I am trying to run this code, with which I have had issues before.
gen ln_PERCAPITA= ln(PERCAPITA)
* Regresión
regress ln_PERCAPITA ethnici1 edumo2 edumo3 edufa2 edufa3 female birthreg2 birthreg3 desplazado1 [iw=FEX_C] if FG==1
**Construccion inicadormatrix
matrix b= e(b)'
mat list b
mat rowname b = b1 b2 b3 b4 b5 b6 b7 b8 b9 b10
mat list b
predict mu
** Smoothed distribution
gen mu_tilde= exp(mu) if FG==1
label var mu_tilde "Smoothed distribution"
gen ln_mutilde = ln(mu_tilde)
**drop mu_tilde
** Residuales
gen e= ln_PERCAPITA-mu if FG==1
*las comillas en el r(mean) indican que estoy utilizando una variable macro
sum ethnici1 [iw=FEX_C] if FG==1
local ethnici1= `r(mean)'
sum edumo2 [iw=FEX_C] if FG==1
local edumo2= `r(mean)'
sum edumo3 [iw=FEX_C] if FG==1
local edumo3= `r(mean)'
sum edufa2 [iw=FEX_C] if FG==1
local edufa2= `r(mean)'
sum edufa3 [iw=FEX_C] if FG==1
local edufa3= `r(mean)'
sum female [iw=FEX_C] if FG==1
local female= `r(mean)'
sum birthreg2 [iw=FEX_C] if FG==1
local birthreg2 = `r(mean)'
sum birthreg3 [iw=FEX_C] if FG==1
local birthreg3= `r(mean)'
sum desplazado1 [iw=FEX_C] if FG==1
local desplazado1= `r(mean)'
mat C = (`ethnici1', `edumo2', `edumo3', `edufa2', `edufa3', `female', `birthreg2', `birthreg3' , `desplazado1', 1)
mat list C
mat v= C*b
mat list v
gen v= `ethnici1'*b[1,1] +`edumo2'*b[2,1] +`edumo3'*b[3,1] + `edufa2'*b[4,1] + `edufa3'*b[5,1] + `female'*b[6,1] + `birthreg2'*b[7,1] + `birthreg3'b[8,1] + `desplazado1'*b[9,1] + b[10, 1]
sum v
gen vtilde= exp(v + e) if FG==1
gen ln_vtilde =ln(vtilde)
sum(PERCAPITA) [iw=FEX_C] if FG==1
local sum_percapita= r(sum)
local mean_percapita= r(mean)
**Generar ln del mean anterior
local ln_meanpercapita= ln(`mean_percapita')
**Logaritmo de los ingresos percapita de cada uno de los indviduos
sum (ln_PERCAPITA) [iw=FEX_C] if FG==1
local sum_lnpercapita= r(sum)
local sum_wlnpercapita= r(sum_w)
local mld= `ln_meanpercapita' - (`sum_lnpercapita'/`sum_wlnpercapita')
gen mld= `ln_meanpercapita' - (`sum_lnpercapita'/`sum_wlnpercapita')
dis `mld'
** ineqdeco PERCAPITA [iw=FEX_C] if FG==1
**Smoothed distribution
sum(mu_tilde) [iw=FEX_C] if FG==1
local sum_mutilde= r(sum)
local mean_mutilde= r(mean)
**Generar ln del mean anterior
local ln_meanmutilde= ln(`mean_mutilde')
sum (ln_mutilde) [iw=FEX_C] if FG==1
local sum_lnmutilde= r(sum)
local sum_wlnmutilde= r(sum_w)
local mldsmooth= `ln_meanmutilde' - (`sum_lnmutilde'/`sum_wlnmutilde')
gen mldsmooth= `ln_meanmutilde' - (`sum_lnmutilde'/`sum_wlnmutilde')
dis `mldsmooth'
*Standardized distribution
sum(vtilde) [iw=FEX_C] if FG==1
local sum_vtilde= r(sum)
local mean_vtilde= r(mean)
**Generar ln del mean anterior
local ln_meanvtilde= ln(`mean_vtilde')
sum (ln_vtilde) [iw=FEX_C] if FG==1
local sum_lnvtilde= r(sum)
local sum_wlnvtilde= r(sum_w)
local mldstandar= `ln_meanvtilde' - (`sum_lnvtilde'/`sum_wlnvtilde')
gen mldstandar= `ln_meanvtilde' - (`sum_lnvtilde'/`sum_wlnvtilde')
dis `mldstandar'
** IOL (θa) ecuación (10) y (10')
gen iol= mldsmooth
gen iol_std= mld - mldstandar
**IOR (θa) ecuación (11) y (11')
gen ior= mldsmooth/mld
gen ior_std= 1 - mldstandar/ mld
The error is:
gen v= `ethnici1'*b[1,1] +`edumo2'*b[2,1] +`edumo3'*b[3,1] + `edufa2'*b[4,1] + `edufa3'*b[5,1] + `female'*b[6,1] + `birthreg2'*b
> [7,1] + `birthreg3'b[8,1] + `desplazado1'*b[9,1] + b[10, 1]
.5178703044404551b invalid name
Can anyone help me figuring out the mistake?
gen ln_PERCAPITA= ln(PERCAPITA)
* Regresión
regress ln_PERCAPITA ethnici1 edumo2 edumo3 edufa2 edufa3 female birthreg2 birthreg3 desplazado1 [iw=FEX_C] if FG==1
**Construccion inicadormatrix
matrix b= e(b)'
mat list b
mat rowname b = b1 b2 b3 b4 b5 b6 b7 b8 b9 b10
mat list b
predict mu
** Smoothed distribution
gen mu_tilde= exp(mu) if FG==1
label var mu_tilde "Smoothed distribution"
gen ln_mutilde = ln(mu_tilde)
**drop mu_tilde
** Residuales
gen e= ln_PERCAPITA-mu if FG==1
*las comillas en el r(mean) indican que estoy utilizando una variable macro
sum ethnici1 [iw=FEX_C] if FG==1
local ethnici1= `r(mean)'
sum edumo2 [iw=FEX_C] if FG==1
local edumo2= `r(mean)'
sum edumo3 [iw=FEX_C] if FG==1
local edumo3= `r(mean)'
sum edufa2 [iw=FEX_C] if FG==1
local edufa2= `r(mean)'
sum edufa3 [iw=FEX_C] if FG==1
local edufa3= `r(mean)'
sum female [iw=FEX_C] if FG==1
local female= `r(mean)'
sum birthreg2 [iw=FEX_C] if FG==1
local birthreg2 = `r(mean)'
sum birthreg3 [iw=FEX_C] if FG==1
local birthreg3= `r(mean)'
sum desplazado1 [iw=FEX_C] if FG==1
local desplazado1= `r(mean)'
mat C = (`ethnici1', `edumo2', `edumo3', `edufa2', `edufa3', `female', `birthreg2', `birthreg3' , `desplazado1', 1)
mat list C
mat v= C*b
mat list v
gen v= `ethnici1'*b[1,1] +`edumo2'*b[2,1] +`edumo3'*b[3,1] + `edufa2'*b[4,1] + `edufa3'*b[5,1] + `female'*b[6,1] + `birthreg2'*b[7,1] + `birthreg3'b[8,1] + `desplazado1'*b[9,1] + b[10, 1]
sum v
gen vtilde= exp(v + e) if FG==1
gen ln_vtilde =ln(vtilde)
sum(PERCAPITA) [iw=FEX_C] if FG==1
local sum_percapita= r(sum)
local mean_percapita= r(mean)
**Generar ln del mean anterior
local ln_meanpercapita= ln(`mean_percapita')
**Logaritmo de los ingresos percapita de cada uno de los indviduos
sum (ln_PERCAPITA) [iw=FEX_C] if FG==1
local sum_lnpercapita= r(sum)
local sum_wlnpercapita= r(sum_w)
local mld= `ln_meanpercapita' - (`sum_lnpercapita'/`sum_wlnpercapita')
gen mld= `ln_meanpercapita' - (`sum_lnpercapita'/`sum_wlnpercapita')
dis `mld'
** ineqdeco PERCAPITA [iw=FEX_C] if FG==1
**Smoothed distribution
sum(mu_tilde) [iw=FEX_C] if FG==1
local sum_mutilde= r(sum)
local mean_mutilde= r(mean)
**Generar ln del mean anterior
local ln_meanmutilde= ln(`mean_mutilde')
sum (ln_mutilde) [iw=FEX_C] if FG==1
local sum_lnmutilde= r(sum)
local sum_wlnmutilde= r(sum_w)
local mldsmooth= `ln_meanmutilde' - (`sum_lnmutilde'/`sum_wlnmutilde')
gen mldsmooth= `ln_meanmutilde' - (`sum_lnmutilde'/`sum_wlnmutilde')
dis `mldsmooth'
*Standardized distribution
sum(vtilde) [iw=FEX_C] if FG==1
local sum_vtilde= r(sum)
local mean_vtilde= r(mean)
**Generar ln del mean anterior
local ln_meanvtilde= ln(`mean_vtilde')
sum (ln_vtilde) [iw=FEX_C] if FG==1
local sum_lnvtilde= r(sum)
local sum_wlnvtilde= r(sum_w)
local mldstandar= `ln_meanvtilde' - (`sum_lnvtilde'/`sum_wlnvtilde')
gen mldstandar= `ln_meanvtilde' - (`sum_lnvtilde'/`sum_wlnvtilde')
dis `mldstandar'
** IOL (θa) ecuación (10) y (10')
gen iol= mldsmooth
gen iol_std= mld - mldstandar
**IOR (θa) ecuación (11) y (11')
gen ior= mldsmooth/mld
gen ior_std= 1 - mldstandar/ mld
The error is:
gen v= `ethnici1'*b[1,1] +`edumo2'*b[2,1] +`edumo3'*b[3,1] + `edufa2'*b[4,1] + `edufa3'*b[5,1] + `female'*b[6,1] + `birthreg2'*b
> [7,1] + `birthreg3'b[8,1] + `desplazado1'*b[9,1] + b[10, 1]
.5178703044404551b invalid name
Can anyone help me figuring out the mistake?
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