Title: | Panel Generalized Linear Models |
---|---|
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 <doi:10.1007/978-3-030-53953-5> Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R <doi:10.1002/9781119504641>. |
Authors: | Yves Croissant [aut, cre] |
Maintainer: | Yves Croissant <[email protected]> |
License: | GPL (>=2) |
Version: | 1.0-0 |
Built: | 2024-11-06 03:26:39 UTC |
Source: | https://github.com/ycroissant/pglm |
a pseudo-panel of 401 individuals from 2003
a tibble 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 ?
provided by the authors
Raux C, Souche S, Croissant Y (2009). “How Fair Is Pricing Perceived to Be? An Empirical Study.” Public Choice, 139(1/2), 227–240. ISSN 00485829, 15737101, http://www.jstor.org/stable/40270755.
a cross-section of 5908 individuals from 1974 to 1982
a tibble containing:
id: the individual index
year: the year
mdu: number of outpatient visits to an MD
opu: number of outpation visits to all providers
coins: coinsurance rate (0, 25, 50 or 100 percent)
idp: if individual deductible plan: 1, otherwise 0
lpi: log of the max of 1 and annual participation incentive payment
fmde: if idp = 1: 0 otherwise ln of the max of 1 and MDE / (0.01 coins)
income: family income
size: family size
age: the age
sex: a factor with level 'male'
and 'female'
child: a factor with levels 'no'
and 'yes'
race: a factor with levels 'white'
and 'black'
health: self-rated health, a factor with levels poor
, fair
, good
and verygood
educ: education of the household head in years
physlim: if the person has a physical limitation: 1
disease: index of chronic diseases
http://cameron.econ.ucdavis.edu/musbook/mus.html
Manning WG, Newhouse JP, Duan N, Keeler EB, Leibowitz A (1987). “Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment.” The American Economic Review, 77(3), 251–277. ISSN 00028282, http://www.jstor.org/stable/1804094.
Deb P, Trivedi PK (2002). “The structure of demand for health care: latent class versus two-part models.” Journal of Health Economics, 21(4), 601-625. ISSN 0167-6296, doi:10.1016/S0167-6296(02)00008-5, https://www.sciencedirect.com/science/article/pii/S0167629602000085.
a cross-section of 506 census tracts
a tibble containing:
mv: median value of owner–occupied homes
crim: crime rate
zn: proportion of 25,000 square feet residential lots
indus: proportion of no–retail business acres
chas: is the tract bounds the Charles River?
nox: annual average nitrogen oxide concentration in parts per hundred million
rm: average number of rooms
age: proportion of owner units built prior to 1940
dis: weighted distances to five employment centers in the Boston area
rad: index of accessibility to radial highways
tax: full value property tax rate ($/$10,000)
ptratio: pupil/teacher ratio
blacks: proportion of blacks in the population
lstat: proportion of population that is lower status
townid: town identifier
Online complements to Baltagi (2013): https://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=4338&itemId=1118672321&resourceId=13452
Baltagi BH (2001). Econometric analysis of panel data. John Wiley and sons.
Baltagi BH (2013). Econometric analysis of panel data. John Wiley and sons.
Belsley DA, Kuh E, Welsch RE (1980). Regression diagnostics: identifying influencal data ans sources of collinearity. John Wiley.
Harrison D, Rubinfeld DL (1978). “Hedonic housing prices and the demand for clean air.” Journal of Environmental Economics and Management, 5(1), 81-102. ISSN 0095-0696, doi:10.1016/0095-0696(78)90006-2, https://www.sciencedirect.com/science/article/pii/0095069678900062.
yearly observations of 346 production units
a tibble containing:
cusip: Compustat's identifying number for the firm
year: year
ardssic: a two-digit code for the applied R
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 and D spending during the year (in 1972 dollars)
patents: the number of patents applied for during the year that were eventually granted
http://cameron.econ.ucdavis.edu/racd/racddata.html, chapter 9.
Hall BH, Griliches Z, Hausman JA (1986). “Patents and R and D: Is There a Lag?” International Economic Review, 27(2), 265–283. ISSN 00206598, 14682354, http://www.jstor.org/stable/2526504.
Estimation by maximum likelihood of glm (binomial and Poisson) and 'glm-like' models (Negbin and ordered) on longitudinal data
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, method = c("bfgs", "newton"), trace = 0, ... ) ordinal(link = c("probit", "logit")) negbin(link = c("log"), vlink = c("nb1", "nb2"))
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, method = c("bfgs", "newton"), trace = 0, ... ) ordinal(link = c("probit", "logit")) negbin(link = c("log"), vlink = c("nb1", "nb2"))
formula |
a symbolic description of the model to be estimated, |
data |
the data: a |
subset |
an optional vector specifying a subset of observations, |
na.action |
a function which indicates what should happen when
the data contains |
effect |
the effects introduced in the model, one of
|
model |
one of |
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, |
method |
the optimization method, one of |
trace |
an integer |
... |
further arguments. |
link , vlink
|
arguments of family functions |
An object of class "miscr"
, a list with elements:
Yves Croissant
## a binomial (probit) example anb <- pglm(union ~ wage + exper + rural, union_wage, family = binomial('probit'), model = "pooling", method = "bfgs", trace = 3, R = 5) ## a gaussian example on unbalanced panel data ra <- pglm(mv ~ crim + zn + indus + nox + age + rm, hedonic, family = gaussian, model = "random", trace = 3, method = "newton", index = "townid") ## some count data models la <- pglm(patents ~ lag(log(rd), 0:5) + scisect + log(capital72) + factor(year), patents_rd, family = negbin, model = "within", trace = 3, method = "newton", index = c('cusip', 'year')) la <- pglm(patents ~ lag(log(rd), 0:5) + scisect + log(capital72) + factor(year), patents_rd, family = poisson, model = "pooling", index = c("cusip", "year"), tracen = 0, method="newton")
## a binomial (probit) example anb <- pglm(union ~ wage + exper + rural, union_wage, family = binomial('probit'), model = "pooling", method = "bfgs", trace = 3, R = 5) ## a gaussian example on unbalanced panel data ra <- pglm(mv ~ crim + zn + indus + nox + age + rm, hedonic, family = gaussian, model = "random", trace = 3, method = "newton", index = "townid") ## some count data models la <- pglm(patents ~ lag(log(rd), 0:5) + scisect + log(capital72) + factor(year), patents_rd, family = negbin, model = "within", trace = 3, method = "newton", index = c('cusip', 'year')) la <- pglm(patents ~ lag(log(rd), 0:5) + scisect + log(capital72) + factor(year), patents_rd, family = poisson, model = "pooling", index = c("cusip", "year"), tracen = 0, method="newton")
yearly observations of 545 individuals from 1980 to 1987
a tibble 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
Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/
Vella F, Verbeek M (1998). “Whose Wages do Unions Raise? A Dynamic Model of Unionism and Wage Rate Determination for Young Men.” Journal of Applied Econometrics, 13(2), 163–183. ISSN 08837252, 10991255, http://www.jstor.org/stable/223257.