vignettes/chapter-03.Rmd
chapter-03.Rmdlibrary(hayashir)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(stargazer)
#>
#> Please cite as:
#> Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
#> R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
library(AER)
#> Loading required package: car
#> Loading required package: carData
#>
#> Attaching package: 'car'
#> The following object is masked from 'package:dplyr':
#>
#> recode
#> Loading required package: lmtest
#> Loading required package: zoo
#>
#> Attaching package: 'zoo'
#> The following objects are masked from 'package:base':
#>
#> as.Date, as.Date.numeric
#>
#> Attaching package: 'lmtest'
#> The following object is masked from 'package:hayashir':
#>
#> moneydemand
#> Loading required package: sandwich
#> Loading required package: survivalstr(griliches)
#> Classes 'tbl_df', 'tbl' and 'data.frame': 758 obs. of 20 variables:
#> $ southern_residence : num 0 0 0 0 0 0 0 0 0 0 ...
#> $ southern_residence_80: num 0 0 0 0 0 0 0 0 0 0 ...
#> $ married : num 0 0 0 0 1 0 1 1 1 1 ...
#> $ married_80 : num 1 1 1 1 1 0 1 1 1 1 ...
#> $ lives_metro : num 1 1 1 1 1 1 1 0 1 0 ...
#> $ lives_metro_80 : num 1 1 1 1 1 1 1 0 1 0 ...
#> $ mothers_educ : num 8 14 14 12 6 8 8 14 12 13 ...
#> $ iq_score : num 93 119 108 96 74 91 114 111 95 132 ...
#> $ kww_score : num 35 41 46 32 27 24 50 37 44 44 ...
#> $ year : num 68 66 67 66 73 66 73 67 66 73 ...
#> $ age : num 19 23 20 18 26 16 30 23 22 30 ...
#> $ age_80 : num 31 37 33 32 34 30 38 36 36 38 ...
#> $ education : num 12 16 14 12 9 9 18 15 12 18 ...
#> $ education_80 : num 12 18 14 12 11 10 18 15 12 18 ...
#> $ experience : num 0.462 0 0.423 0.333 9.013 ...
#> $ experience_80 : num 10.6 11.4 11 13.1 14.4 ...
#> $ tenure : num 0 2 1 1 3 1 6 1 2 5 ...
#> $ tenure_80 : num 2 16 9 7 5 0 14 1 16 13 ...
#> $ log_wage : num 5.9 5.44 5.71 5.48 5.93 ...
#> $ log_wage_80 : num 6.64 6.69 6.71 6.48 6.33 ...
to_summarize <- griliches %>%
select(age, education, log_wage, kww_score, iq_score, experience)
stargazer(as.data.frame(to_summarize), type = "html")| Statistic | N | Mean | St. Dev. | Min | Pctl(25) | Pctl(75) | Max |
| age | 758 | 21.835 | 2.982 | 16 | 20 | 24 | 30 |
| education | 758 | 13.405 | 2.232 | 9 | 12 | 16 | 18 |
| log_wage | 758 | 5.687 | 0.429 | 4.605 | 5.380 | 5.991 | 7.051 |
| kww_score | 758 | 36.574 | 7.302 | 12 | 32 | 41 | 56 |
| iq_score | 758 | 103.856 | 13.619 | 54 | 95.2 | 113.8 | 145 |
| experience | 758 | 1.735 | 2.106 | 0.000 | 0.282 | 2.440 | 11.444 |
Notice that because we are using the Blackburn-Neumark data rather than copying the data from Griliches directly we get slightly different results.
First we estimate the three models. We again expect slightly different results because (i) we have different data than Griliches; and (ii) the controls are slightly different - due to data availability.
model1 <- lm(log_wage ~ education +
experience + tenure +
southern_residence + lives_metro +
age + I(age^2) + married,
data = griliches)
model2 <- lm(log_wage ~ education + iq_score +
experience + tenure +
southern_residence + lives_metro +
age + I(age^2) + married,
data = griliches)
model3 <- ivreg(log_wage ~ education + iq_score +
experience + tenure +
southern_residence + lives_metro +
age + I(age^2) + married |
. -iq_score + kww_score + mothers_educ,
data = griliches)Let’s put them all into a table, we use Std Errors in parentheses, not t-stats:
stargazer(model1, model2, model3, type = "html",
keep = c("education", "iq_score", "experience",
"tenure")
)| Dependent variable: | |||
| log_wage | |||
| OLS | instrumental | ||
| variable | |||
| (1) | (2) | (3) | |
| education | 0.067*** | 0.054*** | 0.062*** |
| (0.007) | (0.008) | (0.017) | |
| iq_score | 0.004*** | 0.001 | |
| (0.001) | (0.005) | ||
| experience | 0.003 | 0.004 | 0.003 |
| (0.007) | (0.007) | (0.007) | |
| tenure | 0.009 | 0.007 | 0.008 |
| (0.008) | (0.008) | (0.008) | |
| Observations | 758 | 758 | 758 |
| R2 | 0.426 | 0.437 | 0.432 |
| Adjusted R2 | 0.420 | 0.430 | 0.426 |
| Residual Std. Error | 0.327 (df = 749) | 0.324 (df = 748) | 0.325 (df = 748) |
| F Statistic | 69.490*** (df = 8; 749) | 64.450*** (df = 9; 748) | |
| Note: | p<0.1; p<0.05; p<0.01 | ||