Hi users
I am new to using the Synthetic control method as outlined in Ben-Michael et al. [2021]. . I am interested in examining the impact of a mandate on a firm's transparency. The mandate is staggered across different countries and I would like to use this method to plot the annual estimates of the average treatment effect on the treated, including 95% confidence intervals, in event-time relative to the year of adoption of the disclosure regulation: beow is the an example of my data:
input float(transparency treatedxmandate) int(Policy1Year Year) float firm_id
.97 0 . 2010 7
1.01 0 . 2013 7
.39 0 . 2014 1561
.98 0 . 2014 4
.99 0 . 2014 7
.99 0 . 2015 7
.98 0 . 2015 4
.42 0 . 2015 1561
.51 0 . 2015 6
.88 0 . 2015 5
.98 0 . 2016 4
.53 0 . 2016 5
.45 0 . 2016 7
.32 0 . 2016 6
.35 0 . 2016 1561
.53999996 0 . 2017 5
.99 0 . 2017 4
.31 0 . 2017 6
.4 0 . 2017 7
1.02 0 . 2018 8
.39 0 . 2018 7
.55999994 0 . 2018 5
.31 0 . 2018 6
.99 0 . 2018 4
.39 0 . 2019 7
.3 0 . 2019 6
.57 0 . 2019 5
.99 0 . 2019 4
1.04 0 . 2019 8
.98 0 . 2020 5
.13 0 . 2005 17
.52 0 . 2005 18
.52 0 . 2005 16
.6 0 . 2006 16
.78 0 . 2006 1722
.36 0 . 2006 17
.53 0 . 2006 18
.34 0 . 2006 22
.26999998 0 . 2007 17
.56 0 . 2007 18
.34 0 . 2007 22
.4 0 . 2008 17
.5 0 . 2008 18
thanks for your kind help.
I am new to using the Synthetic control method as outlined in Ben-Michael et al. [2021]. . I am interested in examining the impact of a mandate on a firm's transparency. The mandate is staggered across different countries and I would like to use this method to plot the annual estimates of the average treatment effect on the treated, including 95% confidence intervals, in event-time relative to the year of adoption of the disclosure regulation: beow is the an example of my data:
input float(transparency treatedxmandate) int(Policy1Year Year) float firm_id
.97 0 . 2010 7
1.01 0 . 2013 7
.39 0 . 2014 1561
.98 0 . 2014 4
.99 0 . 2014 7
.99 0 . 2015 7
.98 0 . 2015 4
.42 0 . 2015 1561
.51 0 . 2015 6
.88 0 . 2015 5
.98 0 . 2016 4
.53 0 . 2016 5
.45 0 . 2016 7
.32 0 . 2016 6
.35 0 . 2016 1561
.53999996 0 . 2017 5
.99 0 . 2017 4
.31 0 . 2017 6
.4 0 . 2017 7
1.02 0 . 2018 8
.39 0 . 2018 7
.55999994 0 . 2018 5
.31 0 . 2018 6
.99 0 . 2018 4
.39 0 . 2019 7
.3 0 . 2019 6
.57 0 . 2019 5
.99 0 . 2019 4
1.04 0 . 2019 8
.98 0 . 2020 5
.13 0 . 2005 17
.52 0 . 2005 18
.52 0 . 2005 16
.6 0 . 2006 16
.78 0 . 2006 1722
.36 0 . 2006 17
.53 0 . 2006 18
.34 0 . 2006 22
.26999998 0 . 2007 17
.56 0 . 2007 18
.34 0 . 2007 22
.4 0 . 2008 17
.5 0 . 2008 18
thanks for your kind help.
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