland" 2015 50 1 1990
"Israel" 2013 48 2 2012
"Italy" 2017 . 1 2223
"Japan" 2020 48 1 2561
"Jordan" 2019 14 5 2282
"South Korea" 2007 35 2 3046
"Kuwait" 2019 48 5 3081
"Lithuania" 2018 48 1 3428
"Malaysia" 2018 44 4 3554
"Mexico" 2016 15 3 3482
"Morocco" 2013 48 4 3451
"Netherlands" 2013 35 1 3623
"New Zealand" 2018 12 1 3692
"Norway" 2013 . 1 3660
"Pakistan" 2015 13 5 3784
"Peru" 2009 49 3 3722
"Philippines" 2016 49 3 3749
"Poland" 2008 49 1 3803
"Portugal" 2019 24 1 3814
"Saudi Arabia" 2019 28 7 3865
I am using the staggered DID in my data analysis and would like to control for industry*Year fixed effects together in my model. How can I create this variable? I also would like to identify firm-year observations that do not have financial data in my dataset so that I can drop them before doing my analysis.
Thanks for your kind help.
here is an example of my dataset:
input str44 Country int Year float SIC_2digit byte Civil_lib float Firm_id
"Argentina" 2014 . 2 9
"Australia" 2011 10 1 183
"Austria" 2011 43 1 37
"Bangladesh" 2019 32 5 196
"Belgium" 2006 38 1 203
"Brazil" 2013 . 2 448
"Canada" 2019 13 1 687
"Sri Lanka" 2018 21 4 3422
"Chile" 2006 49 1 792
"China" 2016 12 6 875
"Colombia" 2014 32 4 1105
"Croatia" 2019 29 2 1930
"Denmark" 2020 . 1 1235
"Finland" 2012 . 1 1342
"France" 2018 36 2 1448
"Germany" 2005 28 1 1208
"Hungary" 2009 48 1 1931
"Iceland" 2019 38 1 2189
"India" 2018 15 3 2187
"Indonesia" 2016 45 4 1970
"Ireland" 2015 50 1 1990
"Israel" 2013 48 2 2012
"Italy" 2017 . 1 2223
"Japan" 2020 48 1 2561
"Jordan" 2019 14 5 2282
"South Korea" 2007 35 2 3046
"Kuwait" 2019 48 5 3081
"Lithuania" 2018 48 1 3428
"Malaysia" 2018 44 4 3554
"Mexico" 2016 15 3 3482
"Morocco" 2013 48 4 3451
"Netherlands" 2013 35 1 3623
"New Zealand" 2018 12 1 3692
"Norway" 2013 . 1 3660
"Pakistan" 2015 13 5 3784
"Peru" 2009 49 3 3722
"Philippines" 2016 49 3 3749
"Poland" 2008 49 1 3803
"Portugal" 2019 24 1 3814
"Saudi Arabia" 2019 28 7 3865
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