Hello, everyone,
I have a very simple request:
I am working the effect of specific crises on the sectoral GVA, and in the left sid, I have three dimention (s,i,t), while my shock is at the country level. I use the local projection of jorda (2005).
and I want to see the effect of the crises on the sectoral GVA.
I am not sure about the control fixed effect.
since I have a three dimentional dataset, I have to control for country-sector and sector-time fe.
1) is it correct that I doing?
Or, becasue the country-sector fixed effect already drop by "xtset country_ind year ", I should not add it in the model?
2) when I used the reghdfe insted of the xtscc, my point estimate is not the same, however I underestood that the only difference between these to is SE.
I am not sude how to add this control in the model.
thanks in advance for any help.
. dataex ifscode year sector_id GVA_sector institutions bankcrisis debtcrisis inflation r
> ecession
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Listed 100 out of 16968 observations
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I have a very simple request:
I am working the effect of specific crises on the sectoral GVA, and in the left sid, I have three dimention (s,i,t), while my shock is at the country level. I use the local projection of jorda (2005).
and I want to see the effect of the crises on the sectoral GVA.
I am not sure about the control fixed effect.
since I have a three dimentional dataset, I have to control for country-sector and sector-time fe.
1) is it correct that I doing?
Code:
xtscc gva_sector_0 l(0/1).recession L(1/2).institutions L(1/2).bankcrisis L(1/2).debtcrisis L(1/2).inflation l(1/1).gva_sector_0 i.country_ind i.time_ind, fe
Or, becasue the country-sector fixed effect already drop by "xtset country_ind year ", I should not add it in the model?
2) when I used the reghdfe insted of the xtscc, my point estimate is not the same, however I underestood that the only difference between these to is SE.
Code:
reghdfe gva_sector_0 l(0/1).recession L(1/2).institutions L(1/2).bankcrisis L(1/2).debtcrisis L(1/2).inflation l(1/1).gva_sector_0 , absorb(i.country_ind i.time_ind) vce(cluster ifscode)
I am not sude how to add this control in the model.
thanks in advance for any help.
Code:
egen country_ind=group(ifscode sector_id) egen country_time=group(ifscode year) egen time_ind=group(year sector_id) xtset country_ind year
. dataex ifscode year sector_id GVA_sector institutions bankcrisis debtcrisis inflation r
> ecession
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Code:
* Example generated by -dataex-. For more info, type help dataex clear input int ifscode double year float(sector_id gva_sector institutions bankcrisis debtcrisis inflation recession) 111 1973 2 .875 1.56345 0 0 .06034303 0 111 1973 3 1 1.56345 0 0 .06034303 0 111 1973 1 .5555556 1.56345 0 0 .06034303 0 111 1973 4 .3809524 1.56345 0 0 .06034303 0 111 1974 1 .5555556 1.71753 0 0 .10467768 0 111 1974 3 1 1.71753 0 0 .10467768 0 111 1974 2 1 1.71753 0 0 .10467768 0 111 1974 4 .3809524 1.71753 0 0 .10467768 0 111 1975 3 1 1.778193 0 0 .08738732 0 111 1975 1 .5555556 1.778193 0 0 .08738732 0 111 1975 4 .3809524 1.778193 0 0 .08738732 0 111 1975 2 1 1.778193 0 0 .08738732 0 111 1976 4 .3809524 1.7866578 0 0 .05578494 0 111 1976 3 1 1.7866578 0 0 .05578494 0 111 1976 2 1 1.7866578 0 0 .05578494 0 111 1976 1 .5555556 1.7866578 0 0 .05578494 0 111 1977 4 .3809524 1.82757 0 0 .06284785 0 111 1977 3 1 1.82757 0 0 .06284785 0 111 1977 2 1 1.82757 0 0 .06284785 0 111 1977 1 .5555556 1.82757 0 0 .06284785 0 111 1978 2 1 1.82757 0 0 .07369137 0 111 1978 3 1 1.82757 0 0 .07369137 0 111 1978 5 .930355 1.82757 0 0 .07369137 0 111 1978 4 .3809524 1.82757 0 0 .07369137 0 111 1978 1 .5555556 1.82757 0 0 .07369137 0 111 1979 1 .5555556 1.8261594 0 0 .09059691 0 111 1979 4 .3809524 1.8261594 0 0 .09059691 0 111 1979 2 1 1.8261594 0 0 .09059691 0 111 1979 5 .930355 1.8261594 0 0 .09059691 0 111 1979 3 1 1.8261594 0 0 .09059691 0 111 1980 1 .7222222 1.826495 0 0 .1053605 0 111 1980 5 .9355956 1.826495 0 0 .1053605 0 111 1980 3 1 1.826495 0 0 .1053605 0 111 1980 4 .3809524 1.826495 0 0 .1053605 0 111 1980 2 1 1.826495 0 0 .1053605 0 111 1981 1 .7222222 1.8737898 0 0 .09172463 0 111 1981 3 1 1.8737898 0 0 .09172463 0 111 1981 5 .9426824 1.8737898 0 0 .09172463 0 111 1981 4 .3809524 1.8737898 0 0 .09172463 0 111 1981 2 1 1.8737898 0 0 .09172463 0 111 1982 3 1 1.8737898 0 0 .05859399 0 111 1982 2 1 1.8737898 0 0 .05859399 0 111 1982 4 .3809524 1.8737898 0 0 .05859399 0 111 1982 1 .8333333 1.8737898 0 0 .05859399 0 111 1982 5 .9430157 1.8737898 0 0 .05859399 0 111 1983 5 .9478928 1.8709682 0 0 .04114723 0 111 1983 4 .3809524 1.8709682 0 0 .04114723 0 111 1983 2 1 1.8709682 0 0 .04114723 0 111 1983 1 .8333333 1.8709682 0 0 .04114723 0 111 1983 3 1 1.8709682 0 0 .04114723 0 111 1984 1 .8333333 1.872379 1 0 .041394 0 111 1984 5 .9499953 1.872379 1 0 .041394 0 111 1984 3 1 1.872379 1 0 .041394 0 111 1984 2 1 1.872379 1 0 .041394 0 111 1984 4 .52380955 1.872379 1 0 .041394 0 111 1985 4 .52380955 1.878022 0 0 .033131838 0 111 1985 2 1 1.878022 0 0 .033131838 0 111 1985 3 1 1.878022 0 0 .033131838 0 111 1985 1 .8333333 1.878022 0 0 .033131838 0 111 1985 5 .9546942 1.878022 0 0 .033131838 0 111 1986 1 .8333333 1.8766114 0 0 .018535614 0 111 1986 4 .52380955 1.8766114 0 0 .018535614 0 111 1986 5 .9547356 1.8766114 0 0 .018535614 0 111 1986 3 1 1.8766114 0 0 .018535614 0 111 1986 2 1 1.8766114 0 0 .018535614 0 111 1987 5 .9547356 1.8695575 0 0 .03263712 0 111 1987 1 .8333333 1.8695575 0 0 .03263712 0 111 1987 3 1 1.8695575 0 0 .03263712 0 111 1987 4 .52380955 1.8695575 0 0 .03263712 0 111 1987 2 1 1.8695575 0 0 .03263712 0 111 1988 5 .9547356 1.8709682 0 0 .03603983 0 111 1988 3 1 1.8709682 0 0 .03603983 0 111 1988 4 .52380955 1.8709682 0 0 .03603983 0 111 1988 2 1 1.8709682 0 0 .03603983 0 111 1988 1 .8333333 1.8709682 0 0 .03603983 0 111 1989 5 .9550222 1.8737898 0 0 .04223704 0 111 1989 4 .52380955 1.8737898 0 0 .04223704 0 111 1989 1 .8333333 1.8737898 0 0 .04223704 0 111 1989 3 .9702252 1.8737898 0 0 .04223704 0 111 1989 2 1 1.8737898 0 0 .04223704 0 111 1990 3 .9702252 1.8752005 0 0 .04813337 0 111 1990 5 .9555556 1.8752005 0 0 .04813337 0 111 1990 2 1 1.8752005 0 0 .04813337 0 111 1990 4 .52380955 1.8752005 0 0 .04813337 0 111 1990 1 .8333333 1.8752005 0 0 .04813337 0 111 1991 1 .8888889 1.8794328 0 0 .035735846 0 111 1991 5 .9546667 1.8794328 0 0 .035735846 0 111 1991 2 1 1.8794328 0 0 .035735846 0 111 1991 4 .52380955 1.8794328 0 0 .035735846 0 111 1991 3 .9702252 1.8794328 0 0 .035735846 0 111 1992 1 .8888889 1.8808436 0 0 .02456188 0 111 1992 2 1 1.8808436 0 0 .02456188 0 111 1992 4 .52380955 1.8808436 0 0 .02456188 0 111 1992 5 .9554667 1.8808436 0 0 .02456188 0 111 1992 3 .9702252 1.8808436 0 0 .02456188 0 111 1993 5 .9568 1.8688853 0 0 .02490139 0 111 1993 2 1 1.8688853 0 0 .02490139 0 111 1993 4 .52380955 1.8688853 0 0 .02490139 0 111 1993 3 .9702252 1.8688853 0 0 .02490139 0 111 1993 1 .8888889 1.8688853 0 0 .02490139 0 end
Listed 100 out of 16968 observations
Use the count() option to list more
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