Help for package pglm

Version:

0.2-3

Date:

2021-07-19

Title:

Panel Generalized Linear Models

Depends:

R (≥ 2.10), maxLik, plm

Imports:

statmod, Formula

Suggests:

lmtest, car

Description:

Estimation of panel models for glm-like models: this includes binomial models (logit and probit), count models (poisson and negbin) and ordered models (logit and probit), as described in: Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

URL:

https://cran.r-project.org/package=pglm

NeedsCompilation:

Packaged:

2021-07-19 18:01:36 UTC; yves

Author:

Yves Croissant [aut, cre]

Maintainer:

Yves Croissant <yves.croissant@univ-reunion.fr>

Repository:

CRAN

Date/Publication:

2021-07-19 18:10:02 UTC

Perveived Fairness of Rules for Allocating Seats in Trains and Parking Spaces

Description

observations of 401 individuals

number of observations : 5614

country : France

economic topic : public economics

econometrics topic : ordered response

Usage

data(Fairness)

Format

A dataframe containing :

id: the individual index
answer: a factor with levels 0 (very unfair), 1 (essentially unfair), 2 (essentially fair) and 3 (very fair)
good: one of 'tgv' (French fast train) and 'Parking'
rule: the allocation rule, a factor with levels 'peak', 'admin', 'lottery', 'addsupply', 'queuing', 'moral' and 'compensation'
driving: does the individual has the driving license ?
education: does the individual has a diploma ?
recurring: does the allocation problem is reccuring ?

Source

provided by the authors.

References

Charles Raux, Stephanie Souche and Yves Croissant (2009) “How fair is pricing perceived to be? An empirical study”, Public Choice, 139(1), 227-240.

Health Insurance and Doctor Visits

Description

observations of 401 individuals

number of observations : 20186

country : United States

economic topic : Health Economics

econometrics topic : censored dependant variable

Usage

data(HealthIns)

Format

A time serie containing :

id: the individual index
year: the year
med: medical expenses
mdu: number of face-to face medical visits
coins: coinsurance rate
disease: count of chronic diseases
sex: a factor with level 'male' and 'female'
age: the age
size: the size of the family
child: a factor with levels 'no' and 'yes'

Source

Manning, W. G., J. P. Newhouse, N. Duan, E. B. Keeler and A. Leibowitz (1987) “Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment”, American Economic Review, 77(3), 251-277.

Deeb P. , and P.K. Trivedi (2002) “The structure of demand for medical care: latent class versus two-part models”, Journal of Health Economics, 21, 601-625..

References

http://cameron.econ.ucdavis.edu/musbook/mus.html.

Patents, R\&d and Technological Spillovers for a Panel of Firms

Description

annual observations of 181 firms from 1983 to 1991

number of observations : 1629

country : world

economic topic : producer behavior

econometrics topic : count data

Usage

data(PatentsRD)

Format

A dataframe containing :

firm: firm's id
year: year
sector: firm's main industry sector, one of aero (aerospace), chem (chemistry), comput (computer), drugs, elec (electricity), food, fuel (fuel and mining), glass, instr (instruments), machin (machinery), metals, other, paper, soft (software), motor (motor vehicules)
geo: geographic area, one of eu (European Union), japan, usa, rotw (rest of the world)
patent: numbers of European patent applications
rdexp: log of R and D expenditures
spil: log of spillovers

Source

Cincer, Michele (1997) “Patents, R \& D and technological spillovers at the firm level : some evidence from econometric count models for panel data”, Journal of Applied Econometrics, 12(3), may–june, 265–280.

References

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

Dynamic Relation Between Patents and R\&d

Description

yearly observations of 346 production units

number of observations : 3460

country : United States

economic topic : industrial economics

econometrics topic : count data

Usage

data(PatentsRDUS)

Format

A dataframe containing :

cusip: compustat's identifying number for the firm
year: year
ardssic: a two-digit code for the applied R&D industrial classification
scisect: is the firm in the scientific sector ?
capital72: book value of capital in 1972
sumpat: the sum of patents applied for between 1972-1979
rd: R&D spending during the year (in 1972 dollars)
patents: the number of patents applied for during the year that were eventually granted

Source

Hall, Browyn, Zvi Griliches and Jerry Hausman (1986) “Patents and R and D: Is there a Lag?”, International Economic Review, 27, 265-283.

References

http://cameron.econ.ucdavis.edu/racd/racddata.html, chapter 9..

Unionism and Wage Rate Determination

Description

yearly observations of 545 individuals from 1980 to 1987

number of observations : 4360

country : United States

economic topic : labor economics

econometrics topic : endogeneity

Usage

data(UnionWage)

Format

A dataframe containing :

id: the individual index
year: the year
exper: the experience, computed as age - 6 - schooling
health: does the individual has health disability ?
hours: the number of hours worked
married: is the individual married ?
rural: does the individual lives in a rural area ?
school: years of schooling
union: does the wage is set by collective bargaining
wage: hourly wage in US dollars
sector: one of agricultural, mining, construction, trade, transportation, finance, businessrepair, personalservice, entertainment, manufacturing, pro.rel.service, pub.admin
occ: one of proftech, manoffpro, sales, clerical, craftfor, operative, laborfarm, farmlabor, service
com: one of black, hisp and other
region: the region, one of NorthEast, NothernCentral, South and other

Source

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

References

Vella, F. and M. Verbeek (1998) “Whose wages do unions raise ? A dynamic model of unionism and wage”, Journal of Applied Econometrics, 13, 163–183.

Panel Estimators for Generalized Linear Models

Description

Estimation by maximum likelihood of glm (binomial and Poisson) and 'glm-like' models (Negbin and ordered) on longitudinal data

Usage

pglm(formula, data, subset, na.action,
     effect = c("individual", "time", "twoways"),
     model = c("random", "pooling", "within", "between"),
     family, other = NULL, index = NULL, start = NULL, R = 20,  ...)

Arguments

formula

a symbolic description of the model to be estimated,

data

the data: a pdata.frame object or an ordinary data.frame,

subset

an optional vector specifying a subset of observations,

na.action

a function which indicates what should happen when the data contains 'NA's,

effect

the effects introduced in the model, one of "individual", "time" or "twoways",

model

one of "pooling", "within", "between", "random",,

family

the distribution to be used,

other

for developper's use only,

index

the index,

start

a vector of starting values,

R

the number of function evaluation for the gaussian quadrature method used,

...

further arguments.

Value

An object of class "pglm", a list with elements:

coefficients

the named vector of coefficients,

logLik

the value of the log-likelihood,

hessian

the hessian of the log-likelihood at convergence,

gradient

the gradient of the log-likelihood at convergence,

call

the matched call,

est.stat

some information about the estimation (time used, optimisation method),

freq

the frequency of choice,

residuals

the residuals,

fitted.values

the fitted values,

formula

the formula (a mFormula object),

expanded.formula

the formula (a formula object),

model

the model frame used,

index

the index of the choice and of the alternatives.

Author(s)

Yves Croissant

Examples

## an ordered probit example
data('Fairness', package = 'pglm')
Parking <- subset(Fairness, good == 'parking')
op <- pglm(as.numeric(answer) ~ education + rule,
           Parking[1:105, ],
           family = ordinal('probit'), R = 5, print.level = 3,
           method = 'bfgs', index = 'id',  model = "random")

## a binomial (probit) example
data('UnionWage', package = 'pglm')
anb <- pglm(union ~ wage + exper + rural, UnionWage, family = binomial('probit'),
            model = "pooling",  method = "bfgs", print.level = 3, R = 5)

## a gaussian example on unbalanced panel data
data(Hedonic, package = "plm")
ra <- pglm(mv ~ crim + zn + indus + nox + age + rm, Hedonic, family = gaussian,
           model = "random", print.level = 3, method = "nr", index = "townid")

## some count data models
data("PatentsRDUS", package="pglm")
la <- pglm(patents ~ lag(log(rd), 0:5) + scisect + log(capital72) + factor(year), PatentsRDUS,
           family = negbin, model = "within", print.level = 3, method = "nr",
           index = c('cusip', 'year'))
la <- pglm(patents ~ lag(log(rd), 0:5) + scisect + log(capital72) + factor(year), PatentsRDUS,
           family = poisson, model = "pooling", index = c("cusip", "year"),
           print.level = 0, method="nr")

## a tobit example
data("HealthIns", package="pglm")
HealthIns$med2 <- HealthIns$med / 1000
HealthIns2 <- HealthIns[-2209, ]
set.seed(2)
subs <- sample(1:20186, 200, replace = FALSE)
HealthIns2 <- HealthIns2[subs, ]
la <- pglm(med ~ mdu + disease + age, HealthIns2,
           model = 'random', family = 'tobit', print.level = 0,
           method = 'nr', R = 5)