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README.md

Pareto Models for Top Incomes

Arthur Charpentier & Emmanuel Flachaire

Install the TopIncome library

The TopIncome library can be installed from github,

library(devtools)
devtools::install_github("freakonometrics/TopIncomes")
## Skipping install of 'TopIncomes' from a github remote, the SHA1 (a5fa2bba) has not changed since last install.
##   Use `force = TRUE` to force installation
library(TopIncomes)

Fitting Pareto Models

n <- 1000
set.seed(123)
x <- repd(n,.5,1,-1)
w <- rgamma(n,10,10)

Pareto 1

The Pareto type 1 distribution is bounded from below by (u>0), and with tail parameter it has the cumulative distribution function for . Note that the tail index is .

estim <- MLE.pareto1(data=x, weights=w, threshold=1)
estim
## $alpha
## [1] 3.300653
## 
## $xi
## [1] 0.3029704
## 
## $k
## [1] 1000

Generalized Pareto

The Generalized Pareto distribution is bounded from below by (u>0), with tail parameter : the cumulative distribution function is for . Note that the tail index is .

estim <- MLE.gpd(data=x, weights=w, threshold=1)
estim
## $xi
## [1] 0.4892361
## 
## $mu
## [1] 1
## 
## $beta
## [1] 0.2488107
## 
## $k
## [1] 1000

Extended Pareto

The Extended Pareto distribution is bounded from below by (u>0), and has cumulative distribution function for . Note that the tail index is .

estim <- EPD(data=x, weights=w)
estim
## $k
## [1] 999
## 
## $gamma
## [1] 0.3737252
## 
## $kappa
## [1] 0.1628108
## 
## $tau
## [1] -3.342535

Application to Income

Consider some simulated data,

url_1 <- "https://github.com/freakonometrics/TopIncome/raw/master/data_csv/dataframe_yw_1.csv"
df <- read.table(url_1,sep=";",header=TRUE)
data_1  <-  tidy_income(income = df$y, weights = df$w)
Pareto_diagram(data_1)

T <- Table_Top_Share(data_1, p=.01)

Tail index , for three fited distributions

T$TailIndex
##             top90%    top95%    top99%
## cutoff   90.000000 95.000000 99.000000
## Pareto 1  1.713197  1.959476  2.187579
## GPD       2.920797  3.017690 18.662425
## EPD       2.279386  2.343399  4.809314
top90% top95% top99%
Pareto_1 1.713197 1.959476 2.187579
GPD 2.920797 3.017690 18.662425
EPD 2.279387 2.343399 4.809314

Tail Index (alpha)

Top share income, for three fited distributions

T$TopShare
##              top90%     top95%     top99%
## cutoff   90.0000000 95.0000000 99.0000000
## edf       0.1284910  0.1284910  0.1284910
## Pareto 1  0.1997239  0.1641696  0.1503932
## GPD       0.1392083  0.1389045  0.1397348
## EPD       0.1166443  0.1242985  0.1390286
top90% top95% top99%
EDF 12.84910 12.84910 12.84910
Pareto_1 19.97239 16.41696 15.03932
GPD 13.92083 13.89045 13.97348
EPD 11.66442 12.42985 13.90286

Top Share (in percent)

See also (to get automatically tables in a markdown format)

# T <- Table_Top_Share(data_1, p=.01, md=TRUE)
Top_Incomes(data_1)

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