Hello,
I am using stata to compute the instability of industry sales over the preceding 5 years (Bergh & Lawless, 1998). Because of paper-style, I have just a few details. I am following the instructions by Bergh & Lawless (1998): it was calculated 1. by regression analyses in which a variable for each year was regressed on a variable for net industry sales 2. five-year data were used for each regression (e.g. net industry sales values from 1985-1989 were used to predict instability in 1990).
The basic equation is: yt = b0 + b1t + at, where y= industry sales, t= year and a=residual.
Finally, instability is then defined as the standard error of the regression slope coefficient divided by the mean of industry sales.
This procedure is very similar to the calculation of the environmental dimension `dynamism" defined by Dess & Beard (1984).
I´ve tried to calculate instability step by step, but sometimes I am not sure, whether I has understood it correctly, especially the regression. So, hope you can help me.
My code:
* Step 1 - The regression and standard error of the regression slope coefficient
collapse (sum)sale, by (SIC year)
xtset SIC year
save instability, replace
rolling regress_sale_SE=_se[year], window(5): regress sale year
save dynamism, replace
*Five years´ data were used for each regression (industry sales 1985-1989 for instability in 1990)
gen lead1= end + 1
rename lead1 year
sort SIC year
save instability, replace
*Step 2 - the mean-value of the dependent variable industry sales (over 5 year period)
collapse (sum) sale, by (SIC year)
ssc inst rangestat
rangestat (mean) sale, interval (year -5 -1) by(SIC)
sort SIC year
save mean_values, replace
*Merge
merge 1:1 SIC year using "instability", keep(master match)
drop _merge
*coefficient divided by the mean value of dependant variable
bysort SIC year: generate dynamism= regress_sale_SE/sale_mean
save dynamism
That´s the code so far, but I am very unsure.
I am thankful for some advice.
I am using stata to compute the instability of industry sales over the preceding 5 years (Bergh & Lawless, 1998). Because of paper-style, I have just a few details. I am following the instructions by Bergh & Lawless (1998): it was calculated 1. by regression analyses in which a variable for each year was regressed on a variable for net industry sales 2. five-year data were used for each regression (e.g. net industry sales values from 1985-1989 were used to predict instability in 1990).
The basic equation is: yt = b0 + b1t + at, where y= industry sales, t= year and a=residual.
Finally, instability is then defined as the standard error of the regression slope coefficient divided by the mean of industry sales.
This procedure is very similar to the calculation of the environmental dimension `dynamism" defined by Dess & Beard (1984).
I´ve tried to calculate instability step by step, but sometimes I am not sure, whether I has understood it correctly, especially the regression. So, hope you can help me.
My code:
* Step 1 - The regression and standard error of the regression slope coefficient
collapse (sum)sale, by (SIC year)
xtset SIC year
save instability, replace
rolling regress_sale_SE=_se[year], window(5): regress sale year
save dynamism, replace
*Five years´ data were used for each regression (industry sales 1985-1989 for instability in 1990)
gen lead1= end + 1
rename lead1 year
sort SIC year
save instability, replace
*Step 2 - the mean-value of the dependent variable industry sales (over 5 year period)
collapse (sum) sale, by (SIC year)
ssc inst rangestat
rangestat (mean) sale, interval (year -5 -1) by(SIC)
sort SIC year
save mean_values, replace
*Merge
merge 1:1 SIC year using "instability", keep(master match)
drop _merge
*coefficient divided by the mean value of dependant variable
bysort SIC year: generate dynamism= regress_sale_SE/sale_mean
save dynamism
That´s the code so far, but I am very unsure.
I am thankful for some advice.
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