Introductory Econometrics Using R

Also covered using Python and Stata

library(wooldridge)
library(psych)
library(stargazer)
library(car)

Example7.1. Hourly wage equation

Average wage difference between men and women in the sample

wage_fem <- lm(wage ~ female + educ + exper + tenure + 1, data=wage1)
stargazer(wage_fem, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                wage            
## -----------------------------------------------
## female                       -1.811***         
##                               (0.265)          
## educ                         0.572***          
##                               (0.049)          
## exper                         0.025**          
##                               (0.012)          
## tenure                       0.141***          
##                               (0.021)          
## Constant                     -1.568**          
##                               (0.725)          
## -----------------------------------------------
## Observations                    526            
## R2                             0.364           
## Adjusted R2                    0.359           
## Residual Std. Error      2.958 (df = 521)      
## F Statistic           74.398*** (df = 4; 521)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Average wage for men in the sample

wage_fem2 <- lm(wage ~ female + 1, data=wage1)
stargazer(wage_fem2,type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                wage            
## -----------------------------------------------
## female                       -2.512***         
##                               (0.303)          
## Constant                     7.099***          
##                               (0.210)          
## -----------------------------------------------
## Observations                    526            
## R2                             0.116           
## Adjusted R2                    0.114           
## Residual Std. Error      3.476 (df = 524)      
## F Statistic           68.537*** (df = 1; 524)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Average wage for men and women in the sample

aggregate(wage1$wage,list(wage1$female), mean )

##   Group.1        x
## 1       0 7.099489
## 2       1 4.587659

Example 7.2. Effect of computer ownership on collage GPA

colGPA_ur <- lm(colGPA ~ PC + hsGPA + ACT + 1, data=gpa1)
stargazer(colGPA_ur, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               colGPA           
## -----------------------------------------------
## PC                           0.157***          
##                               (0.057)          
## hsGPA                        0.447***          
##                               (0.094)          
## ACT                            0.009           
##                               (0.011)          
## Constant                     1.264***          
##                               (0.333)          
## -----------------------------------------------
## Observations                    141            
## R2                             0.219           
## Adjusted R2                    0.202           
## Residual Std. Error      0.333 (df = 137)      
## F Statistic           12.834*** (df = 3; 137)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

linearHypothesis(colGPA_ur, c("hsGPA = 0", "ACT=0"))

## Linear hypothesis test
## 
## Hypothesis:
## hsGPA = 0
## ACT = 0
## 
## Model 1: restricted model
## Model 2: colGPA ~ PC + hsGPA + ACT + 1
## 
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    139 18.436                                  
## 2    137 15.149  2    3.2873 14.865 1.437e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

colGPA_r <- lm(colGPA ~ PC + 1, data=gpa1)
stargazer(colGPA_r, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               colGPA           
## -----------------------------------------------
## PC                           0.170***          
##                               (0.063)          
## Constant                     2.989***          
##                               (0.040)          
## -----------------------------------------------
## Observations                    141            
## R2                             0.050           
## Adjusted R2                    0.043           
## Residual Std. Error      0.364 (df = 139)      
## F Statistic           7.314*** (df = 1; 139)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

aggregate(gpa1$colGPA,list(gpa1$PC), mean )

##   Group.1        x
## 1       0 2.989412
## 2       1 3.158929

Example 7.3. Effect of Training Grants on hours of training

jtrain88 <- subset(jtrain, jtrain$year==1988)
jobb_reg <- lm(hrsemp ~ grant + lsales + lemploy + 1, data=jtrain88)
stargazer(jobb_reg, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               hrsemp           
## -----------------------------------------------
## grant                        26.254***         
##                               (5.592)          
## lsales                        -0.985           
##                               (3.540)          
## lemploy                       -6.070           
##                               (3.883)          
## Constant                      46.665           
##                              (43.412)          
## -----------------------------------------------
## Observations                    105            
## R2                             0.237           
## Adjusted R2                    0.214           
## Residual Std. Error      24.380 (df = 101)     
## F Statistic           10.444*** (df = 3; 101)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Example 7.4. Housing price regression

hrpice_reg <- lm(lprice ~ llotsize + lsqrft  + bdrms + colonial + 1, data=hprice1)
stargazer(hrpice_reg, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               lprice           
## -----------------------------------------------
## llotsize                     0.168***          
##                               (0.038)          
## lsqrft                       0.707***          
##                               (0.093)          
## bdrms                          0.027           
##                               (0.029)          
## colonial                       0.054           
##                               (0.045)          
## Constant                     -1.350**          
##                               (0.651)          
## -----------------------------------------------
## Observations                    88             
## R2                             0.649           
## Adjusted R2                    0.632           
## Residual Std. Error       0.184 (df = 83)      
## F Statistic           38.378*** (df = 4; 83)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Example7.5. Hourly wage equation

wage_reg <- lm(lwage ~ female + educ  + exper + expersq + tenure + tenursq + 1, data=wage1)
stargazer(wage_reg, type="text", single.row=TRUE, no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                lwage           
## -----------------------------------------------
## female                   -0.297*** (0.036)     
## educ                     0.080*** (0.007)      
## exper                    0.029*** (0.005)      
## expersq                 -0.001*** (0.0001)     
## tenure                   0.032*** (0.007)      
## tenursq                  -0.001** (0.0002)     
## Constant                 0.417*** (0.099)      
## -----------------------------------------------
## Observations                    526            
## R2                             0.441           
## Adjusted R2                    0.434           
## Residual Std. Error      0.400 (df = 519)      
## F Statistic           68.177*** (df = 6; 519)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

level_fem <- exp(wage_reg$coef[2]) - 1
level_fem

##     female 
## -0.2565925

Example 7.6. Hourly wage equation

marrmale <- (wage1$female==0 & wage1$married==1)
marrfem <- (wage1$female==1 & wage1$married==1)
singfem <- (wage1$female==1 & wage1$married==0)
singmale <- (wage1$female==0 & wage1$married==0)

wage_sf = lm(lwage ~ marrmale + marrfem + singfem + educ  + exper + expersq + tenure + tenursq + 1, data=wage1)
stargazer(wage_sf, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                lwage           
## -----------------------------------------------
## marrmale                 0.213*** (0.055)      
## marrfem                  -0.198*** (0.058)     
## singfem                  -0.110** (0.056)      
## educ                     0.079*** (0.007)      
## exper                    0.027*** (0.005)      
## expersq                 -0.001*** (0.0001)     
## tenure                   0.029*** (0.007)      
## tenursq                  -0.001** (0.0002)     
## Constant                 0.321*** (0.100)      
## -----------------------------------------------
## Observations                    526            
## R2                             0.461           
## Adjusted R2                    0.453           
## Residual Std. Error      0.393 (df = 517)      
## F Statistic           55.246*** (df = 8; 517)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

wage_sm = lm(lwage ~ marrmale + marrfem + singmale + educ  + exper + expersq + tenure + tenursq + 1, data=wage1)
stargazer(wage_sm, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                lwage           
## -----------------------------------------------
## marrmale                 0.323*** (0.050)      
## marrfem                   -0.088* (0.052)      
## singmale                  0.110** (0.056)      
## educ                     0.079*** (0.007)      
## exper                    0.027*** (0.005)      
## expersq                 -0.001*** (0.0001)     
## tenure                   0.029*** (0.007)      
## tenursq                  -0.001** (0.0002)     
## Constant                  0.211** (0.097)      
## -----------------------------------------------
## Observations                    526            
## R2                             0.461           
## Adjusted R2                    0.453           
## Residual Std. Error      0.393 (df = 517)      
## F Statistic           55.246*** (df = 8; 517)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Example 7.7. Effects of physical attractiveness on wage

beauty0<-subset(beauty, beauty$female==0)
beauty_reg0 <- lm(lwage ~ belavg + abvavg + educ  + exper + expersq + union + married + black + south + goodhlth + 1, data=beauty0)
stargazer(beauty_reg0, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                lwage           
## -----------------------------------------------
## belavg                   -0.165*** (0.053)     
## abvavg                    -0.025 (0.038)       
## educ                     0.061*** (0.007)      
## exper                    0.046*** (0.006)      
## expersq                 -0.001*** (0.0001)     
## union                    0.149*** (0.036)      
## married                    0.064 (0.044)       
## black                    -0.257*** (0.076)     
## south                     0.085** (0.043)      
## goodhlth                   0.001 (0.070)       
## Constant                 0.478*** (0.120)      
## -----------------------------------------------
## Observations                    824            
## R2                             0.255           
## Adjusted R2                    0.246           
## Residual Std. Error      0.469 (df = 813)      
## F Statistic          27.819*** (df = 10; 813)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

beauty1<-subset(beauty, beauty$female==1)

beauty_reg1 <- lm(lwage ~ belavg + abvavg + educ  + exper + expersq + union + married + black + south + goodhlth + 1, data=beauty1)

stargazer(beauty_reg1, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                lwage           
## -----------------------------------------------
## belavg                    -0.114* (0.066)      
## abvavg                     0.069 (0.049)       
## educ                     0.076*** (0.009)      
## exper                    0.029*** (0.007)      
## expersq                 -0.0005*** (0.0002)    
## union                    0.293*** (0.054)      
## married                   -0.062 (0.044)       
## black                     0.144** (0.069)      
## south                      0.008 (0.060)       
## goodhlth                   0.113 (0.081)       
## Constant                  -0.077 (0.144)       
## -----------------------------------------------
## Observations                    436            
## R2                             0.278           
## Adjusted R2                    0.261           
## Residual Std. Error      0.451 (df = 425)      
## F Statistic          16.399*** (df = 10; 425)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Example 7.8. Effects of law school rankings on starting salaries

r61_100 <- (lawsch85$rank>60 & lawsch85$rank<=100)
lawsch85 <- cbind(lawsch85, r61_100)
lawsch85_reg = lm(lsalary ~ top10 + r11_25  + r26_40 + r41_60 + r61_100 + LSAT + GPA + llibvol + lcost + 1, data=lawsch85)
stargazer(lawsch85_reg, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               lsalary          
## -----------------------------------------------
## top10                    0.700*** (0.053)      
## r11_25                   0.594*** (0.039)      
## r26_40                   0.375*** (0.034)      
## r41_60                   0.263*** (0.028)      
## r61_100                  0.132*** (0.021)      
## LSAT                      0.006* (0.003)       
## GPA                        0.014 (0.074)       
## llibvol                    0.036 (0.026)       
## lcost                      0.001 (0.025)       
## Constant                 9.165*** (0.411)      
## -----------------------------------------------
## Observations                    136            
## R2                             0.911           
## Adjusted R2                    0.905           
## Residual Std. Error      0.086 (df = 126)      
## F Statistic          143.199*** (df = 9; 126)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

linearHypothesis (lawsch85_reg, c("LSAT = 0",  "GPA = 0", "llibvol = 0", "lcost = 0"))

## Linear hypothesis test
## 
## Hypothesis:
## LSAT = 0
## GPA = 0
## llibvol = 0
## lcost = 0
## 
## Model 1: restricted model
## Model 2: lsalary ~ top10 + r11_25 + r26_40 + r41_60 + r61_100 + LSAT + 
##     GPA + llibvol + lcost + 1
## 
##   Res.Df     RSS Df Sum of Sq      F Pr(>F)  
## 1    130 0.99409                             
## 2    126 0.92411  4  0.069978 2.3853 0.0547 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Example 7.9. Effects of computer usage on wages

Data not available

Example 7.10. Log hourly wage equation

wage_reg <- lm(lwage ~ female*educ  + exper + expersq + tenure + tenursq + 1, data=wage1)
stargazer(wage_reg, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                lwage           
## -----------------------------------------------
## female                    -0.227 (0.168)       
## educ                     0.082*** (0.008)      
## exper                    0.029*** (0.005)      
## expersq                 -0.001*** (0.0001)     
## tenure                   0.032*** (0.007)      
## tenursq                  -0.001** (0.0002)     
## female:educ               -0.006 (0.013)       
## Constant                 0.389*** (0.119)      
## -----------------------------------------------
## Observations                    526            
## R2                             0.441           
## Adjusted R2                    0.433           
## Residual Std. Error      0.400 (df = 518)      
## F Statistic           58.371*** (df = 7; 518)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

linearHypothesis (wage_reg, c("female = 0", "female:educ = 0"))

## Linear hypothesis test
## 
## Hypothesis:
## female = 0
## female:educ = 0
## 
## Model 1: restricted model
## Model 2: lwage ~ female * educ + exper + expersq + tenure + tenursq + 
##     1
## 
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    520 93.911                                  
## 2    518 82.922  2     10.99 34.325 1.002e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Example 7.11. Effects of race on baseball player salaries

mlb1<-subset(mlb1, mlb1$percblck!=0)
mlb1_reg <- lm(lsalary ~ years + gamesyr + bavg + hrunsyr + rbisyr + runsyr + fldperc + allstar + black + hispan + black:percblck + hispan:perchisp  + 1, data=mlb1)
stargazer(mlb1_reg, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               lsalary          
## -----------------------------------------------
## years                    0.067*** (0.013)      
## gamesyr                  0.009*** (0.003)      
## bavg                       0.001 (0.002)       
## hrunsyr                    0.015 (0.016)       
## rbisyr                     0.004 (0.008)       
## runsyr                     0.007 (0.005)       
## fldperc                    0.001 (0.002)       
## allstar                  0.008*** (0.003)      
## black                     -0.198 (0.125)       
## hispan                    -0.190 (0.153)       
## black:percblck            0.012** (0.005)      
## hispan:perchisp           0.020** (0.010)      
## Constant                 10.344*** (2.183)     
## -----------------------------------------------
## Observations                    330            
## R2                             0.638           
## Adjusted R2                    0.624           
## Residual Std. Error      0.713 (df = 317)      
## F Statistic          46.482*** (df = 12; 317)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

linearHypothesis (mlb1_reg, c("black = 0","hispan = 0","hispan:perchisp = 0", "black:percblck = 0"))

## Linear hypothesis test
## 
## Hypothesis:
## black = 0
## hispan = 0
## hispan:perchisp = 0
## black:percblck = 0
## 
## Model 1: restricted model
## Model 2: lsalary ~ years + gamesyr + bavg + hrunsyr + rbisyr + runsyr + 
##     fldperc + allstar + black + hispan + black:percblck + hispan:perchisp + 
##     1
## 
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    321 166.67                              
## 2    317 161.28  4    5.3886 2.6479 0.03348 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mlb1_reg_r <- lm(lsalary ~ years + gamesyr + bavg + hrunsyr + rbisyr + runsyr + fldperc + allstar + 1, data=mlb1)
stargazer(mlb1_reg_r, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               lsalary          
## -----------------------------------------------
## years                    0.067*** (0.013)      
## gamesyr                   0.009** (0.003)      
## bavg                       0.001 (0.002)       
## hrunsyr                    0.014 (0.017)       
## rbisyr                     0.005 (0.008)       
## runsyr                    0.008* (0.005)       
## fldperc                    0.001 (0.002)       
## allstar                   0.007** (0.003)      
## Constant                 10.637*** (2.199)     
## -----------------------------------------------
## Observations                    330            
## R2                             0.626           
## Adjusted R2                    0.616           
## Residual Std. Error      0.721 (df = 321)      
## F Statistic           67.023*** (df = 8; 321)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Equation [7.22] (Page 222)

gpa3 <- subset(gpa3, gpa3$spring==1)
gpa3_reg <- lm(cumgpa ~ female*sat + hsperc + female:hsperc + tothrs + female:tothrs + 1, data=gpa3)
stargazer(gpa3_reg, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               cumgpa           
## -----------------------------------------------
## female                    -0.353 (0.411)       
## sat                      0.001*** (0.0002)     
## hsperc                   -0.008*** (0.001)     
## tothrs                   0.002*** (0.001)      
## female:sat                0.001* (0.0004)      
## female:hsperc             -0.001 (0.003)       
## female:tothrs             -0.0001 (0.002)      
## Constant                 1.481*** (0.207)      
## -----------------------------------------------
## Observations                    366            
## R2                             0.406           
## Adjusted R2                    0.394           
## Residual Std. Error      0.468 (df = 358)      
## F Statistic           34.946*** (df = 7; 358)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

linearHypothesis (gpa3_reg, c("female:tothrs = 0",  "female:sat = 0", "female:hsperc = 0"))

## Linear hypothesis test
## 
## Hypothesis:
## female:tothrs = 0
## female:sat = 0
## female:hsperc = 0
## 
## Model 1: restricted model
## Model 2: cumgpa ~ female * sat + hsperc + female:hsperc + tothrs + female:tothrs + 
##     1
## 
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    361 79.362                           
## 2    358 78.355  3    1.0072 1.5339 0.2054

Equation [7.25] (Page 224)

gpa3_reg_r <- lm(cumgpa ~ female + sat + hsperc + tothrs + 1, data=gpa3)
stargazer(gpa3_reg_r, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               cumgpa           
## -----------------------------------------------
## female                       0.310***          
##                               (0.059)          
## sat                          0.001***          
##                              (0.0002)          
## hsperc                       -0.008***         
##                               (0.001)          
## tothrs                       0.002***          
##                               (0.001)          
## Constant                     1.329***          
##                               (0.180)          
## -----------------------------------------------
## Observations                    366            
## R2                             0.398           
## Adjusted R2                    0.392           
## Residual Std. Error      0.469 (df = 361)      
## F Statistic           59.739*** (df = 4; 361)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Equation [7.29] (Page 225)

mroz_reg <- lm(inlf ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6 + 1, data=mroz)
stargazer(mroz_reg, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                inlf            
## -----------------------------------------------
## nwifeinc                 -0.003** (0.001)      
## educ                     0.038*** (0.007)      
## exper                    0.039*** (0.006)      
## expersq                 -0.001*** (0.0002)     
## age                      -0.016*** (0.002)     
## kidslt6                  -0.262*** (0.034)     
## kidsge6                    0.013 (0.013)       
## Constant                 0.586*** (0.154)      
## -----------------------------------------------
## Observations                    753            
## R2                             0.264           
## Adjusted R2                    0.257           
## Residual Std. Error      0.427 (df = 745)      
## F Statistic           38.218*** (df = 7; 745)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Example7.12. A linear probability model of arrest

arr86 <- (crime1$narr86 > 0)
crime1 <- cbind(crime1, arr86)
crime_reg <- lm(arr86 ~ pcnv + avgsen + tottime + ptime86 + qemp86 + 1, data=crime1)
stargazer(crime_reg, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                arr86           
## -----------------------------------------------
## pcnv                         -0.162***         
##                               (0.021)          
## avgsen                         0.006           
##                               (0.006)          
## tottime                       -0.002           
##                               (0.005)          
## ptime86                      -0.022***         
##                               (0.005)          
## qemp86                       -0.043***         
##                               (0.005)          
## Constant                     0.441***          
##                               (0.017)          
## -----------------------------------------------
## Observations                   2,725           
## R2                             0.047           
## Adjusted R2                    0.046           
## Residual Std. Error      0.437 (df = 2719)     
## F Statistic          27.030*** (df = 5; 2719)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

linearHypothesis (crime_reg, c("avgsen = 0",  "tottime = 0"))

## Linear hypothesis test
## 
## Hypothesis:
## avgsen = 0
## tottime = 0
## 
## Model 1: restricted model
## Model 2: arr86 ~ pcnv + avgsen + tottime + ptime86 + qemp86 + 1
## 
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1   2721 520.38                           
## 2   2719 519.97  2   0.40531 1.0597 0.3467

Equation [7.32] (Page 228)

crime_reg_2 <- lm(arr86 ~ pcnv + avgsen + tottime + ptime86 + qemp86 + black + hispan + 1, data=crime1)
stargazer(crime_reg_2, type="text", no.space=TRUE, single.row=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                arr86           
## -----------------------------------------------
## pcnv                     -0.152*** (0.021)     
## avgsen                     0.005 (0.006)       
## tottime                   -0.003 (0.005)       
## ptime86                  -0.024*** (0.005)     
## qemp86                   -0.038*** (0.005)     
## black                    0.170*** (0.024)      
## hispan                   0.096*** (0.021)      
## Constant                 0.380*** (0.019)      
## -----------------------------------------------
## Observations                   2,725           
## R2                             0.068           
## Adjusted R2                    0.066           
## Residual Std. Error      0.433 (df = 2717)     
## F Statistic          28.405*** (df = 7; 2717)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Equation [7.33] (Page 229)

jtrain <- subset(jtrain, jtrain$year==1988)
jobb_reg <- lm(lscrap ~ grant + lsales + lemploy + 1, data=jtrain)
stargazer(jobb_reg, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                               lscrap           
## -----------------------------------------------
## grant                         -0.052           
##                               (0.431)          
## lsales                        -0.455           
##                               (0.373)          
## lemploy                       0.639*           
##                               (0.365)          
## Constant                       4.987           
##                               (4.656)          
## -----------------------------------------------
## Observations                    50             
## R2                             0.072           
## Adjusted R2                    0.011           
## Residual Std. Error       1.385 (df = 46)      
## F Statistic             1.182 (df = 3; 46)     
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Equation [7.35] (Page 231)

fert_reg <- lm(children ~ age + educ + 1, data=fertil2)
stargazer(fert_reg, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                              children          
## -----------------------------------------------
## age                          0.175***          
##                               (0.003)          
## educ                         -0.090***         
##                               (0.006)          
## Constant                     -1.997***         
##                               (0.094)          
## -----------------------------------------------
## Observations                   4,361           
## R2                             0.560           
## Adjusted R2                    0.559           
## Residual Std. Error      1.475 (df = 4358)     
## F Statistic         2,767.702*** (df = 2; 4358)
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Equation [7.37] (Page 232)

fert_reg_2 <- lm(children ~ age + educ + electric + 1, data=fertil2)
stargazer(fert_reg_2, type="text", no.space=TRUE, align=TRUE)

## 
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                              children          
## -----------------------------------------------
## age                          0.177***          
##                               (0.003)          
## educ                         -0.079***         
##                               (0.006)          
## electric                     -0.362***         
##                               (0.068)          
## Constant                     -2.071***         
##                               (0.095)          
## -----------------------------------------------
## Observations                   4,358           
## R2                             0.562           
## Adjusted R2                    0.562           
## Residual Std. Error      1.471 (df = 4354)     
## F Statistic         1,862.831*** (df = 3; 4354)
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Introductory Econometrics Using R

Chapter 7. Multiple Regression Analysis with Qualitative Information

Example7.1. Hourly wage equation

Example 7.2. Effect of computer ownership on collage GPA

Example 7.3. Effect of Training Grants on hours of training

Example 7.4. Housing price regression

Example7.5. Hourly wage equation

Example 7.6. Hourly wage equation

Example 7.7. Effects of physical attractiveness on wage

Example 7.8. Effects of law school rankings on starting salaries

Example 7.9. Effects of computer usage on wages

Example 7.10. Log hourly wage equation

Example 7.11. Effects of race on baseball player salaries

Equation [7.22] (Page 222)

Equation [7.25] (Page 224)

Equation [7.29] (Page 225)

Example7.12. A linear probability model of arrest

Equation [7.32] (Page 228)

Equation [7.33] (Page 229)

Equation [7.35] (Page 231)

Equation [7.37] (Page 232)