Hi everyone, I have a question about producing table in Stata. What I'm doing in the following example code is to run regressions first and then collect the p-values of each coefficient. Then I calculate a new statistic, sharpened-q value, based on these p-values. q-value is stored in a variable named bky06_qval. In the table, I want to show 1) coefficients 2) t-statistic 3) sharpened q-value. Can anybody help? Also, is there an easier way to extract p-values from the regression results? Thanks a ton!
| dependent var | |
| independent var | coefficient |
| t-statistic | |
| sharpened-q value |
Code:
sysuse auto, clear
* regression 1
reg price mpg rep78 headroom
mat m1 = r(table)
forval i = 1/3 {
scalar p1_`i' = m1[4, `i']
}
eststo reg1
* regression 2
reg trunk mpg rep78 headroom
mat m2 = r(table)
forval i = 1/3 {
scalar p2_`i' = m2[4, `i']
}
eststo reg2
* calculate sharpened q-value; taken from Michael Anderson’s code (2008)
quietly gen float pval = .
display "***********************************"
display "Please paste the vector of p-values that you wish to test into the variable 'pval'"
display "After pasting, type 'q' to resume"
display "***********************************"
replace pval = p1_1 if _n == 1
replace pval = p1_2 if _n == 2
replace pval = p1_3 if _n == 3
replace pval = p2_1 if _n == 4
replace pval = p2_2 if _n == 5
replace pval = p2_3 if _n == 6
* Collect the total number of p-values tested
quietly sum pval
local totalpvals = r(N)
* Sort the p-values in ascending order and generate a variable that codes each p-value's rank
quietly gen int original_sorting_order = _n
quietly sort pval
quietly gen int rank = _n if pval~=.
* Set the initial counter to 1
local qval = 1
* Generate the variable that will contain the BKY (2006) sharpened q-values
gen bky06_qval = 1 if pval~=.
* Set up a loop that begins by checking which hypotheses are rejected at q = 1.000, then checks which hypotheses are rejected at q = 0.999, then checks which hypotheses are rejected at q = 0.998, etc. The loop ends by checking which hypotheses are rejected at q = 0.001.
while `qval' > 0 {
* First Stage
* Generate the adjusted first stage q level we are testing: q' = q/1+q
local qval_adj = `qval'/(1+`qval')
* Generate value q'*r/M
gen fdr_temp1 = `qval_adj'*rank/`totalpvals'
* Generate binary variable checking condition p(r) <= q'*r/M
gen reject_temp1 = (fdr_temp1>=pval) if pval~=.
* Generate variable containing p-value ranks for all p-values that meet above condition
gen reject_rank1 = reject_temp1*rank
* Record the rank of the largest p-value that meets above condition
egen total_rejected1 = max(reject_rank1)
* Second Stage
* Generate the second stage q level that accounts for hypotheses rejected in first stage: q_2st = q'*(M/m0)
local qval_2st = `qval_adj'*(`totalpvals'/(`totalpvals'-total_rejected1[1]))
* Generate value q_2st*r/M
gen fdr_temp2 = `qval_2st'*rank/`totalpvals'
* Generate binary variable checking condition p(r) <= q_2st*r/M
gen reject_temp2 = (fdr_temp2>=pval) if pval~=.
* Generate variable containing p-value ranks for all p-values that meet above condition
gen reject_rank2 = reject_temp2*rank
* Record the rank of the largest p-value that meets above condition
egen total_rejected2 = max(reject_rank2)
* A p-value has been rejected at level q if its rank is less than or equal to the rank of the max p-value that meets the above condition
replace bky06_qval = `qval' if rank <= total_rejected2 & rank~=.
* Reduce q by 0.001 and repeat loop
drop fdr_temp* reject_temp* reject_rank* total_rejected*
local qval = `qval' - .001
}
quietly sort original_sorting_order
esttab reg* using "test.tex"
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