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{estout} for R
             Regression Output for *.csv, *.tex Formats

                                  Ben Mazzotta

                                   Fletcher School


                                 August 24, 2010




Ben Mazzotta (Fletcher School)       Estout Demo     August 24, 2010   1 / 11
1     Regression Output




2     {estout}




    Ben Mazzotta (Fletcher School)   Estout Demo   August 24, 2010   2 / 11
Regression




                                          Y = ÎČX + ÎłC +                                        (1)

                                  Figure 1: A generic OLS regression model



Key features
      Multiple speciïŹcations
      α signiïŹcance level
      σÎČ precision of the coeïŹƒcient estimates



 Ben Mazzotta (Fletcher School)                  Estout Demo                 August 24, 2010   3 / 11
Output




                                  Figure 2: Sample outreg output



 Ben Mazzotta (Fletcher School)             Estout Demo            August 24, 2010   4 / 11
1     Regression Output




2     {estout}




    Ben Mazzotta (Fletcher School)   Estout Demo   August 24, 2010   5 / 11
Code I


Preliminaries
library(datasets)
data(freeny)
names(freeny)
names(freeny) <- c("y","lag.rev","price","income", "potential")

Regressions
reg1 <- lm(y ~ lag.rev + price, data=freeny)
reg2 <- lm(y ~ lag.rev + price + income, data=freeny)
reg3 <- lm(y ~ lag.rev + price + income + potential, data=freeny)
Code II


Output descriptive statistics
# Open the estout package
library(estout)

# Write the descriptive statistics
# Specify the objects
descsto(freeny)
# Write to files
desctab(filename="freeny", csv=FALSE)
desctab(filename="freeny", csv=TRUE)




 Ben Mazzotta (Fletcher School)   Estout Demo   August 24, 2010   7 / 11
Code III



Output regression estimates
# Store the objects
eststo(reg1)
eststo(reg2)
eststo(reg3)
# Write to file
esttab(filename="freeny.reg", csv=FALSE)
esttab(filename="freeny.reg", csv=TRUE)
Descriptive Statistics in LaTeX

                             Min.    1st Qu.   Median        Mean    3rd Qu.   Max.    Missing Values
 y                           8.791    9.045    9.314         9.306    9.591    9.794         0
 lag.quarterly.revenue       8.791     9.02    9.284         9.281    9.561    9.775         0
 price.index                 4.278    4.392     4.51         4.496    4.605     4.71         0
 income.level                5.821    5.948    6.061         6.039    6.139     6.2          0
 market.potential            12.97    13.01    13.07         13.07    13.12    13.17         0




 Ben Mazzotta (Fletcher School)                Estout Demo                        August 24, 2010   9 / 11
Regression in CSV




                                  Figure 3: CSV format output



 Ben Mazzotta (Fletcher School)           Estout Demo           August 24, 2010   10 / 11
Regression in LaTeX
                                         (1)            (2)            (3)
                                           y              y               y
                 (Intercept)             2.186       4.971∗∗∗        -10.473∗
                                       (1.472)         (1.24)         (6.022)
                 lag.rev               0.891∗∗∗      0.373∗∗∗          0.124
                                       (0.074)        (0.114)         (0.142)
                 price                  -0.256       -0.819∗∗∗       -0.754∗∗∗
                                       (0.175)        (0.172)         (0.161)
                 income                              0.754∗∗∗        0.767∗∗∗
                                                      (0.145)         (0.134)
                 potential                                            1.331∗∗
                                                                      (0.509)
                 R2                     0.996          0.998           0.998
                 adj.R 2                0.996          0.997           0.998
                 N                       39             39               39
                 Standard errors in parentheses
                 ∗                ∗∗                 ∗∗∗
                     (p ≀ 0.1),        (p ≀ 0.05),         (p ≀ 0.01)
                                  Table 1: LaTeX format output


 Ben Mazzotta (Fletcher School)                        Estout Demo               August 24, 2010   11 / 11

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Estout demo

  • 1. {estout} for R Regression Output for *.csv, *.tex Formats Ben Mazzotta Fletcher School August 24, 2010 Ben Mazzotta (Fletcher School) Estout Demo August 24, 2010 1 / 11
  • 2. 1 Regression Output 2 {estout} Ben Mazzotta (Fletcher School) Estout Demo August 24, 2010 2 / 11
  • 3. Regression Y = ÎČX + ÎłC + (1) Figure 1: A generic OLS regression model Key features Multiple speciïŹcations α signiïŹcance level σÎČ precision of the coeïŹƒcient estimates Ben Mazzotta (Fletcher School) Estout Demo August 24, 2010 3 / 11
  • 4. Output Figure 2: Sample outreg output Ben Mazzotta (Fletcher School) Estout Demo August 24, 2010 4 / 11
  • 5. 1 Regression Output 2 {estout} Ben Mazzotta (Fletcher School) Estout Demo August 24, 2010 5 / 11
  • 6. Code I Preliminaries library(datasets) data(freeny) names(freeny) names(freeny) <- c("y","lag.rev","price","income", "potential") Regressions reg1 <- lm(y ~ lag.rev + price, data=freeny) reg2 <- lm(y ~ lag.rev + price + income, data=freeny) reg3 <- lm(y ~ lag.rev + price + income + potential, data=freeny)
  • 7. Code II Output descriptive statistics # Open the estout package library(estout) # Write the descriptive statistics # Specify the objects descsto(freeny) # Write to files desctab(filename="freeny", csv=FALSE) desctab(filename="freeny", csv=TRUE) Ben Mazzotta (Fletcher School) Estout Demo August 24, 2010 7 / 11
  • 8. Code III Output regression estimates # Store the objects eststo(reg1) eststo(reg2) eststo(reg3) # Write to file esttab(filename="freeny.reg", csv=FALSE) esttab(filename="freeny.reg", csv=TRUE)
  • 9. Descriptive Statistics in LaTeX Min. 1st Qu. Median Mean 3rd Qu. Max. Missing Values y 8.791 9.045 9.314 9.306 9.591 9.794 0 lag.quarterly.revenue 8.791 9.02 9.284 9.281 9.561 9.775 0 price.index 4.278 4.392 4.51 4.496 4.605 4.71 0 income.level 5.821 5.948 6.061 6.039 6.139 6.2 0 market.potential 12.97 13.01 13.07 13.07 13.12 13.17 0 Ben Mazzotta (Fletcher School) Estout Demo August 24, 2010 9 / 11
  • 10. Regression in CSV Figure 3: CSV format output Ben Mazzotta (Fletcher School) Estout Demo August 24, 2010 10 / 11
  • 11. Regression in LaTeX (1) (2) (3) y y y (Intercept) 2.186 4.971∗∗∗ -10.473∗ (1.472) (1.24) (6.022) lag.rev 0.891∗∗∗ 0.373∗∗∗ 0.124 (0.074) (0.114) (0.142) price -0.256 -0.819∗∗∗ -0.754∗∗∗ (0.175) (0.172) (0.161) income 0.754∗∗∗ 0.767∗∗∗ (0.145) (0.134) potential 1.331∗∗ (0.509) R2 0.996 0.998 0.998 adj.R 2 0.996 0.997 0.998 N 39 39 39 Standard errors in parentheses ∗ ∗∗ ∗∗∗ (p ≀ 0.1), (p ≀ 0.05), (p ≀ 0.01) Table 1: LaTeX format output Ben Mazzotta (Fletcher School) Estout Demo August 24, 2010 11 / 11