[George Ford]

sysuse auto, clear
reg price mpg c.mpg#(c.weight c.length c.foreign) , noconstant
predict pfit , xb
gen pfitalt = _b[mpg]*mpg + _b[c.mpg#c.weight]*mpg*weight+_b[c.mpg#c.length]*mpg*length 
gen newvar = _b[c.mpg#c.weight]*mpg*weight+_b[c.mpg#c.length]*mpg*length 
gen newvaralt = pfit-_b[mpg]*mpg

[George Ford]

cancel that. error.

[George Ford]

A start (thoughts welcome).

sysuse auto, clear

capture program drop myprog
program define myprog, rclass
    reg mpg weight c.weight#(c.length c.foreign) , noconstant
    capture drop gr
    gen gr = _b[c.weight#c.length]*weight*length+_b[c.weight#c.foreign]*weight*foreign  
    reg price trunk gr
    return scalar t_trunk = (_b[trunk]-b_trunk)/_se[trunk]
    return scalar t_gr = (_b[gr]-b_gr)/_se[gr]
    return scalar t_cons = (_b[_cons]-b_cons)/_se[_cons]
end

reg mpg weight c.weight#(c.length c.foreign) , noconstant
capture drop gr
gen gr = _b[c.weight#c.length]*weight*length+_b[c.weight#c.foreign]*weight*foreign
reg price trunk gr
scalar b_trunk = _b[trunk]
scalar b_gr = _b[gr]
scalar b_cons = _b[_cons]

bootstrap t_trunk=r(t_trunk) t_gr=r(t_gr) t_cons=r(t_cons), reps(100) seed(12345) saving(bs_t, replace): myprog
preserve
use bs_t, replace
centile t_trunk, centile(2.5, 97.5)
centile t_gr, centile(2.5, 97.5)
centile t_cons, centile(2.5, 97.5)
gen t_abs_trunk = abs(t_trunk)
gen t_abs_gr = abs(t_gr)
gen t_abs_cons = abs(t_cons)
centile t_abs_trun t_abs_gr t_abs_cons, centile(95)
restore

[gagandeep sharma]

A start (thoughts welcome).

sysuse auto, clear

capture program drop myprog
program define myprog, rclass
reg mpg weight c.weight#(c.length c.foreign) , noconstant
capture drop gr
gen gr = _b[c.weight#c.length]*weight*length+_b[c.weight#c.foreign]*weight*foreign
reg price trunk gr
return scalar t_trunk = (_b[trunk]-b_trunk)/_se[trunk]
return scalar t_gr = (_b[gr]-b_gr)/_se[gr]
return scalar t_cons = (_b[_cons]-b_cons)/_se[_cons]
end

reg mpg weight c.weight#(c.length c.foreign) , noconstant
capture drop gr
gen gr = _b[c.weight#c.length]*weight*length+_b[c.weight#c.foreign]*weight*foreign
reg price trunk gr
scalar b_trunk = _b[trunk]
scalar b_gr = _b[gr]
scalar b_cons = _b[_cons]

bootstrap t_trunk=r(t_trunk) t_gr=r(t_gr) t_cons=r(t_cons), reps(100) seed(12345) saving(bs_t, replace): myprog
preserve
use bs_t, replace
centile t_trunk, centile(2.5, 97.5)
centile t_gr, centile(2.5, 97.5)
centile t_cons, centile(2.5, 97.5)
gen t_abs_trunk = abs(t_trunk)
gen t_abs_gr = abs(t_gr)
gen t_abs_cons = abs(t_cons)
centile t_abs_trun t_abs_gr t_abs_cons, centile(95)
restore

Thanks George,

With my limited coding abilities I think it will take me a day or two to understand this. I'm grateful that you spent so much time to help me out.

[George Ford]

I ripped the general setup from

https://www.schmidheiny.name/teaching/bootstrap2up.pdf

This bootstraps the critical t-stats for each variable in the second stage equation (since the nominal ones are presumed incorrect). You'd compare the t-stats from the regression to these values rather than the normal table. First set is asymmetric values, the second is a symmetric. I think the former is probably better, but others may disagree.

You can fancy it up where it makes a nice table if that's your desire.

The main thing is that both equations are part of the bootstrap procedure (each round generates a new generated regressor gr).

[Carlo Lazzaro]

Gagandeep:
another source to grasp the building blocks ogf George's helpful advice, is Example 3, -bootstrap- entry, Stata .pdf manual.

[gagandeep sharma]

sysuse auto, clear
reg price mpg weight length foreign
* use this if you want the prediction of the regression
predict pfit , xb
* use this if you want parts of the regression (2 ways to get to the same result)
gen newvar = _b[_cons] + _b[mpg]*mpg + _b[weight]*weight +_b[length]*length
gen newvaralt = pfit-_b[foreign]*foreign
* newvar = newvaralt

You've got ugly variable names. If you have trouble matching up the variable names with the coefficients (_b[x]), then

matrix list e(b)

to see what Stata sees.

Hi George,

Terribly sorry to bother you again. if possible can you address my original query. I have tried multiple iterations but I keep getting results which aren't theoretically possible.

Post the regression command:

regress actual_tier1_gap l.tier1_gap1 l.((c.tier1_gap1)#(i.state1nonstate0 c.size c.return_on_equity_w ///
c.provforNPA_to_net_advances_w i.Listeddummy1iflisted c.GdpGrowthRate c.inflation)), noconst

I need to construct a new variable VarX = Λ^hatZ_i,t-1 where vector Z is (i.state1nonstate0 c.size c.return_on_equity_w i.below_tier1 c.provforNPA_to_net_advances_w i.Listeddummy1iflisted c.GdpGrowthRate c.inflation)
since in the regression all these variables are interacted with Gap_i,t-1 so to get the original coefficient in vector z is following approach correct?

generate X = ((_b[cL.tier1_gap1#cL.size] / (_b[L.tier1_gap1])) * L.size) --------- (for one of the variables in vector z, and so on adding all the variables)

in short, is the true Λ^hat in vector Z_i,t-1 given by: [_b(l.variable*l.gap)/_b(l.gap)] for each variable in the vector Z?

[George Ford]

Use all the variables you want to include in generate X to get a single X.

[gagandeep sharma]

Use all the variables you want to include in generate X to get a single X.

Thanks George,

Could you please comment on this:

Since in the regression individual Betas are the product of two variables (variable*gap), is the true Λ^hat in vector Z_i,t-1 given by: [_b(l.variable*l.gap)/_b(l.gap)] for each variable in the vector Z?

so that i can use:

generate X = ((_b[cL.tier1_gap1#cL.size] / (_b[L.tier1_gap1])) * L.size) --------- (for one of the variables in vector z, and so on adding all the variables)

[George Ford]

I don't think you need to include a ratio, but I'm not sure exactly what you're after. You have a linear model with a lot of interactions. Not sure where the ratio comes into play if you want a prediction from that regression. If the theory calls for it, then sure.