storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
county int %9.0g county identifier
year byte %9.0g 81 to 87
crmrte float %9.0g crimes committed per person
prbarr float %9.0g 'probability' of arrest
prbconv float %9.0g 'probability' of conviction
prbpris float %9.0g 'probability' of prison sentenc
avgsen float %9.0g avg. sentence, days
polpc float %9.0g police per capita
density float %9.0g people per sq. mile
taxpc float %9.0g tax revenue per capita
west byte %9.0g =1 if in western N.C.
central byte %9.0g =1 if in central N.C.
urban byte %9.0g =1 if in SMSA
pctmin80 float %9.0g perc. minority, 1980
wcon float %9.0g weekly wage, construction
wtuc float %9.0g wkly wge, trns, util, commun
wtrd float %9.0g wkly wge, whlesle, retail trade
wfir float %9.0g wkly wge, fin, ins, real est
wser float %9.0g wkly wge, service industry
wmfg float %9.0g wkly wge, manufacturing
wfed float %9.0g wkly wge, fed employees
wsta float %9.0g wkly wge, state employees
wloc float %9.0g wkly wge, local gov emps
mix float %9.0g offense mix: face-to-face/other
pctymle float %9.0g percent young male
d82 byte %9.0g =1 if year == 82
d83 byte %9.0g =1 if year == 83
d84 byte %9.0g =1 if year == 84
d85 byte %9.0g =1 if year == 85
d86 byte %9.0g =1 if year == 86
d87 byte %9.0g =1 if year == 87
lcrmrte float %9.0g log(crmrte)
lprbarr float %9.0g log(prbarr)
lprbconv float %9.0g log(prbconv)
lprbpris float %9.0g log(prbpris)
lavgsen float %9.0g log(avgsen)
lpolpc float %9.0g log(polpc)
ldensity float %9.0g log(density)
ltaxpc float %9.0g log(taxpc)
lwcon float %9.0g log(wcon)
lwtuc float %9.0g log(wtuc)
lwtrd float %9.0g log(wtrd)
lwfir float %9.0g log(wfir)
lwser float %9.0g log(wser)
lwmfg float %9.0g log(wmfg)
lwfed float %9.0g log(wfed)
lwsta float %9.0g log(wsta)
lwloc float %9.0g log(wloc)
lmix float %9.0g log(mix)
lpctymle float %9.0g log(pctymle)
lpctmin float %9.0g log(pctmin)
clcrmrte float %9.0g lcrmrte - lcrmrte[_n-1]
clprbarr float %9.0g lprbarr - lprbarr[_n-1]
clprbcon float %9.0g lprbconv - lprbconv[_n-1]
clprbpri float %9.0g lprbpri - lprbpri[t-1]
clavgsen float %9.0g lavgsen - lavgsen[t-1]
clpolpc float %9.0g lpolpc - lpolpc[t-1]
cltaxpc float %9.0g ltaxpc - ltaxpc[t-1]
clmix float %9.0g lmix - lmix[t-1]
Estimoi lineaarinen malli POLS estimaattorilla (pooled ordinary least squares) käyttäen kaikkia vuosia 81-87, kun mallissa on mukana koostettu aikavaikutus (aggregate time effect). Käytä indikaattorimuuttujia (dummy variables) koostetun aikavaikutuksen estimoimiseen. Mallissa vastemuuttuja on log(crmrte) ja aikavaikutuksen lisäksi selittävät muuttujat ovat log(prbarr), log(prbconv), log(prbpris), log(avgsen) ja log(polpc).
file<-"http://cc.oulu.fi/~jklemela/panel/cornwell.raw"
data<-read.table(file=file)
y<-log(data[,3])
x1<-log(data[,4])
x2<-log(data[,5])
x3<-log(data[,6])
x4<-log(data[,7])
x5<-log(data[,8])
year<-data[,2]
d1<-as.numeric(year==81)
d2<-as.numeric(year==82)
d3<-as.numeric(year==83)
d4<-as.numeric(year==84)
d5<-as.numeric(year==85)
d6<-as.numeric(year==86)
d7<-as.numeric(year==87)
reg.model<-lm(y ~ d2+d3+d4+d5+d6+d7+x1+x2+x3+x4+x5)
summary(reg.model)
Call:
lm(formula = y ~ d2 + d3 + d4 + d5 + d6 + d7 + x1 + x2 + x3 +
x4 + x5)
Residuals:
Min 1Q Median 3Q Max
-1.89966 -0.18748 0.02896 0.23189 1.31319
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.082303 0.251625 -8.275 7.9e-16 ***
d2 0.005137 0.057931 0.089 0.929370
d3 -0.043503 0.057624 -0.755 0.450575
d4 -0.108753 0.057923 -1.878 0.060914 .
d5 -0.078042 0.058324 -1.338 0.181365
d6 -0.042077 0.057822 -0.728 0.467068
d7 -0.027042 0.056899 -0.475 0.634771
x1 -0.719503 0.036766 -19.570 < 2e-16 ***
x2 -0.545659 0.026368 -20.694 < 2e-16 ***
x3 0.247551 0.067227 3.682 0.000251 ***
x4 -0.086755 0.057920 -1.498 0.134686
x5 0.365988 0.030025 12.189 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3789 on 618 degrees of freedom
Multiple R-squared: 0.57, Adjusted R-squared: 0.5624
F-statistic: 74.49 on 11 and 618 DF, p-value: < 2.2e-16
---------------------------------------------------------------
reg.model<-lm(y ~ 0+d1+d2+d3+d4+d5+d6+d7+x1+x2+x3+x4+x5)
summary(reg.model)
Call:
lm(formula = y ~ 0 + d1 + d2 + d3 + d4 + d5 + d6 + d7 + x1 +
x2 + x3 + x4 + x5)
Residuals:
Min 1Q Median 3Q Max
-1.89966 -0.18748 0.02896 0.23189 1.31319
Coefficients:
Estimate Std. Error t value Pr(>|t|)
d1 -2.08230 0.25162 -8.275 7.9e-16 ***
d2 -2.07717 0.24508 -8.476 < 2e-16 ***
d3 -2.12581 0.24544 -8.661 < 2e-16 ***
d4 -2.19106 0.24265 -9.030 < 2e-16 ***
d5 -2.16035 0.24238 -8.913 < 2e-16 ***
d6 -2.12438 0.24582 -8.642 < 2e-16 ***
d7 -2.10935 0.24814 -8.501 < 2e-16 ***
x1 -0.71950 0.03677 -19.570 < 2e-16 ***
x2 -0.54566 0.02637 -20.694 < 2e-16 ***
x3 0.24755 0.06723 3.682 0.000251 ***
x4 -0.08676 0.05792 -1.498 0.134686
x5 0.36599 0.03003 12.189 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3789 on 618 degrees of freedom
Multiple R-squared: 0.9895, Adjusted R-squared: 0.9892
F-statistic: 4831 on 12 and 618 DF, p-value: < 2.2e-16
Estimoi matriisi Omega = Euiui', kun ui on T kertaa 1 vektori, joka sisältää residuaalit lineaarisesta mallista, kun vastemuuttuja on log(crmrte) ja selittävät muuttujat ovat log(prbarr), log(prbconv), log(prbpris), log(avgsen) ja log(polpc). Kaikki vuodet 81-87 sisältyvät malliin. Käytä POLS-residuaaleja
y<-log(data[,3])
x1<-log(data[,4])
x2<-log(data[,5])
x3<-log(data[,6])
x4<-log(data[,7])
x5<-log(data[,8])
reg.model<-lm(y ~ x1+x2+x3+x4+x5)
summary(reg.model)
resi<-residuals(reg.model)
county<-data[,1]
year<-data[,2]
uc<-unique(county)
uy<-unique(year)
N<-length(uc)
T<-length(uy)
U<-matrix(0,T,N)
for (i in 1:N){
now<-uc[i]
ind<-(county==now)
U[,i]<-resi[ind]
}
omegahat<-U%*%t(U)/N
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.13295214 0.10691554 0.08725805 0.09418204 0.09834043 0.07883118
[2,] 0.10691554 0.13246877 0.09984562 0.10790282 0.10569842 0.08345536
[3,] 0.08725805 0.09984562 0.12093801 0.10635122 0.10554241 0.08992647
[4,] 0.09418204 0.10790282 0.10635122 0.15925729 0.12980230 0.10205059
[5,] 0.09834043 0.10569842 0.10554241 0.12980230 0.16121446 0.10989033
[6,] 0.07883118 0.08345536 0.08992647 0.10205059 0.10989033 0.12945747
[7,] 0.09169674 0.10168747 0.08737220 0.10057430 0.10401350 0.10791154
[,7]
[1,] 0.09169674
[2,] 0.10168747
[3,] 0.08737220
[4,] 0.10057430
[5,] 0.10401350
[6,] 0.10791154
[7,] 0.15928928
--------------------------------------------------
y<-matrix(y,length(y),1)
K<-6
x<-matrix(1,length(y),K)
x[,2]<-x1
x[,3]<-x2
x[,4]<-x3
x[,5]<-x4
x[,6]<-x5
I<-diag(1,K)
xtxinv<-solve(t(x)%*%x,I)
betahat<-xtxinv%*%t(x)%*%y
county<-data[,1]
year<-data[,2]
uc<-unique(county)
uy<-unique(year)
N<-length(uc)
T<-length(uy)
U<-matrix(0,T,N)
for (i in 1:N){
now<-uc[i]
ind<-(county==now)
yi<-y[ind]
xi<-x[ind,]
U[,i]<-yi-xi%*%betahat
}
omegahat<-U%*%t(U)/N
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.13295214 0.10691554 0.08725805 0.09418204 0.09834043 0.07883118
[2,] 0.10691554 0.13246877 0.09984562 0.10790282 0.10569842 0.08345536
[3,] 0.08725805 0.09984562 0.12093801 0.10635122 0.10554241 0.08992647
[4,] 0.09418204 0.10790282 0.10635122 0.15925729 0.12980230 0.10205059
[5,] 0.09834043 0.10569842 0.10554241 0.12980230 0.16121446 0.10989033
[6,] 0.07883118 0.08345536 0.08992647 0.10205059 0.10989033 0.12945747
[7,] 0.09169674 0.10168747 0.08737220 0.10057430 0.10401350 0.10791154
[,7]
[1,] 0.09169674
[2,] 0.10168747
[3,] 0.08737220
[4,] 0.10057430
[5,] 0.10401350
[6,] 0.10791154
[7,] 0.15928928
Estimoi matriisi Omega = Euiui', kun ui on T kertaa 1 vektori, joka sisältää residuaalit lineaarisesta mallista, kun vastemuuttuja on log(crmrte) ja selittävät muuttujat ovat log(prbarr), log(prbconv), log(prbpris), log(avgsen) ja log(polpc). Kaikki vuodet 81-87 sisältyvät malliin. Käytä POLS-residuaaleja
y<-matrix(y,length(y),1)
K<-6
x<-matrix(1,length(y),K)
x[,2]<-x1
x[,3]<-x2
x[,4]<-x3
x[,5]<-x4
x[,6]<-x5
I<-diag(1,K)
xtxinv<-solve(t(x)%*%x,I)
betahat<-xtxinv%*%t(x)%*%y
county<-data[,1]
year<-data[,2]
uc<-unique(county)
uy<-unique(year)
N<-length(uc)
T<-length(uy)
U<-matrix(0,T,N)
for (i in 1:N){
now<-uc[i]
ind<-(county==now)
yi<-y[ind]
xi<-x[ind,]
U[,i]<-yi-xi%*%betahat
}
sigmav2<-sum(U^2)/(N*T)
sigmac2<-0
for (i in 1:N){
for (t in 1:(T-1)){
for (s in (t+1):T){
sigmac2<-sigmac2+U[t,i]*U[s,i]
}
}
}
sigmac2<-2*sigmac2/(N*T*(T-1))
sigmau2<-sigmav2-sigmac2
omegahat.re<-sigmav2*diag(1,T)+sigmac2*matrix(1,T,T)
omegahat.re
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.24218956 0.09996421 0.09996421 0.09996421 0.09996421 0.09996421
[2,] 0.09996421 0.24218956 0.09996421 0.09996421 0.09996421 0.09996421
[3,] 0.09996421 0.09996421 0.24218956 0.09996421 0.09996421 0.09996421
[4,] 0.09996421 0.09996421 0.09996421 0.24218956 0.09996421 0.09996421
[5,] 0.09996421 0.09996421 0.09996421 0.09996421 0.24218956 0.09996421
[6,] 0.09996421 0.09996421 0.09996421 0.09996421 0.09996421 0.24218956
[7,] 0.09996421 0.09996421 0.09996421 0.09996421 0.09996421 0.09996421
[,7]
[1,] 0.09996421
[2,] 0.09996421
[3,] 0.09996421
[4,] 0.09996421
[5,] 0.09996421
[6,] 0.09996421
[7,] 0.24218956