Herv�, you need to store the predicted signs in a matrix that
contains all variables. Thus, estimate a model with all variables
before defining the signs. In your case:
regress em_leuz rank_investor rank_legal rank_absence1 ///
rank_divergence1 investor legal_enforcement absence1 divergence1
tempname b
matrix `b' = e(b)
...
ben
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Herve STOLOWY
> Sent: Saturday, March 05, 2005 10:32 AM
> To: [email protected]
> Subject: RE: st: RE: Estout and predicted signs - new questions
>
>
> Dear Ben:
>
> I tried and it worked for three out of four of my estout
> outputs. However, I have a fourth and more complex table
> which generates a conformability error. In summary, it has
> the following structure:
>
> Model 1: regress y var1 var2
> Model 2: regress y var1 var2 var3
> Model 3: regress y var1 var2 var4
> Model 4: regress y var1 var2 var3 var4
> Model 5: regress y var5 var6
> Model 6: regress y var5 var6 var7
> Model 7: regress y var5 var6 var8
> Model 8: regress y var5 var6 var7 var8
>
> I get a conformability error message after the following command:
> matrix `b'[1,6] = -999 //neg. sign for legal enforcement
>
> Here are the commands:
>
> regress em_leuz rank_investor rank_legal, cluster(name) robust
> est store m7, title(Model 1)
> regress em_leuz rank_investor rank_legal rank_absence1,
> cluster(name) robust
> est store m8, title(Model 2)
> regress em_leuz rank_investor rank_legal rank_divergence1,
> cluster(name) robust
> est store m9, title(Model 3)
> regress em_leuz rank_investor rank_legal rank_absence1
> rank_divergence1, cluster(name) robust
> est store m10, title(Model 4)
> regress em_leuz investor legal_enforcement, cluster(name) robust
> est store m11, title(Model 5)
> regress em_leuz investor legal_enforcement absence1,
> cluster(name) robust
> est store m12, title(Model 6)
> regress em_leuz investor legal_enforcement divergence1,
> cluster(name) robust
> est store m13, title(Model 7)
> regress em_leuz investor legal_enforcement absence1
> divergence1, cluster(name) robust
> est store m14, title(Model 8)
>
> tempname b
> matrix `b' = e(b)
> matrix `b'[1,1] = -999 //neg. sign for rank of investor protection
> matrix `b'[1,2] = -999 //neg. sign for rank of legal enforcement
> matrix `b'[1,3] = +999 //pos. sign for rank of absence
> matrix `b'[1,4] = 998 //? for rank of divergence
> matrix `b'[1,5] = -999 //neg. sign for investor protection
> matrix `b'[1,6] = -999 //neg. sign for legal enforcement
> matrix `b'[1,7] = +999 //pos. sign for absence
> matrix `b'[1,8] = 998 //? for divergence
> matrix `b'[1,9] = 998 //? for _cons
> eret2 matrix signs = `b'
> local e_scalars: e(scalars) //get the names of the scalars in e()
> foreach e of local e_scalars {
> eret2 local `e' , replace //replace the scalars by empty macros
> //or: eret2 scalar `e' = .z , replace //set the scalars to missing
> }
> estimates store pred
>
> estout pred m7 m8 m9 m10 m11 m12 m13 m14 using using
> estout_table6_V3, replace substitute(-999.000 - 999.000 +
> 998.000 ?) cells ("signs(pat (1 0 0 0 0 0 0 0 0))
> b(fmt(%9.3f) pat(0)) p(fmt(%9.3f) pattern(0))") stats (N F p
> r2 r2_a, fmt(%9.0f %9.3f %9.3f %9.3f %9.3f) labels ("Number
> of observations" "F" "Prob>F" "R-square" "Adjusted
> R-square")) label varlabels(_cons Constant)
>
> Maybe the problem arises from the structure of the table
> which mixes two basic models (with var1 to var4 and then with
> var 5 to var 8).
>
> I can add that in the three other tables, the solution you
> proposed works perfectly. But they are all based on the
> accumulation of the same variables (from x1 to x8 for the
> more complex table).
>
> Sorry again to bother you but I promise it's the last table.
>
> Best regards
>
> Herv�
>
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