Version: 1.0-2
Date: 2022-01-25
Title: Panel Data Econometrics with R
Depends: R (≥ 3.5.0), plm
Suggests: car, dplyr, ggplot2, lmtest, msm, pglm, splm, survival, texreg
Description: Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> book.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://cran.r-project.org/package=pder
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-01-25 12:13:45 UTC; yves
Author: Yves Croissant ORCID iD [aut, cre], Giovanni Millo [aut]
Maintainer: Yves Croissant <yves.croissant@univ-reunion.fr>
Repository: CRAN
Date/Publication: 2022-01-26 20:02:25 UTC

Callbacks to Job Applications

Description

a pseudo-panel of 1518 resumes from 2014

number of observations : 6072

number of individual observations : 4

country : United States

package : binomial

JEL codes: E24, E32, J14, J22, J23, J64

Chapter : 08

Usage

data(CallBacks)

Format

A dataframe containing:

jobid

the job index

unempdur

unemployment duration in month

interim

a dummy for interim experience

callback

a dummy for call backs

old

a dummy for age 57-58

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Farber, Henry S.; Silverman, Dan and Till von Wachter (2016) “Determinants of Callbacks to Job Applications: An Audit Study”, American Economic Review, 106(5), 314-318, doi: 10.1257/aer.p20161010 .

How to Overcome Organization Failure in Organization

Description

a pseudo-panel of 240 individuals

number of observations : 7168

number of individual observations : 30

country : United States and Spain

package : ordinalpanelexpe

JEL codes: C92, D23

Chapter : 08

Usage

data(CoordFailure)

Format

A dataframe containing:

firm

the firm index

id

the individual index

period

the period

place

either Cleveland or Barcelona

bonus1

the bonus for the first block of 10 rounds

bonus2

the bonus for the second block of 10 rounds

bonus3

the bonus for the third block of 10 rounds

effort

the level of effort of the employee

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Brandts, Jordi and David J. Cooper (2006) “A Change Would Do You Good... An Experimental Study on How to Overcome Coordination Failure in Organizations”, American Economic Review, 96(3), 669-693, doi: 10.1257/aer.96.3.669 .

The Relation Between Democraty and Income

Description

5-yearly observations of 211 countries from 1950 to 2000

number of observations : 2321

number of time-series : 11

country : world

package : panel

JEL codes: D72, O47

Chapter : 02, 07

Usage

data(DemocracyIncome)

Format

A dataframe containing:

country

country

year

the starting year of the 5-years period

democracy

democracy index

income

the log of the gdp per capita

sample

a dummy variable to select the subset used in the original article

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Daron Acemoglu, Simon Johnson, James A. Robinson and Pierre Yared (2008) “Income and Democracy”, American Economic Review, 98(3), 808-842, doi: 10.1257/aer.98.3.808 .

Examples

#### Example 7-1

## ------------------------------------------------------------------------

## Not run: 
data("DemocracyIncome", package = "pder")

## ------------------------------------------------------------------------
data("DemocracyIncome", package="pder")
set.seed(1)
di2000 <- subset(DemocracyIncome, year == 2000,
                 select = c("democracy", "income", "country"))
di2000 <- na.omit(di2000)
di2000$country <- as.character(di2000$country)
di2000$country[- c(2,5, 23, 16, 17, 22, 71,  125, 37, 43, 44,
                   79, 98, 105, 50, 120,  81, 129, 57, 58,99)] <- NA

if(requireNamespace("ggplot2")){
    library("ggplot2")
    ggplot(di2000, aes(income, democracy, label = country)) + 
        geom_point(size = 0.4) + 
        geom_text(aes(y= democracy + sample(0.03 * c(-1, 1), 
                                            nrow(di2000), replace = TRUE)),
                  size = 2) +
        theme(legend.text = element_text(size = 6), 
              legend.title= element_text(size = 8),
              axis.title = element_text(size = 8),
              axis.text = element_text(size = 6))
}


## ------------------------------------------------------------------------
library("plm")
pdim(DemocracyIncome)
head(DemocracyIncome, 4)


#### Example 7-2

## ------------------------------------------------------------------------
mco <- plm(democracy ~ lag(democracy) + lag(income) + year - 1, 
           DemocracyIncome, index = c("country", "year"), 
           model = "pooling", subset = sample == 1)

## ------------------------------------------------------------------------
mco <- plm(democracy ~ lag(democracy) + lag(income), 
           DemocracyIncome, index = c("country", "year"), 
           model = "within", effect = "time",
           subset = sample == 1)
coef(summary(mco))


#### Example 7-3

## ------------------------------------------------------------------------
within <- update(mco, effect = "twoways")
coef(summary(within))


#### Example 7-4

## ------------------------------------------------------------------------
ahsiao <- plm(diff(democracy) ~ lag(diff(democracy)) + 
              lag(diff(income)) + year - 1  | 
              lag(democracy, 2) + lag(income, 2) + year - 1, 
              DemocracyIncome, index = c("country", "year"),
              model = "pooling", subset = sample == 1)
coef(summary(ahsiao))[1:2, ]


#### Example 7-5

## ------------------------------------------------------------------------
diff1 <- pgmm(democracy ~ lag(democracy) + lag(income) | 
              lag(democracy, 2:99)| lag(income, 2),
              DemocracyIncome, index=c("country", "year"), 
              model="onestep", effect="twoways", subset = sample == 1)
coef(summary(diff1))

## ------------------------------------------------------------------------
diff2 <- update(diff1, model = "twosteps")
coef(summary(diff2))


#### Example 7-7

## ------------------------------------------------------------------------
sys2 <- pgmm(democracy ~ lag(democracy) + lag(income) | 
             lag(democracy, 2:99)| lag(income, 2),
             DemocracyIncome, index = c("country", "year"), 
             model = "twosteps", effect = "twoways",
             transformation = "ld")
coef(summary(sys2))


#### Example 7-8

## ------------------------------------------------------------------------
sqrt(diag(vcov(diff2)))[1:2]
sqrt(diag(vcovHC(diff2)))[1:2]


#### Example 7-10

## ------------------------------------------------------------------------
mtest(diff2, order = 2)


#### Example 7-9

## ------------------------------------------------------------------------
sargan(diff2)
sargan(sys2)

## End(Not run)

The Relation Between Democraty and Income

Description

25-yearly observations of 25 countries from 1850 to 2000

number of observations : 175

number of time-series : 7

country : world

package : panel

JEL codes: D72, O47

Chapter : 02, 07

Usage

data(DemocracyIncome25)

Format

A dataframe containing:

country

country

year

the starting year of the 5-years period

democracy

democracy index

income

the log of the gdp per capita

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Daron Acemoglu, Simon Johnson, James A. Robinson and Pierre Yared (2008) “Income and Democracy”, American Economic Review, 98(3), 808-842, doi: 10.1257/aer.98.3.808 .

Examples

#### Example 2-7

## ------------------------------------------------------------------------
library("plm")
data("DemocracyIncome25", package = "pder")
DI <- pdata.frame(DemocracyIncome25)
summary(lag(DI$income))
ercomp(democracy ~ lag(income), DI)
models <- c("within", "random", "pooling", "between")
sapply(models, function(x)
       coef(plm(democracy ~ lag(income), DI, model = x))["lag(income)"])


#### Example 7-6

## ------------------------------------------------------------------------
data("DemocracyIncome25", package = "pder")
pdim(DemocracyIncome25)

## ------------------------------------------------------------------------
diff25 <- pgmm(democracy ~ lag(democracy) + lag(income) |
               lag(democracy, 2:99) + lag(income, 2:99),
               DemocracyIncome25, model = "twosteps")

## ------------------------------------------------------------------------
diff25lim <- pgmm(democracy ~ lag(democracy) + lag(income) | 
                  lag(democracy, 2:4)+ lag(income, 2:4),
                  DemocracyIncome25, index=c("country", "year"), 
                  model="twosteps", effect="twoways", subset = sample == 1)
diff25coll <- pgmm(democracy ~ lag(democracy) + lag(income) | 
                   lag(democracy, 2:99)+ lag(income, 2:99),
                   DemocracyIncome25, index=c("country", "year"), 
                   model="twosteps", effect="twoways", subset = sample == 1,
                   collapse = TRUE)
sapply(list(diff25, diff25lim, diff25coll), function(x) coef(x)[1:2])

#### Example 7-9

## ------------------------------------------------------------------------
sapply(list(diff25, diff25lim, diff25coll), 
       function(x) sargan(x)[["p.value"]])

Diffusion of Haemodialysis Technology

Description

yearly observations of 50 states from 1977 to 1990

number of observations : 700

number of time-series : 14

country : United States

package : panel

JEL codes: I18, O31

Chapter : 09

Usage

data(Dialysis)

Format

A dataframe containing:

state

the state id

time

the year of observation

diffusion

the number of equipment divided by the number of the equipment in the given state for the most recent period

trend

a linear trend

regulation

a dummy variable for the presence of a certificate of need regulation for the given state and the given period

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Steven B. Caudill, Jon M. Ford and David L. Kaserman (1995) “Certificate of Need Regulation and the Diffusion of Innovations : a Random Coefficient Model”, Journal of Applied Econometrics, 10, 73–78., doi: 10.1002/jae.3950100107 .

Examples


#### Example 9-1

## ------------------------------------------------------------------------
library("plm")

## ------------------------------------------------------------------------
data("Dialysis", package = "pder")
rndcoef <- pvcm(log(diffusion / (1 - diffusion)) ~ trend + trend:regulation, 
                 Dialysis, model="random")
summary(rndcoef)

## ------------------------------------------------------------------------
cbind(coef(rndcoef), stdev = sqrt(diag(rndcoef$Delta)))

Dynamics of Charitable Giving

Description

a pseudo-panel of 32 individuals from 2006

number of observations : 1039

number of individual observations : 4-80

country : United States

package : limdeppanel

JEL codes: C93, D64, D82, H41, L31, Z12

Chapter : 08

Usage

data(Donors)

Format

A dataframe containing:

id

the id of the sollicitor

solsex

the sex of the sollicitor

solmin

does the sollicitor belongs to a minority ?

beauty

beauty rating for the sollicitor

assertive

assertive rating for the sollicitor

social

social rating for the sollicitor

efficacy

efficacy rating for the sollicitor

performance

performance rating for the sollicitor

confidence

confidence rating for the sollicitor

age

age of the individual

sex

sex of the individual

min

does the individual belongs to a minority

treatment

the treatment, one of "vcm", "sgift" and "lgift"

refgift

has the individual refused the gift ?

donation

the amount of the donation

prior

has the individual been visited during the previous campaign ?

prtreat

the treatment during the previous campaign, one of "none", "vcm", and "lottery"

prcontr

has the individual made a donation during the previous campaign ?

prdonation

the amount of the donation during the previous campaign

prsolsex

the sex of the sollicitor during the previous campaign

prsolmin

did the sollicitor of the previous campaign belong to a minority ?

prbeauty

beauty rating for the sollicitor of the previous campaign

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Landry, Craig E.; Lange, Andreas; List, John A.; Price, Michael K. and Nicholas G. Rupp (2010) “Is a Donor in Hand Better Than Two in the Bush ? Evidence From a Natural Field Experiment”, American Economic Review, 100(3), 958–983, doi: 10.1257/aer.100.3.958 .

Examples


#### Example 8-5

## ------------------------------------------------------------------------
## Not run: 
data("Donors", package = "pder")
library("plm")
T3.1 <- plm(donation ~ treatment +  prcontr, Donors, index = "id")
T3.2 <- plm(donation ~ treatment * prcontr - prcontr, Donors, index = "id")
T5.A <- pldv(donation ~ treatment +  prcontr, Donors, index = "id", 
             model = "random", method = "bfgs")
T5.B <- pldv(donation ~ treatment * prcontr - prcontr, Donors, index = "id", 
             model = "random", method = "bfgs")

## End(Not run)

Evapotranspiration

Description

a pseudo-panel of 86 areas from 2008

number of observations : 430

number of individual observations : 5

country : France

package : panel

Chapter : 10

Usage

data(EvapoTransp)

Format

A dataframe containing:

id

observation site

period

measuring period

et

evapotranspiration

prec

precipitation

meansmd

mean soil moisture deficit

potet

potential evapotranspiration

infil

infiltration rate

biomass

biomass

biomassp1

biomass in early growing season

biomassp2

biomass in main growth period

biomassp3

peak biomass

biomassp4

peak biomass after clipping

biomassp5

biomass in autumn

plantcover

plant cover

softforbs

soft-leaved forbs

tallgrass

tall grass

diversity

species diversity

matgram

mat-forming graminoids

dwarfshrubs

dwarf shrubs

legumes

abundance of legumes

Source

kindly provided by the authors

References

Obojes, N.; Bahn, M.; Tasser, E.; Walde, J.; Inauen, N.; Hiltbrunner, E.; Saccone, P.; Lochet, J.; Clément, J. and S. Lavorel (2015) “Vegetation Effects on the Water Balance of Mountain Grasslands Depend on Climatic Conditions”, Ecohydrology, 8(4), 552-569, doi: 10.1002/eco.1524 .

Examples


#### Example 10-14

## ------------------------------------------------------------------------
## Not run: 
data("EvapoTransp", package = "pder")
data("etw", package = "pder")
if (requireNamespace("splm")){
    library("splm")
    evapo <- et ~ prec + meansmd + potet + infil + biomass + plantcover +
        softforbs + tallgrass + diversity + matgram + dwarfshrubs + legumes
    semsr.evapo <- spreml(evapo, data=EvapoTransp, w=etw,
                          lag=FALSE, errors="semsr")
    summary(semsr.evapo)
}

## ------------------------------------------------------------------------
library("plm")
if (requireNamespace("lmtest")){
    coeftest(plm(evapo, EvapoTransp, model="pooling"))
}

## ------------------------------------------------------------------------

if (requireNamespace("lmtest") & requireNamespace("splm")){
    coeftest(spreml(evapo, EvapoTransp, w=etw, errors="sem"))
}



#### Example 10-17

## ------------------------------------------------------------------------

if (requireNamespace("lmtest")){
    saremsrre.evapo <- spreml(evapo, data = EvapoTransp,
                              w = etw, lag = TRUE, errors = "semsr")
    summary(saremsrre.evapo)$ARCoefTable
    round(summary(saremsrre.evapo)$ErrCompTable, 6)
}

## End(Not run)

Financial Institutions and Growth

Description

5-yearly observations of 78 countries from 1960 to 1995

number of observations : 546

number of time-series : 7

country : world

package : panel

JEL codes: G20, O16, O47, C23, C33, O15

Chapter : 07

Usage

data(FinanceGrowth)

Format

A dataframe containing:

country

country name

period

period

growth

growth rate * 100

privo

log private credit / GDP

lly

log liquid liabilities / GDP

btot

log bank credit/total credit

lgdp

log initial gdp per capita (PPP)

sec

mean years of secondary schooling

gov

log government spending / GDP

lbmp

log(1 black market premium)

lpi

log(1 + inflation rate)

trade

log (imports + exports)/GDP

Source

http://www.cgdev.org/content/publications/detail/14256

References

Levine, Ross; Loayza, Norman and Thorsten Beck (2000) “Financial Intermediation and Growth: Causality and Causes”, Journal of Monetary Economics, 46, 31-77, doi: 10.1016/S0304-3932(00)00017-9 .

Roodman, David (2009) “A Note on the Theme of Two Many Instruments”, Oxford Bulletin of Economics An Statistics, 71(1), 135–158, doi: 10.1111/j.1468-0084.2008.00542.x .

Foreign Trade of Developing Countries

Description

yearly observations of 31 countries from 1963 to 1986

number of observations : 744

number of time-series : 24

country : developing countries

package : panelivreg

JEL codes: O19, C51, F17

Chapter : 02, 06

Usage

data(ForeignTrade)

Format

A dataframe containing:

country

country name

year

year

exports

nominal exports deflated by the unit value of exports per capita

imports

nominal imports deflated by the unit value of exports per capita

resimp

official foreing reserves (in US dollars) divided by nominal imports (in US dollars)

gnp

real GNP per capita

pgnp

trend real GNP per capita calculated by fitting linear trend yit*=y0iexp(gi t), where y0i is the initial value of real gnp per capita for country i and gi is the ith country's average growth rate over 1964-1986

gnpw

real genp for USA per capita

pm

unit value of imports (in US dollars), 1980 = 100

px

unit value of exports (in US dollars), 1980 = 100

cpi

domestic CPI, 1980 = 100

pw

US producer's price index, 1980 = 100

exrate

exchange rate (price of US dollars in local currency), 1980 = 1

consump

domestic consumption per capita,

invest

domestic fixed gross investment per capita

income

domestic disposable income per capita

pop

population

reserves

official foreing reserves (in US dollars)

money

domestic money supply per capita

trend

trend dummy, 1964 = 1

pwcpi

log of us producer price index divided by domestic cpi

importspmpx

log of nominal imports divided by export prices

pmcpi

log of imports price divided by domestic cpi

pxpw

log of exports price divided by domestic cpi

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Kinal, T. and K. Lahiri (1993) “On the Estimation of Simultaneous-equations Error-components Models with An Application to a Model of Developing Country Foreign Trade”, Journal of Applied Economics, 8, 81-92, doi: 10.1002/jae.3950080107 .

Examples

#### Example 2-4

## ------------------------------------------------------------------------
library("plm")
data("ForeignTrade", package = "pder")
FT <- pdata.frame(ForeignTrade)
summary(FT$gnp)
ercomp(imports ~ gnp, FT)
models <- c("within", "random", "pooling", "between")
sapply(models, function(x) coef(plm(imports ~ gnp, FT, model = x))["gnp"])


#### Example 6-2

## ------------------------------------------------------------------------
data("ForeignTrade", package = "pder")
w1 <- plm(imports~pmcpi + gnp + lag(imports) + lag(resimp)  |
          lag(consump) + lag(cpi) + lag(income) + lag(gnp) + pm +
          lag(invest) + lag(money) + gnpw + pw + lag(reserves) +
          lag(exports) + trend + pgnp + lag(px),
          ForeignTrade, model = "within")
r1 <- update(w1, model = "random", random.method = "nerlove", 
             random.dfcor = c(1, 1), inst.method = "baltagi")

## ------------------------------------------------------------------------
phtest(r1, w1)

## ------------------------------------------------------------------------
r1b <- plm(imports ~ pmcpi + gnp + lag(imports) + lag(resimp) |
            lag(consump) + lag(cpi) + lag(income) + lag(px) + 
            lag(reserves) + lag(exports) | lag(gnp) + pm + 
            lag(invest) + lag(money) + gnpw + pw  + trend + pgnp,
            ForeignTrade, model = "random", inst.method = "baltagi", 
            random.method = "nerlove", random.dfcor = c(1, 1))

phtest(w1, r1b)

## ------------------------------------------------------------------------
rbind(within = coef(w1), ec2sls = coef(r1b)[-1])

## ------------------------------------------------------------------------
elast <- sapply(list(w1, r1, r1b), 
                function(x) c(coef(x)["pmcpi"], 
                              coef(x)["pmcpi"] / (1 - coef(x)["lag(imports)"])))
dimnames(elast) <- list(c("ST", "LT"), c("w1", "r1", "r1b"))
elast

## ------------------------------------------------------------------------
rbind(within = coef(summary(w1))[, 2], 
      ec2sls = coef(summary(r1b))[-1, 2])

#### Example 6-4

## ------------------------------------------------------------------------
eqimp <- imports ~ pmcpi + gnp + lag(imports) + 
                lag(resimp) | lag(consump) + lag(cpi) + lag(income) + 
                lag(px) + lag(reserves) + lag(exports) | lag(gnp) + pm + 
                lag(invest) + lag(money) + gnpw + pw  + trend + pgnp
eqexp <- exports ~ pxpw + gnpw + lag(exports) |
                lag(gnp) + pw + lag(consump) + pm + lag(px) + lag(cpi) | 
                lag(money) + gnpw +  pgnp + pop + lag(invest) + 
                lag(income) + lag(reserves) + exrate
r12 <- plm(list(import.demand = eqimp,
                export.demand = eqexp),
           data = ForeignTrade, index = 31, model = "random", 
           inst.method = "baltagi", random.method = "nerlove",
           random.dfcor = c(1, 1))
summary(r12)

## ------------------------------------------------------------------------
rbind(ec2sls = coef(summary(r1b))[-1, 2],
      ec3sls = coef(summary(r12), "import.demand")[-1, 2])

Impact of Institutions on Cumulative Research

Description

yearly observations of 216 articles from 1970 to 2001

number of observations : 4880

number of time-series : 32

country : United States

package : countpanel

JEL codes: D02, D83, I23, O30

Chapter : 08

Usage

data(GiantsShoulders)

Format

A dataframe containing:

pair

the pair article index

article

the article index

brc

material of the article is deposit on a Biological Ressource Center

pubyear

publication year of the article

brcyear

year of the deposit in brc of the material related to the article

year

the year index

citations

the number of citations

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Furman, Jeffrey L. and Scott Stern (2011) “Climbing Atop the Shoulders of Giants: the Impact of Institutions on Cumulative Research”, American Economic Review, 101(5), 1933-1963, doi: 10.1257/aer.101.5.1933 .

Examples


#### Example 8-6

## ------------------------------------------------------------------------
## Not run: 
data("GiantsShoulders", package = "pder")
head(GiantsShoulders)

## ------------------------------------------------------------------------

if (requireNamespace("dplyr")){
    library("dplyr")
    GiantsShoulders <- mutate(GiantsShoulders, age = year - pubyear)
    cityear <- summarise(group_by(GiantsShoulders, brc, age), 
                         cit = mean(citations, na.rm = TRUE))
    GiantsShoulders <- mutate(GiantsShoulders,
                              window = as.numeric( (brc == "yes") & 
                                                   abs(brcyear - year) <= 1),
                              post_brc = as.numeric( (brc == "yes") & 
                                                     year - brcyear > 1),
                              age = year - pubyear)
    GiantsShoulders$age[GiantsShoulders$age == 31] <- 0
    #GiantsShoulders$year[GiantsShoulders$year 
    #GiantsShoulders$year[GiantsShoulders$year 
    GiantsShoulders$year[GiantsShoulders$year < 1975] <- 1970
    GiantsShoulders$year[GiantsShoulders$year >= 1975 & GiantsShoulders$year < 1980] <- 1975

    if (requireNamespace("pglm")){
        library("pglm")
        t3c1 <- lm(log(1 + citations) ~ brc + window + post_brc + factor(age), 
                   data = GiantsShoulders)
        t3c2 <- update(t3c1, . ~ .+  factor(pair) + factor(year))
        t3c3 <- pglm(citations ~ brc + window + post_brc + factor(age) + factor(year),
                     data = GiantsShoulders, index = "pair", 
                     effect = "individual", model = "within", family = negbin)
        t3c4 <- pglm(citations ~ window + post_brc + factor(age) + factor(year),
                     data = GiantsShoulders, index = "article", 
                     effect = "individual", model = "within", family = negbin)
        ## screenreg(list(t3c2, t3c3, t3c4),
        ##           custom.model.names = c("ols: age/year/pair-FE", 
        ##                                  "NB:age/year/pair-FE", "NB: age/year/article-FE"),
        ##           omit.coef="(factor)|(Intercept)", digits = 3)
    }
}

## End(Not run)

House Prices Data

Description

yearly observations of 49 regions from 1976 to 2003

number of observations : 1421

number of time-series : 29

country : United States

package : hedprice

JEL codes: C51, R31

Chapter : 09, 10

Usage

data(HousePricesUS)

Format

A dataframe containing:

state

state index

year

year

names

state name

plate

state number plate index

region

region index

region.name

region name

price

real house price index, 1980=100

income

real per-capita income

pop

total population

intrate

real interest rate on borrowing

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Holly, S.; Pesaran, M.G. and T. Yamagata (2010) “A Spatio-temporal Model of House Prices in the USA”, Journal of Econometrics, 158(1), 160–173, doi: 10.1016/j.jeconom.2010.03.040 .

Millo, Giovanni (2015) “Narrow Replication of 'spatio-temporal Model of House Prices in the Usa', Using R”, Journal of Applied Econometrics, 30(4), 703–704, doi: 10.1002/jae.2424 .

Examples

#### Example 4-11

## ------------------------------------------------------------------------
## Not run: 
data("HousePricesUS", package = "pder")
library("plm")
php <- pdata.frame(HousePricesUS)

## ------------------------------------------------------------------------
cbind("rho"   = pcdtest(diff(log(php$price)), test = "rho")$statistic,
      "|rho|" = pcdtest(diff(log(php$price)), test = "absrho")$statistic)

## ------------------------------------------------------------------------
regions.names <- c("New Engl", "Mideast", "Southeast", "Great Lks",
                   "Plains", "Southwest", "Rocky Mnt", "Far West")
corr.table.hp <- cortab(diff(log(php$price)), grouping = php$region,
                        groupnames = regions.names)
colnames(corr.table.hp) <- substr(rownames(corr.table.hp), 1, 5)
round(corr.table.hp, 2)

## ------------------------------------------------------------------------
pcdtest(diff(log(price)) ~ diff(lag(log(price))) + diff(lag(log(price), 2)),
        data = php)

#### Example 9-2

## ------------------------------------------------------------------------
data("HousePricesUS", package = "pder")
swmod <- pvcm(log(price) ~ log(income), data = HousePricesUS, model= "random")
mgmod <- pmg(log(price) ~ log(income), data = HousePricesUS, model = "mg")
coefs <- cbind(coef(swmod), coef(mgmod))
dimnames(coefs)[[2]] <- c("Swamy", "MG")
coefs

#### Example 9-3

## ------------------------------------------------------------------------

if (requireNamespace("texreg")){
    library("texreg")
    data("RDSpillovers", package = "pder")
    fm.rds <- lny ~ lnl + lnk + lnrd
    mg.rds <- pmg(fm.rds, RDSpillovers, trend = TRUE)
    dmg.rds <- update(mg.rds, . ~ lag(lny) + .)
    screenreg(list('Static MG' = mg.rds, 'Dynamic MG'= dmg.rds), digits = 3)
    if (requireNamespace("msm")){
        library("msm")
        b.lr <- coef(dmg.rds)["lnrd"]/(1 - coef(dmg.rds)["lag(lny)"])
        SEb.lr <- deltamethod(~ x5 / (1 - x2),
                              mean = coef(dmg.rds), cov = vcov(dmg.rds))
        z.lr <- b.lr / SEb.lr
        pval.lr <- 2 * pnorm(abs(z.lr), lower.tail = FALSE)
        lr.lnrd <- matrix(c(b.lr, SEb.lr, z.lr, pval.lr), nrow=1)
        dimnames(lr.lnrd) <- list("lnrd (long run)", c("Est.", "SE", "z", "p.val"))
        round(lr.lnrd, 3)
    }
}


#### Example 9-4

## ------------------------------------------------------------------------
housep.np <- pvcm(log(price) ~ log(income), data = HousePricesUS, model = "within")
housep.pool <- plm(log(price) ~ log(income), data = HousePricesUS, model = "pooling")
housep.within <- plm(log(price) ~ log(income), data = HousePricesUS, model = "within")

d <- data.frame(x = c(coef(housep.np)[[1]], coef(housep.np)[[2]]), 
                coef = rep(c("intercept", "log(income)"), 
                           each = nrow(coef(housep.np))))
if (requireNamespace("ggplot2")){
    library("ggplot2")
    ggplot(d, aes(x)) + geom_histogram(col = "black", fill = "white", bins = 8) +
        facet_wrap(~ coef, scales = "free") + xlab("") + ylab("")
}


## ------------------------------------------------------------------------
summary(housep.np)

## ------------------------------------------------------------------------
pooltest(housep.pool, housep.np)
pooltest(housep.within, housep.np)


#### Example 9-5

## ------------------------------------------------------------------------
library("texreg")
cmgmod <- pmg(log(price) ~ log(income), data = HousePricesUS, model = "cmg")
screenreg(list(mg = mgmod, ccemg = cmgmod), digits = 3)

#### Example 9-6

## ------------------------------------------------------------------------
ccemgmod <- pcce(log(price) ~ log(income), data=HousePricesUS, model="mg")
summary(ccemgmod)

## ------------------------------------------------------------------------
ccepmod <- pcce(log(price) ~ log(income), data=HousePricesUS, model="p")
summary(ccepmod)



#### Example 9-8

## ------------------------------------------------------------------------
data("HousePricesUS", package = "pder")
price <- pdata.frame(HousePricesUS)$price
purtest(log(price), test = "levinlin", lags = 2, exo = "trend")
purtest(log(price), test = "madwu", lags = 2, exo = "trend")
purtest(log(price), test = "ips", lags = 2, exo = "trend")


#### Example 9-9

## ------------------------------------------------------------------------
tab5a <- matrix(NA, ncol = 4, nrow = 2)
tab5b <- matrix(NA, ncol = 4, nrow = 2)

for(i in 1:4) {
    mymod <- pmg(diff(log(income)) ~ lag(log(income)) + 
                 lag(diff(log(income)), 1:i),
                 data = HousePricesUS,
                 model = "mg", trend = TRUE)
    tab5a[1, i] <- pcdtest(mymod, test = "rho")$statistic
    tab5b[1, i] <- pcdtest(mymod, test =  "cd")$statistic
}

for(i in 1:4) {
    mymod <- pmg(diff(log(price)) ~ lag(log(price)) +
                 lag(diff(log(price)), 1:i),
                 data=HousePricesUS,
                 model="mg", trend = TRUE)
    tab5a[2, i] <- pcdtest(mymod, test = "rho")$statistic
    tab5b[2, i] <- pcdtest(mymod, test =  "cd")$statistic
}

tab5a <- round(tab5a, 3)
tab5b <- round(tab5b, 2)
dimnames(tab5a) <- list(c("income", "price"),
                        paste("ADF(", 1:4, ")", sep=""))
dimnames(tab5b) <- dimnames(tab5a)

tab5a
tab5b

## ------------------------------------------------------------------------
php <- pdata.frame(HousePricesUS)
cipstest(log(php$price), type = "drift")
cipstest(diff(log(php$price)), type = "none")

## ------------------------------------------------------------------------
cipstest(resid(ccemgmod), type="none")
cipstest(resid(ccepmod), type="none")


#### Example 10-2

## ------------------------------------------------------------------------
data("usaw49", package="pder")
library("plm")
php <- pdata.frame(HousePricesUS)
pcdtest(php$price, w = usaw49)

## ------------------------------------------------------------------------

if (requireNamespace("splm")){
    library("splm")
    rwtest(php$price, w = usaw49, replications = 999)
}

## ------------------------------------------------------------------------
mgmod <- pmg(log(price) ~ log(income), data = HousePricesUS)
ccemgmod <- pmg(log(price) ~ log(income), data = HousePricesUS, model = "cmg")
pcdtest(resid(ccemgmod), w = usaw49)
rwtest(resid(mgmod), w = usaw49, replications = 999)

## End(Not run)

Income and Migration, Household Data

Description

yearly observations of 317 households from 2000 to 2006

number of observations : 2219

number of time-series : 7

country : Indonesia

package : limdeppanel

JEL codes: F22, J43, O13, O15, Q11, Q12, R23

Chapter : 08

Usage

data(IncomeMigrationH)

Format

A dataframe containing:

household

household index

year

the year

migration

a dummy indicating whether a household has any migrant departing in year t+1

price

rice price shock

rain

rain shock

land

landholdings (ha)

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Bazzi, Samuel (2017) “Wealth Heterogeneity and the Income Elasticity of Migration”, American Economic Journal, Applied Economics, 9(2), 219–255, doi: 10.1257/app.20150548 .

Income and Migration, Village Data

Description

3-yearly observations of 44674 villages from 2005 to 2008

number of observations : 89348

number of time-series : 2

country : Indonesia

package : panellimdep

JEL codes: F22, J43, O13, O15, Q11, Q12, R23

Chapter : 08

Usage

data(IncomeMigrationV)

Format

A dataframe containing:

village

village index

year

the year

emigration

share of the emigrants in the total population

district

the district of the village

price

rice price shock

rain

rain shock

pareto

Pareto parameter of the landholdings distribution

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Bazzi, Samuel (2017) “Wealth Heterogeneity and the Income Elasticity of Migration”, American Economic Journal, Applied Economics, 9(2), 219–255, doi: 10.1257/app.20150548 .

JEL codes

Description

Inequality and Growth

Description

5-yearly observations of 266 world from 1961 to 1995

number of observations : 1862

number of time-series : 7

country : country

package : panel

JEL codes: O47, O15, C23, C33, O16

Chapter : 07

Usage

data(IneqGrowth)

Format

A dataframe containing:

country

country name

period

the period

growth

growth rate

yssw

years of secondary schooling among women, lagged

yssm

years of secondary schooling among men, lagged

pinv

price level of investment, lagged

lgdp

log initial gdp per capita

gini

gini index

Source

http://www.cgdev.org/content/publications/detail/14256

References

Forbes, Kristin J. (2000) “A Reassessment of the Relationship Between Inequality and Growth”, American Economic Review, 90(4), 869-887, doi: 10.1257/aer.90.4.869 .

Roodman, David (2009) “A Note on the Theme of Two Many Instruments”, Oxford Bulletin of Economics An Statistics, 71(1), 135–158, doi: 10.1111/j.1468-0084.2008.00542.x .

Politics and Land Reforms in India

Description

yearly observations of 89 villages from 1974 to 2003

number of observations : 2670

number of time-series : 30

country : India

package : panellimdep

JEL codes: D72, O13, O17, Q15

Chapter : 08

Usage

data(LandReform)

Format

A dataframe containing:

mouza

village id number

year

Year

district

District

rplacul

ratio of patta land registered to operational land

rpdrhh

ratio of pattadar households to total households (hh)

rblacul

ratio of barga land registered to operational land

rbgdrrghh

ratio of bargadar registered hh to total hh

election

election year dummy

preelect

preelection year dummy

edwalfco

to complete

erlesscu

interpolated landless hh, gi

ermgcu

interpolated mg hh, gi

ersmcu

interpolated sm hh, gi

ermdcu

interpolated md hh, gi

ercusmol

ratio of land below 5 acres cultivable NOT extrapolated

ercubgol

ratio of land above 12.5 acres cultivable

erillnb

interpolated ratio of illiterate non big hh

erlow

interpolated ratio of low caste hh

ratleft0

Left Front share in GP, == 0 for 1974

dwalfco

Assembly average vote difference LF-INC, district

inflat

Inflation in last 5 years in CPI for Agricultural Labourers

smfempyv

Year variation in Employment in Small Scale Industrial Units registered with Dir

incseats

INC seats / Total seats in Lok Sabha

lfseats

Ratio of LF seats in parliament

inflflag

Interaction between Inflation and ratleft lagged

inclflag

Interaction between INC seats and ratleft lagged

lflflag

Interaction between LF seats and ratleft lagged

ratleft

Left Front share in GP, ==share of assembly seats for 1974

infiw

to complete

infumme

to complete

infal

to complete

gp

Gran Panchayat

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Bardhan, Pranab and Dilip Mookherjee (2010) “Determinants of Redistributive Politics: An Empirical Analysis of Land Reform in West Bengal, India”, American Economic Review, 100(4), 1572–1600, doi: 10.1257/aer.100.4.1572 .

Late Budgets

Description

yearly observations of 48 States from 1978 to 2007

number of observations : 1440

number of time-series : 30

country : United States

package : limdeppanel

JEL codes: C78, D72, H61, H72

Chapter : 08

Usage

data(LateBudgets)

Format

A dataframe containing:

state

the state

year

the year

late

late budget ?

dayslate

number of days late for the budget

unempdiff

unemployment variation

splitbranch

split branch

splitleg

split legislature

elecyear

election year

endbalance

end of year balances in the general fund and stabilization fund

demgov

democrat governor ?

lameduck

lameduck

govexp

number of years since the incumbent governor took office

newgov

new governor ?

pop

the polulation

kids

percentage of population aged 5-17

elderly

percentage of population aged 65 or older

nocarry

does the state law does not allow a budget deficit to be carried over to the next fiscal year ?

supmaj

is a super majority required to pass each budget ?

fulltimeleg

full time legislature ?

shutdown

shutdown provision ?

black

percentage of blacks

graduate

percentage of graduates

censusresp

census response rate

fiveyear

five year dummies, one of '93-97', '98-02', '03-07'

deadline

is there a deadline ? one of 'none', 'soft' and 'hard'

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Andersen, Asger Lau; Lassen, David Dreyer and Lasse Holboll Westh Nielsen (2012) “Late Budgets”, American Economic Journal, Economic Policy, 4(4), 1-40, doi: 10.1257/pol.4.4.1 .

Examples


#### Example 8-4

## ------------------------------------------------------------------------
data("LateBudgets", package = "pder")
library("plm")
LateBudgets$dayslatepos <- pmax(LateBudgets$dayslate, 0)
LateBudgets$divgov <- with(LateBudgets, 
                           factor(splitbranch == "yes" | 
                                  splitleg == "yes", 
                                  labels = c("no", "yes")))
LateBudgets$unemprise <- pmax(LateBudgets$unempdiff, 0)
LateBudgets$unempfall <- - pmin(LateBudgets$unempdiff, 0)
form <- dayslatepos ~ unemprise + unempfall + divgov + elecyear + 
    pop + fulltimeleg + shutdown + censusresp + endbalance + kids + 
    elderly + demgov + lameduck + newgov + govexp + nocarry + 
    supmaj + black + graduate

## ------------------------------------------------------------------------
FEtobit <- pldv(form, LateBudgets)
summary(FEtobit)

Mafia and Public Spending

Description

yearly observations of 95 provinces from 1986 to 1999

number of observations : 1330

number of time-series : 14

country : Italy

package : panelivreg

JEL codes: D72, E62, H71, K42

Chapter : 06

Usage

data(Mafia)

Format

A dataframe containing:

province

the province (95)

region

the region (19)

year

the year

pop

the population

y

percentage growth of real per-capita value added

g

annual variation of the per-capita public investment in infrastructure divided by lagged real per-capita value added

cd

number of municipalities placed under the administration of external commissioners

cds1

same as cd, provided that the official deccree is publisehd in the first semester of the year

cds2

same as cd, provided that the average number of days betwen the dismissal of the city concil and the year end is less than 180

u1

change in the log of per-capita employment

u2

change in the log of per-capita hours of wage supplement provided by the unemployment insurance scheme

mafiosi

first difference of the number of people reported by the police forces to the judicial authority because of mafia-type association

extortion

first difference of the number of people reported by the police forces to the judicial authority because of extorsion

corruption1

first difference of the number of people reported by the police forces to the judicial authority because of corruption

corruption2

first difference of the number of crimes reported by the police forces to the judicial authority because of corruption

murder

first difference of the number of people reported by the police forces to the judicial authority because of murder related to mafia activity

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Acconcia, Antonio; Corsetti, Giancarlo and Saviero Simonelli (2014) “Mafia and Public Spending: Evidence on the Fiscal Multimplier Form a Quasi-experiment”, American Economic Review, 104(7), 2189-2209, doi: 10.1257/aer.104.7.2185 .

Magazine Prices

Description

yearly observations of 38 magazines from 1940 to 1980

number of observations : 1262

number of time-series : 41

country : United States

package : binomialpanel

JEL codes: L82

Chapter : 08

Usage

data(MagazinePrices)

Format

A dataframe containing:

year

the year

magazine

the magazine name

price

the price of the magazine in january

change

has the price changed between january of the current year and january of the following year ?

length

number of years since the previous price change

cpi

gdp deflator index

cuminf

cummulative change in inflation since the previous price change

sales

single copy sales of magazines for magazine industry

cumsales

cumulative change in magazine industry sales since previous price change

included

is the observation included in the econometric analysis ?

id

group index numbers used for the conditional logit estimation

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Willis, Jonathan L. (2006) “Magazine Prices Revisited”, Journal of Applied Econometrics, 21(3), 337-344, doi: 10.1002/jae.836 .

Cecchetti, Stephen G. (1986) “The Frequency of Price Adjustment, a Study of Newsstand Prices of Magazines”, Journal of Econometrics, 31, 255-274, doi: 10.1016/0304-4076(86)90061-8 .

Examples


#### Example 8-3

## ------------------------------------------------------------------------
data("MagazinePrices", package = "pder")
logitS <- glm(change ~ length + cuminf + cumsales, data = MagazinePrices, 
              subset = included == 1, family = binomial(link = 'logit'))
logitD <- glm(change ~ length + cuminf + cumsales + magazine, 
              data = MagazinePrices, 
              subset = included == 1, family = binomial(link = 'logit'))

if (requireNamespace("survival")){
    library("survival")
    logitC <- clogit(change ~ length + cuminf + cumsales + strata(id), 
                     data = MagazinePrices,
                     subset = included == 1)
    if (requireNamespace("texreg")){
        library("texreg")
        screenreg(list(logit = logitS, "FE logit" = logitD,
                       "cond. logit" = logitC), omit.coef = "magazine")
    }
}

R and D Performing Companies

Description

yearly observations of 509 firms from 1982 to 1989

number of observations : 4072

number of time-series : 8

country : United States

package : panel

JEL codes: C51, D24

Chapter : 07

Usage

data(RDPerfComp)

Format

A dataframe containing:

id

firm identifier

year

year

y

production in logs

n

labor in logs

k

capital in logs

Source

author's website https://www.nuffield.ox.ac.uk/users/bond/index.html

References

Blundell, Richard and Stephen Bond (2000) “GMM Estimation with Persistent Panel Data: An Application to Production Functions”, Econometric Reviews, 19(3), 321-340, doi: 10.1080/07474930008800475 .

Research and Development Spillovers Data

Description

a cross-section of 119 industries from 1980 to 2005

country : world

package : panel

JEL codes: C51, D24, O32, O33

Chapter : 04, 05, 09

Usage

data(RDSpillovers)

Format

A dataframe containing:

id

country-industry index

year

year

country

country

sector

manufacturing sector as SIC 15-37, excluding SIC 23

lny

log output

lnl

log of labour input

lnk

log of physical capital stock

lnrd

log of RD capital stock

Source

author's web site https://sites.google.com/site/medevecon/home

References

Eberhardt, M.; Helmers, C. and H. Strauss (2013) “Do Spillovers Matter in Estimating Private Returns to R and D?”, The Review of Economics and Statistics, 95(2), 436–448, doi: 10.1162/REST_a_00272 .

Examples

#### Example 4-10

## ------------------------------------------------------------------------
## Not run: 
data("RDSpillovers", package = "pder")
library("plm")
fm.rds <- lny ~ lnl + lnk + lnrd

## ------------------------------------------------------------------------
pcdtest(fm.rds, RDSpillovers)

## ------------------------------------------------------------------------
rds.2fe <- plm(fm.rds, RDSpillovers, model = "within", effect = "twoways")
pcdtest(rds.2fe)

## ------------------------------------------------------------------------
cbind("rho"  = pcdtest(rds.2fe, test = "rho")$statistic,
      "|rho|"= pcdtest(rds.2fe, test = "absrho")$statistic)


#### Example 5-10

## ------------------------------------------------------------------------
data("RDSpillovers", package = "pder")
pehs <- pdata.frame(RDSpillovers, index = c("id", "year"))
ehsfm <- lny ~ lnl + lnk + lnrd
phtest(ehsfm, pehs, method = "aux")

## ------------------------------------------------------------------------
phtest(ehsfm, pehs, method = "aux", vcov = vcovHC)


#### Example 5-15

## ------------------------------------------------------------------------
fm <- lny ~ lnl + lnk + lnrd

## ------------------------------------------------------------------------


if (requireNamespace("lmtest")){
    library("lmtest")
    gglsmodehs <- pggls(fm, RDSpillovers, model = "pooling")
    coeftest(gglsmodehs)

    feglsmodehs <- pggls(fm, RDSpillovers, model = "within")
    coeftest(feglsmodehs)
    
    phtest(gglsmodehs, feglsmodehs)
    
    fdglsmodehs <- pggls(fm, RDSpillovers, model = "fd")
    
    fee <- resid(feglsmodehs)
    dbfee <- data.frame(fee=fee, id=attr(fee, "index")[[1]])
    coeftest(plm(fee~lag(fee)+lag(fee,2), dbfee, model = "p", index="id"))
    
    fde <- resid(fdglsmodehs)
    dbfde <- data.frame(fde=fde, id=attr(fde, "index")[[1]])
    coeftest(plm(fde~lag(fde)+lag(fde,2), dbfde, model = "p", index="id"))
    
    coeftest(fdglsmodehs)
}


#### Example 9-7

## ------------------------------------------------------------------------
ccep.rds <- pcce(fm.rds, RDSpillovers, model="p")
if (requireNamespace("lmtest")){
    library("lmtest")
    ccep.tab <- cbind(coeftest(ccep.rds)[, 1:2],
                      coeftest(ccep.rds, vcov = vcovNW)[, 2],
                      coeftest(ccep.rds, vcov = vcovHC)[, 2])
    dimnames(ccep.tab)[[2]][2:4] <- c("Nonparam.", "vcovNW", "vcovHC")
    round(ccep.tab, 3)
}


## ------------------------------------------------------------------------
autoreg <- function(rho = 0.1, T = 100){
  e <- rnorm(T+1)
  for (t in 2:(T+1)) e[t] <- e[t]+rho*e[t-1]
  e
}
set.seed(20)

f <- data.frame(time = rep(0:40, 2), 
                rho = rep(c(0.2, 1), each = 41),
                y = c(autoreg(rho = 0.2, T = 40), 
                      autoreg(rho = 1, T = 40)))
if (requireNamespace("ggplot2")){
    library("ggplot2")
    ggplot(f, aes(time, y)) + geom_line() + facet_wrap(~ rho) + xlab("") + ylab("")

    autoreg <- function(rho = 0.1, T = 100){
        e <- rnorm(T)
        for (t in 2:(T)) e[t] <- e[t] + rho *e[t-1]
        e
    }
    tstat <- function(rho = 0.1, T = 100){
        y <- autoreg(rho, T)
        x <- autoreg(rho, T)
        z <- lm(y ~ x)
        coef(z)[2] / sqrt(diag(vcov(z))[2])
    }
    result <- c()
    R <- 1000
    for (i in 1:R) result <- c(result, tstat(rho = 0.2, T = 40))
    quantile(result, c(0.025, 0.975))
    prop.table(table(abs(result) > 2))


    result <- c()
    R <- 1000
    for (i in 1:R) result <- c(result, tstat(rho = 1, T = 40))
    quantile(result, c(0.025, 0.975))
    prop.table(table(abs(result) > 2))

    
    R <- 1000
    T <- 100
    result <- c()
    for (i in 1:R){
        y <- autoreg(rho=1, T=100)
        Dy <- y[2:T] - y[1:(T-1)]
        Ly <- y[1:(T-1)]
        z <- lm(Dy ~ Ly)
        result <- c(result, coef(z)[2] / sqrt(diag(vcov(z))[2]))
    }

    ggplot(data.frame(x = result), aes(x = x)) + 
        geom_histogram(fill = "white", col = "black", 
                       bins = 20, aes(y = ..density..)) +
        stat_function(fun = dnorm) + xlab("") + ylab("")


    prop.table(table(result < -1.64))
}

## End(Not run)

Deficits and Reelection

Description

yearly observations of 75 countries from 1960 to 2003

number of observations : 439

number of time-series : 16

country : world

package : panelbinomial

JEL codes: D72, E62, H62, O47

Chapter : 08

Usage

data(Reelection)

Format

A dataframe containing:

country

the country

year

the year

narrow

TRUE if the observation belongs to the narrow data set

reelect

one if the incumbent was reelected and zero otherwise

ddefterm

the change in the ratio of the government surplus to gdp in the two years preeceding the election year, relative to the two previous years

ddefey

the change in the government surplus ratio to gdpin the election year, compared to the previous year

gdppc

the average growth rate of real per capita gdp during the leader's current term

dev

one for developped countries, 0 otherwise

nd

one for a new democratic country, 0 otherwise

maj

one for majoritarian electoral system, 0 otherwise

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Adi Brender and Allan Drazen (2008) “How Do Budget Deficits and Economic Growth Affect Reelection Prospects? Evidence From a Large Panel of Countries”, American Economic Review, 98(5), 2203-2220, doi: 10.1257/aer.98.5.2203 .

Examples



#### Example 8-1

## ------------------------------------------------------------------------
## Not run: 
library("plm")
data("Reelection", package = "pder")

## ------------------------------------------------------------------------
elect.l <- glm(reelect ~ ddefterm + ddefey + gdppc + dev + nd + maj, 
          data = Reelection, family = "binomial", subset = narrow)
l2 <- update(elect.l, family = binomial)
l3 <- update(elect.l, family = binomial())
l4 <- update(elect.l, family = binomial(link = 'logit'))

## ------------------------------------------------------------------------
elect.p <- update(elect.l, family = binomial(link = 'probit'))

## ------------------------------------------------------------------------

if (requireNamespace("pglm")){
    library("pglm")
    elect.pl <- pglm(reelect ~ ddefterm + ddefey + gdppc + dev + nd + maj, 
                 Reelection, family = binomial(link = 'logit'), 
                subset = narrow)
    elect.pp <- pglm(reelect ~ ddefterm + ddefey + gdppc + dev + nd + maj, 
                 Reelection, family = binomial(link = 'probit'), 
                subset = narrow)
}

## End(Not run)

Interregional Redistribution and Inequalities

Description

yearly observations of 17 countries from 1982 to 1999

number of observations : 102

number of time-series : 6

country : oecd

package : panel

JEL codes: D72, H23, H71, H73, H77, R12, R23

Chapter : 07

Usage

data(RegIneq)

Format

A dataframe containing:

country

the country

period

the period

regineq

coefficient of variatio of regional gdp per capita

gdppc

real gross domestic product per capita

pop

total population

popgini

gini coefficient of regional population size

urban

share of urban living population

social

total government social expenditures as share of gdp

unempl

unemployment rate

dec

sub-national expenditures as share of total government expenditures

transrev

grants received by national and sub-national governments from other levels of government as share of total government revenues

transaut

sub-national non autonomous revenues as share of total government revenues

Source

Review of Economic Studies' web site https://academic.oup.com/restud

References

Anke S. Kessler and Nico A. Hansen and Christian Lessmann (2011) “Interregional Redistribution and Mobility in Federations: a Positive Approach”, Review of Economic Studies, 78(4), 1345-1378, doi: 10.1093/restud/rdr003 .

The Long-run Effects of the Scramble for Africa

Description

a pseudo-panel of 49 countries

number of observations : 1212

number of individual observations : 2-112

country : Africa

package : countpanel

JEL codes: D72, D74, F51, J15, O15, O17, Z13

Chapter : 08

Usage

data(ScrambleAfrica)

Format

A dataframe containing:

country

country code

group

ethnic group name

conflicts

number of conflicts

split

dummy for partitioned ethnic area

spillover

spillover index, the fraction of adjacent groups in the same country that are partitioned

region

the region

pop

population according to the first post-independance census

area

land area

lake

lakes dummy

river

rivers dummy

capital

dummy if a capital city falls in the homeland of an ethnic group

borderdist

distance of the centroid of the area from the national border

capdist

distance of the centroid of the area from the capital

seadist

distance of the centroid of the area from the sea coast

coastal

dummy for areas that are by the sea coast

meanelev

mean elevation

agriculture

index of land suitability for agriculture

diamond

diamond mine indicator

malaria

malaria stability index

petroleum

oil field indicator

island

island dummy

city1400

dummy for areas with major city in 1400

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Michalopoulos, Stelios and Elias Papaioannou (2016) “The Long-run Effects of the Scramble for Africa”, American Economic Review, 106(7), 1802–1848, doi: 10.1257/aer.20131311 .

Seat Belt Usage and Traffic Fatalities

Description

yearly observations of 51 states from 1983 to 1997

number of observations : 765

number of time-series : 15

country : United States

package : panel

JEL codes: R41, K42

Chapter : 06

Usage

data(SeatBelt)

Format

A dataframe containing:

state

the state code

year

the year

farsocc

the number of traffic fatalities of drivers and passengers (of any seating position) of a motor vehicule in transport

farsnocc

the number of traffic fatalities of pedestrians and bicyclists

usage

rate of seat belt usage

percapin

median income in current US dollars

unemp

unemployment rate

meanage

mean age

precentb

the percentage of african-americans in the state population

precenth

the percentage of people of hispanic origin in the state population

densurb

traffic density urban ; registered vehicules per unit length of urban roads in miles

densrur

traffic density rural ; registered vehicules per unit length of urban roads in miles

viopcap

number of violent crimes (homicide, rape and robbery) per capita

proppcap

number of preperty rimes (burglary, larceny and auto theft) per capita

vmtrural

vehicule miles traveled on rural roads

vmturban

vehicule miles traveled on urban roads

fueltax

fuel tax (in curent cents)

lim65

65 miles per hour speed limit (55 mph is the base category)

lim70p

70 miles per hour or above speed limit (55 mph is the base caegory)

mlda21

a dummy variable that is equal to 1 for a minimum for a minimum legal drinking age of 21 years (18 years is the base category)

bac08

a dummy variable that is equal to 1 foe a maximum of 0.08 blood alcohol content (0.1 is the base category)

ds

a dummy equal to 1 for the periods in which the state had a secondary-enforcement mandatory seat belt law, or a primary-enforcement law that preceded by a secondary-enforcement law (no seat belt law is the base category)

dp

a dummy variable eqal to 1 for the periods in which the state had a primary-enforcement mandatory seat belt law that was not preceded by a secondary-enforcement law (no seat belt is the base category)

dsp

a dummy variable equal to 1 for the periods in which the state had a primary-enforcement mandatory seat belt law that was preceded by a secondary enforcement law (no seat belt law is the base category

Source

author's website https://leinav.people.stanford.edu

References

Cohen, Alma and Liran Einav (2003) “The Effects of Mandatory Seat Belt Laws on Driving Behavior and Traffic Fatalities”, The Review of Economics and Statistics, 85(4), 828-843, doi: 10.2139/ssrn.293582 .

Examples

#### Example 6-1

## ------------------------------------------------------------------------
## Not run: 
library("plm")

## ------------------------------------------------------------------------
y ~ x1 + x2 + x3 | x1 + x3 + z
y ~ x1 + x2 + x3 | . - x2 + z

## ------------------------------------------------------------------------

data("SeatBelt", package = "pder")
SeatBelt$occfat <- with(SeatBelt, log(farsocc / (vmtrural + vmturban)))
ols <- plm(occfat ~ log(usage) + log(percapin) + log(unemp) + log(meanage) + 
           log(precentb) + log(precenth)+ log(densrur) + 
           log(densurb) + log(viopcap) + log(proppcap) +
           log(vmtrural) + log(vmturban) + log(fueltax) +
           lim65 + lim70p + mlda21 + bac08, SeatBelt, 
           effect = "time")
fe <- update(ols, effect = "twoways")
ivfe <- update(fe, . ~ . |  . - log(usage) + ds + dp +dsp)

rbind(ols = coef(summary(ols))[1,],
      fe = coef(summary(fe))[1, ],
      w2sls = coef(summary(ivfe))[1, ])

## ------------------------------------------------------------------------
SeatBelt$noccfat <- with(SeatBelt, log(farsnocc / (vmtrural + vmturban)))
nivfe <- update(ivfe, noccfat ~ . | .)
coef(summary(nivfe))[1, ]

## End(Not run)

Intergenerationals Experiments

Description

a pseudo-panel of 159 Individuals

number of observations : 2703

number of individual observations : 17

country : France

package : panellimdep

JEL codes: C90, J14, J26, M12, M51

Chapter : 08

Usage

data(Seniors)

Format

A dataframe containing:

id

individual number of each subject

period

from 1 to 17

session

from 1 to 12

firm

1 if working subject, 0 otherwise

firmx

1 if the firm is X, 0 if the firm is Y

order

1 if the treatment with no information on the generation of the group is played first in the Public Good game, 0 otherwise

gender

1 if male subject, 0 if female subject

manager

1 if the subject is a manager, 0 otherwise

student

1 if the subject is a student, 0 otherwise

retir

1 if retiree, 0 otherwise

senior

1 if the subject is a senior, 0 otherwise

seniord

1 if the subject reports s/he is a senior, 0 if junior

workingsenior

1 if the subject is a working senior, 0 otherwise

workingjunior

1 if the subject is a working junior, 0 otherwise

information

1 if information is given on the generation composition of the group, 0 otherwise

nbseniors

number of seniors in the group, excluding the subject

homogend

1 if the group is homogenous in terms of declared generation, 0 otherwise

homodgenck

1 if the group is homogenous in terms of declared generation and this is common information, 0 otherwise

contribution

amount of the contribution to the public good (from 0 to 20)

pot

amount of the public good (from 0 to 60)

potlag

amount of the public good in the previous period (from 0 to 60)

potimean

amount of the public good, excluding the subject's contribution (from 0 to 40)

potimeanlag

amount of the public good in the previous period, excluding the subject's contribution (from 0 to 40)

payoffpggame

payoff in the public good game

desirnbseniors

desired number of seniors co-participants in the Selection treatment (from 0 to 2)

invest

amount invested in the risky lotery

payoffriskgame

payoff in the investment game

letters

1 if letters are A M F U R I P , 0 if they are OATFNED

idicompet

individual number of the co-participant in the Task game

seniordopponent

1 if the co-participant in the Task game reports s/he is a senior, 0 otherwise

seniori

1 if the co-participant in the Task game is a senior

option

1 if the subject has chosen the tournament, 0 otherwise

option0

1 if the co-participant has chosen the tournament, 0 otherwise

twoperstour

1 if both participants have chosen the tournament, 0 otherwise

beliefself

number of words the subject believes s/he will create

beliefseniors

number of words the subject believes the seniors will create on average

beliefjuniors

number of words the subject believes the juniors will create on average

beliefsmatchs

number of words the subject believes the seniors will create on average when matched with a senior

beliefjmatchj

number of words the subject believes the juniors will create on average when matched with a junior

relatabil

1 if the subject believes s/he can create more words than the generation of his/her co-participant, 0 otherwise

performance

number of words actually created

perfi

number of words actually created by the co-participant

payoffcompetitiongame

payoff in the Task game

expesenck

1 if the subject has been informed that s/he was interacting with seniors in the Public Good game, 0 otherwise

potlagsenior

Amount of the pot in the previous period * the subject is a senior

heterogend

1 if the group mixes the two generations, 0 otherwise

Source

American Economic Association Data Archive : https://www.aeaweb.org/aer/

References

Charness, Gary and Marie-Claire Villeval (2009) “Cooperation and Competition in Intergenerational Experiments in the Field and the Laboratory”, American Economic Review, 99(3), 956–978, doi: 10.1257/aer.99.3.956 .

Growth Model

Description

yearly observations of 97 countries from 1960 to 1985

number of observations : 576

number of time-series : 6

country : world

package : panel

JEL codes: O47, O41

Chapter : 07

Usage

data(Solow)

Format

A dataframe containing:

id

country id

year

year

lgdp

log of gdp per capita

lsrate

log of the saving rate, approximated by the investement rate

lpopg

log of population growth + 0.05 (which is an approximation of the sum of the rate of labor-augmenting technological progress and of the rate of depreciation of physical capital)

Source

author's website https://www.nuffield.ox.ac.uk/users/bond/index.html

References

Caselli, Francesco; Esquivel, Gerardo and Fernando Lefort (1996) “Reopening the Convergence Debate: a New Look at Cross-country Growth Empirics”, Journal of Economic Growth, 1, 363-389, doi: 10.1007/BF00141044 .

Bond, Stephen; Hoeffler, Anke and Johnatan Temple (2001) “GMM Estimation of Empirical Growth Model”, CEPR Discussion Paper, 3048, 1-33.

Production of Electricity in Texas

Description

yearly observations of 10 firms from 1966 to 1983

number of observations : 180

number of time-series : 18

country : Texas

package : productionpanel

JEL codes: D24, C13, C51, C23, J31

Chapter : 02, 03

Usage

data(TexasElectr)

Format

A dataframe containing:

id

the firm identifier

year

the year, from 1966 to 1983

output

output

pfuel

price of fuel

plab

price of labor

pcap

price of capital

expfuel

expense in fuel

explab

expense in labor

expcap

expense in capital

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Kumbhakar SC (1996) “Estimation of Cost Efficiency with Heteroscedasticity: An Application to Electric Utilities”, Journal of the Royal Statistical Society, Series D, 45, 319–335.

Horrace and Schmidt (1996) “Confidence Statements for Efficiency Estimates From Stochastic Frontier Models”, Journal of Productity Analysis, 7, 257–282, doi: 10.1007/BF00157044 .

Horrace and Schmidt (2012) “Multiple Comparisons with the Best, with Economic Applications”, Journal of Applied Econometrics, 15(1), 1–26, doi: 10.1002/(SICI)1099-1255(200001/02)15:1<1::AID-JAE551>3.0.CO;2-Y .

Examples

#### Example 2-6

## ------------------------------------------------------------------------
data("TexasElectr", package = "pder")
library("plm")
TexasElectr$cost <- with(TexasElectr, explab + expfuel + expcap)
TE <- pdata.frame(TexasElectr)
summary(log(TE$output))
ercomp(log(cost) ~ log(output), TE)
models <- c("within", "random", "pooling", "between")
sapply(models, function(x)
       coef(plm(log(cost) ~ log(output), TE, model = x))["log(output)"])

#### Example 3-2

## ------------------------------------------------------------------------
data("TexasElectr", package = "pder")

if (requireNamespace("dplyr")){
    library("dplyr")
    TexasElectr <- mutate(TexasElectr,
                          pf = log(pfuel / mean(pfuel)),
                          pl = log(plab / mean(plab)) - pf,
                          pk = log(pcap / mean(pcap)) - pf)

## ------------------------------------------------------------------------
    TexasElectr <- mutate(TexasElectr, q = log(output / mean(output)))

## ------------------------------------------------------------------------
    TexasElectr <- mutate(TexasElectr,
                          C = expfuel + explab + expcap,
                          sl = explab / C,
                          sk = expcap / C,
                          C = log(C / mean(C)) - pf)
    
## ------------------------------------------------------------------------
    TexasElectr <- mutate(TexasElectr,
                          pll = 1/2 * pl ^ 2,
                          plk = pl * pk,
                          pkk = 1/2 * pk ^ 2,
                          qq = 1/2 * q ^ 2)

## ------------------------------------------------------------------------
    cost <- C ~ pl + pk + q + pll + plk + pkk + qq
    shlab <- sl ~ pl + pk
    shcap <- sk ~ pl + pk

## ------------------------------------------------------------------------
    R <- matrix(0, nrow = 6, ncol = 14)
    R[1, 2] <- R[2, 3] <- R[3, 5] <- R[4, 6] <- R[5, 6] <- R[6, 7] <- 1
    R[1, 9] <- R[2, 12] <- R[3, 10] <- R[4, 11] <- R[5, 13] <- R[6, 14] <- -1

## ------------------------------------------------------------------------
    z <- plm(list(cost = C ~ pl + pk + q + pll + plk + pkk + qq,
                  shlab = sl ~ pl + pk,
                  shcap = sk ~ pl + pk),
             TexasElectr, model = "random",
             restrict.matrix = R)
    summary(z)
}

Production of Tileries in Egypt

Description

weeklyly observations of 25 firms from 1982 to 1983

number of observations : 483

number of time-series : 22

country : Egypt

package : panelproduction

JEL codes: D24, C13, C51, C23, J31

Chapter : 01, 03

Usage

data(Tileries)

Format

A dataframe containing:

id

firm id

week

week (3 weeks aggregated)

area

one of "fayoum" and "kalyubiya"

output

output

labor

labor hours

machine

machine hours

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Horrace and Schmidt (1996) “Confidence Statements for Efficiency Estimates From Stochastic Frontier Models”, Journal of Productity Analysis, 7, 257–282, doi: 10.1007/BF00157044 .

Horrace and Schmidt (2012) “Multiple Comparisons with the Best, with Economic Applications”, Journal of Applied Econometrics, 15(1), 1–26, doi: 10.1002/(SICI)1099-1255(200001/02)15:1<1::AID-JAE551>3.0.CO;2-Y .

Seale J.L. (1990) “Estimating Stochastic Frontier Systems with Unbalanced Panel Data: the Case of Floor Tile Manufactories in Egypt”, Journal of Applied Econometrics, 5, 59–79, doi: 10.1002/jae.3950050105 .

Examples

#### Example 1-2

## ------------------------------------------------------------------------
data("Tileries", package = "pder")
library("plm")
coef(summary(plm(log(output) ~ log(labor) + machine, data = Tileries,
             subset = area == "fayoum")))

## ------------------------------------------------------------------------
coef(summary(plm(log(output) ~ log(labor) + machine, data = Tileries,
             model = "pooling", subset = area == "fayoum")))


#### Example 1-5

## ------------------------------------------------------------------------
data("Tileries", package = "pder")
til.fm <- log(output) ~ log(labor) + log(machine)
lm.mod <- lm(til.fm, data = Tileries, subset = area == "fayoum")

## ------------------------------------------------------------------------
if (requireNamespace("car")){
    library("car")
    lht(lm.mod, "log(labor) + log(machine) = 1")

## ------------------------------------------------------------------------
    library("car")
    lht(lm.mod, "log(labor) + log(machine) = 1", vcov=vcovHC)
}


#### Example 1-6

## ------------------------------------------------------------------------
plm.mod <- plm(til.fm, data = Tileries, subset = area == "fayoum")

## ------------------------------------------------------------------------
if (requireNamespace("car")){
    library("car")
    lht(plm.mod, "log(labor) + log(machine) = 1", vcov = vcovHC)
}

#### Example 3-1

## ------------------------------------------------------------------------
library(plm)
data("Tileries", package = "pder")
head(Tileries, 3)
pdim(Tileries)

## ------------------------------------------------------------------------
Tileries <- pdata.frame(Tileries)
plm.within <- plm(log(output) ~ log(labor) + log(machine), Tileries)
y <- log(Tileries$output)
x1 <- log(Tileries$labor)
x2 <- log(Tileries$machine)
lm.within <- lm(I(y - Between(y)) ~ I(x1 - Between(x1)) + I(x2 - Between(x2)) - 1)
lm.lsdv <- lm(log(output) ~ log(labor) + log(machine) + factor(id), Tileries)
coef(lm.lsdv)[2:3]
coef(lm.within)
coef(plm.within)

## ------------------------------------------------------------------------
tile.r <- plm(log(output) ~ log(labor) + log(machine), Tileries, model = "random")
summary(tile.r)

## ------------------------------------------------------------------------
plm.within <- plm(log(output) ~ log(labor) + log(machine),
                  Tileries, effect = "twoways")
lm.lsdv <- lm(log(output) ~ log(labor) + log(machine) +
                  factor(id) + factor(week), Tileries)
y <- log(Tileries$output)
x1 <- log(Tileries$labor)
x2 <- log(Tileries$machine)
y <- y - Between(y, "individual") - Between(y, "time") + mean(y)
x1 <- x1 - Between(x1, "individual") - Between(x1, "time") + mean(x1)
x2 <- x2 - Between(x2, "individual") - Between(x2, "time") + mean(x2)
lm.within <- lm(y ~ x1 + x2 - 1)
coef(plm.within)
coef(lm.within)
coef(lm.lsdv)[2:3]

## ------------------------------------------------------------------------
wh <- plm(log(output) ~ log(labor) + log(machine), Tileries,
          model = "random", random.method = "walhus",
          effect = "twoways")
am <- update(wh, random.method = "amemiya")
sa <- update(wh, random.method = "swar")
ercomp(sa)

## ------------------------------------------------------------------------
re.models <- list(walhus = wh, amemiya = am, swar = sa)
sapply(re.models, function(x) sqrt(ercomp(x)$sigma2))
sapply(re.models, coef)

The Q Theory of Investment

Description

yearly observations of 188 firms from 1951 to 1985

number of observations : 6580

number of time-series : 35

country : United States

package : panel

Chapter : 02

Usage

data(TobinQ)

Format

A dataframe containing:

cusip

compustat's identifying number

year

year

isic

sic industry classification

ikb

investment divided by capital : broad definition

ikn

investment divided by capital : narrow definition

qb

Tobin's Q : broad definition

qn

Tobin's Q : narrow definition

kstock

capital stock

ikicb

investment divided by capital with imperfect competition : broad definition

ikicn

investment divided by capital with imperfect competition : narrow definition

omphi

one minus phi (see the article p. 320)

qicb

Tobin's Q with imperfect competition : broad definition

qicn

Tobin's Q with imperfect competition : narrow definition

sb

S (see equation 10 p. 320) : broad definition

sn

S (see equation 10 p. 320) : narrow definition

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Schaller, Huntley (1990) “A Re-examination of the Q Theory of Investment Using U.S. Firm Data”, Journal of Applied Econometrics, 5(4), 309–325, doi: 10.1002/jae.3950050402 .

Examples

#### Example 2-1

## ------------------------------------------------------------------------
## Not run: 
library("plm")
data("TobinQ", package = "pder")

## ------------------------------------------------------------------------
pTobinQ <- pdata.frame(TobinQ)
pTobinQa <- pdata.frame(TobinQ, index = 188)
pTobinQb <- pdata.frame(TobinQ, index = c('cusip'))
pTobinQc <- pdata.frame(TobinQ, index = c('cusip', 'year'))

## ------------------------------------------------------------------------
pdim(pTobinQ)

## ----results = 'hide'----------------------------------------------------
pdim(TobinQ, index = 'cusip')
pdim(TobinQ)

## ------------------------------------------------------------------------
head(index(pTobinQ))

## ------------------------------------------------------------------------
Qeq <- ikn ~ qn
Q.pooling <- plm(Qeq, pTobinQ, model = "pooling")
Q.within <- update(Q.pooling, model = "within")
Q.between <- update(Q.pooling, model = "between")

## ------------------------------------------------------------------------
Q.within
summary(Q.within)

## ------------------------------------------------------------------------
head(fixef(Q.within))
head(fixef(Q.within, type = "dfirst"))
head(fixef(Q.within, type = "dmean"))

## ------------------------------------------------------------------------
head(coef(lm(ikn ~ qn + factor(cusip), pTobinQ)))


#### Example 2-2

## ------------------------------------------------------------------------
Q.swar <- plm(Qeq, pTobinQ, model = "random", random.method = "swar")
Q.swar2 <- plm(Qeq, pTobinQ, model = "random",
               random.models = c("within", "between"),
               random.dfcor = c(2, 2))
summary(Q.swar)

## ------------------------------------------------------------------------
ercomp(Qeq, pTobinQ)
ercomp(Q.swar)

## ------------------------------------------------------------------------
Q.walhus <- update(Q.swar, random.method = "swar")
Q.amemiya <- update(Q.swar, random.method = "amemiya")
Q.nerlove <- update(Q.swar, random.method = "nerlove")
Q.models <- list(swar = Q.swar, walhus = Q.walhus,
                 amemiya = Q.amemiya, nerlove = Q.nerlove)
sapply(Q.models, function(x) ercomp(x)$theta)
sapply(Q.models, coef)


#### Example 2-3

## ------------------------------------------------------------------------
sapply(list(pooling = Q.pooling, within = Q.within,
            between = Q.between, swar = Q.swar),
       function(x) coef(summary(x))["qn", c("Estimate", "Std. Error")])

## ------------------------------------------------------------------------
summary(pTobinQ$qn)

## ------------------------------------------------------------------------
SxxW <- sum(Within(pTobinQ$qn) ^ 2)
SxxB <- sum((Between(pTobinQ$qn) - mean(pTobinQ$qn)) ^ 2)
SxxTot <- sum( (pTobinQ$qn - mean(pTobinQ$qn)) ^ 2)
pondW <- SxxW / SxxTot
pondW
pondW * coef(Q.within)[["qn"]] +
  (1 - pondW) * coef(Q.between)[["qn"]]

## ------------------------------------------------------------------------
T <- 35
N <- 188
smxt2 <- deviance(Q.between) * T / (N - 2)
sidios2 <- deviance(Q.within) / (N * (T - 1) - 1)
phi <- sqrt(sidios2 / smxt2)

## ------------------------------------------------------------------------
pondW <- SxxW / (SxxW + phi^2 * SxxB)
pondW
pondW * coef(Q.within)[["qn"]] +
  (1 - pondW) * coef(Q.between)[["qn"]]

#### Example 2-8

## ------------------------------------------------------------------------
Q.models2 <- lapply(Q.models, function(x) update(x, effect = "twoways"))
sapply(Q.models2, function(x) sqrt(ercomp(x)$sigma2))
sapply(Q.models2, function(x) ercomp(x)$theta)

## End(Not run)

Trade in the European Union

Description

yearly observations of 91 pairs of countries from 1960 to 2001

number of observations : 3822

number of time-series : 42

country : Europe

package : gravity

JEL codes: C51, F14

Chapter : 06

Usage

data(TradeEU)

Format

A dataframe containing:

year

the year

pair

a pair of countries

trade

the sum of logged exports and imports, bilateral trade flow

gdp

the sum of the logged real GDPs

sim

a measure of similarity between two trading countries;

rlf

a measure of relative factor endowments;

rer

the logged bilateral real exchange rate;

cee

a dummy equal to 1 when both belong to European Community;

emu

a dummy equal to 1 when both adopt the common currency;

dist

the geographical distance between capital cities;

bor

a dummy equal to 1 when the trading partners share a border;

lan

a dummy equal to 1 when both speak the same language;

rert

the logarithm of real exchange rates between the European currencies and the U.S. dollar;

ftrade

the time specific common factors (individual means) of the variables trade

fgdp

the time specific common factors (individual means) of the variables gdp

fsim

the time specific common factors (individual means) of the variables sim

frlf

the time specific common factors (individual means) of the variables rlf

frer

the time specific common factors (individual means) of the variables rer

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Serlenga, Laura and Yongcheol Shin (2007) “Gravity Models of Intra-eu Trade: Application of the Ccep-ht Estimation in Heterogenous Panels with Unobserved Common Time-specific Factors”, Journal of Applied Econometrics, 22, 361–381, doi: 10.1002/jae.944 .

Examples

#### Example 6-3

## ------------------------------------------------------------------------
## Not run: 
data("TradeEU", package = "pder")
library("plm")

## ------------------------------------------------------------------------
ols <- plm(trade ~ gdp + dist + rer + rlf + sim + cee + emu + bor + lan, TradeEU, 
          model = "pooling", index = c("pair", "year"))
fe <- update(ols, model = "within")
fe

## ------------------------------------------------------------------------
re <- update(fe, model = "random")
re

## ------------------------------------------------------------------------
phtest(re, fe)

## ----results='hide'------------------------------------------------------
ht1 <- plm(trade ~ gdp + dist + rer + rlf + sim + cee + emu + bor + lan | 
           rer + dist + bor | gdp + rlf + sim + cee + emu + lan , 
           data = TradeEU, model = "random", index = c("pair", "year"), 
           inst.method = "baltagi", random.method = "ht")
ht2 <- update(ht1, trade ~ gdp + dist + rer + rlf + sim + cee + emu + bor + lan | 
              rer + gdp + rlf + dist + bor| sim + cee + emu + lan)

## ------------------------------------------------------------------------
phtest(ht1, fe)
phtest(ht2, fe)

## ------------------------------------------------------------------------
ht2am <- update(ht2, inst.method = "am")

## ------------------------------------------------------------------------
phtest(ht2am, fe)

## End(Not run)

Trade and Foreign Direct Investment in Germany and the United States

Description

yearly observations of 490 combinations of countries / industries from 1989 to 1999

number of observations : 3860

number of time-series : 11

country : Germany and United States

package : gravity

JEL codes: F12, F14, F21, F23

Chapter : 06

Usage

data(TradeFDI)

Format

A dataframe containing:

id

id

year

time period

country

country name

indusid

industry code

importid

importer code

lrex

log real bilateral exports

lrfdi

log real bilateral outward stocks of FDI

lgdt

log sum of bilateral real GDP

lsimi

log (1-[exporter GDP/(exporter+importer GDP)]^2- [exporter GDP/(exporter+importer GDP)]^2)

lrk

log (real capital stock of exporter/real capital stock of importer)

lrh

log (secondary school enrolment of exporter/secondary school enrolment of importer)

lrl

log (labor force of exporter/labor force of importer)

ldist

log bilateral distance between exporter and importer

lkldist

(lrk-lrl) * ldist

lkgdt

abs(lrk)*lgdt

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Peter Egger and Michael Pfaffermayr (2004) “Distance, Trade, and Fdi: A Hausman-taylor Sur Approach”, Journal of Applied Econometrics, 19(2), 227–246, doi: 10.1002/jae.721 .

Turkish Banks

Description

yearly observations of 53 banks from 1990 to 2000

number of observations : 583

number of time-series : 11

country : Turkey

package : productionpanel

JEL codes: D24, G21, L33

Chapter : 02

Usage

data(TurkishBanks)

Format

A dataframe containing:

id

bank id

year

the years

type

one of "conventional" and "islamic"

pl

price of labor

pf

price of borrowed funds

pk

price of physical capital

output

output, total loans

cost

total cost

empexp

employee expenses

nbemp

number of employees

faexp

assets expenses

fa

fixed assets

intexp

total interest expenses (interest on deposits and non-deposit funds + other interest expenses),

bfunds

borrowed funds (deposits + non-deposit funds)

dep

deposits

nondep

non-deposits

npl

non performing loans

ec

equity capital

quality

quality index

rindex

risk index

ta

total assets

ts

total securities (only for conventional banks)

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Mahmoud A. El-Gamal and Hulusi Inanoglu (2005) “Inefficiency and Heterogeneity in Turkish Banking: 1990-2000”, Journal of Applied Econometrics, 20(5), 641–664, doi: 10.1002/jae.835 .

Examples

#### Example 2-5

## ------------------------------------------------------------------------
data("TurkishBanks", package = "pder")
library("plm")
TurkishBanks <- na.omit(TurkishBanks)
TB <- pdata.frame(TurkishBanks)
summary(log(TB$output))
ercomp(log(cost) ~ log(output), TB)
models <- c("within", "random", "pooling", "between")
sapply(models, function(x)
       coef(plm(log(cost) ~ log(output), TB, model = x))["log(output)"])

Costs of Currency and Banking Crises

Description

yearly observations of 22 countries from 1970 to 1997

number of observations : 616

number of time-series : 28

country : world

package : panel

JEL codes: F32, G15, G21, O16, O19, O47

Chapter : 06

Usage

data(TwinCrises)

Format

A dataframe containing:

country

the country name

year

the year

gdp

real gdp growth

pubsurp

change in budget surplus to real gdp ratio

credit

credit growth

extgdp

external growth rates (weight average)

exr

real exchange rate overvaluation

open

openess

curcrises

currency crises

bkcrises

banking crises

twin

twin crises

area

a factor with levels 'other', 'asia' and 'latam' (for latin America)

Source

Journal of Money, Credit and Banking : https://jmcb.osu.edu/archive

References

Hutchison, Michael M. and Ilan Noy (2005) “How Bad Are Twins ? Output Costs of Currency and Banking Crises”, Journal of Money, Credit and Banking, 37(4), 725–752.

Spatial weights matrix for EvapoTransp

Description

Spatial weights matrix for the EvapoTransp data frame

Usage

data(etw)

Format

A 86x86 matrix with elements different from zero if area i and j are neighbours. Weights are row standardized.

Author(s)

Giovanni Millo

Spatial weights matrix - 49 US states

Description

Spatial weights matrix of the 48 continental US States plus District of Columbia based on the queen contiguity criterium.

Usage

data(usaw49)
data(usaw46)

Format

A matrix with elements different from zero if state i and j are neighbors. Weights are row standardized. According to the queen contiguity criterium, Arizona and Colorado are considered neighbours. Two versions are provided, one for 49 States, the other one for 46 States.

Author(s)

Giovanni Millo