Package 'pdR' reference manual

Title:	Threshold Model and Unit Root Tests in Cross-Section and Time Series Data
Description:	Threshold model, panel version of Hylleberg et al. (1990) <DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.
Authors:	Ho Tsung-wu [aut, cre]
Maintainer:	Ho Tsung-wu <[email protected]>
License:	GPL (>= 2)
Version:	1.9.4
Built:	2026-07-15 07:13:28 UTC
Source:	https://github.com/cran/pdR

Panel Data Regression: Threshold Model and Unit Root Tests

Description

Functions for analysis of panel data, including the panel threshold model of Hansen (1999,JE), panel unit root test of Chang(2002,JE) based upon instuments generating functions (IGF), and panel seasonal unit root test based upon Hylleberg et al.(1990,JE).

Details

This version offers formatted output. This package designs a specification function ptm() to estimate the panel threshold model of Hansen(1999). The key feature of ptm() is to generalize Hansen's original code to allow multiple (more-than-one) regime-dependent right-hand-side independent variables; Dr. Hansen's original code admits only 1 regime-dependent right-hand-side independent variable. This version also includes panel unit root tests based on the instrument generating functions(IGF), proposed by Chang (2002, J. of Econometrics), and the panel version of Hylleberg et al.(1990) seasonal unit root test, proposed by Otero, et al. (2005, 2007).

Author(s)

Ho Tsung-wu <[email protected]>, College of Management, National Taiwan Normal University.
Maintainer: Ho Tsung-wu <[email protected]>

References

Chang, Y. (2002) Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency. Journal of Econometrics, 110, 261-292.
Hansen B. E. (1999) Threshold effects in non-dynamic panels: Estimation, testing and inference. Journal of Econometrics,93, 345-368.
Hylleberg, S., Engle, R.F., Granger, C.W.J., and Yoo, B.S.(1990) Seasonal integration and cointegration. Journal of Econometrics,44, 215-238.
Otero, J., Smith, J., and Giulietti, M. (2005) Testing for seasonal unit roots in heterogeneous panels. Economics Letters, 86, 229-235.
Otero, J., Smith, J., and Giulietti, M. (2007) Testing for seasonal unit roots in heterogeneous panels in the presence of cross section dependence. Economics Letters, 86, 179-184.
Pesaran M. Hashem (2007) A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22, 265-312.

Panel data of bank,2001Q1~2010Q1

Description

A quarterly panel data frame with 1000 observations on the following 7 variables, unbalanced panel data ranges from 2001Q1~2010Q1.

Usage

data("bank_income")data("bank_income")

Format

ID: a numeric vector
Qtr: a numeric vector
preTax_Income: a numeric vector
shortRatio: a numeric vector
longRatio: a numeric vector
Current_ratio: a numeric vector
LoanDeposit_ratio: a numeric vector

Examples

data(bank_income)
data(bank_income)

Cigaretts consumption of US states

Description

Cigaretts consumption of US states

Usage

data(cigaretts)
data(cigaretts)

Format

A data frame of 48 US states' cigaretts consumption

State: State abbreviation, N
Year: Year, t
Y_SALES: Cigarette sales in packs per capita, deflated by population
X1_PRICE: P=Real price per pack of cigarettes, deflated by 1983 CPI.
X2_PIMIN: Real minimum price in adjoining states per pack of cigarettes, deflated by CPI
X3_NDI: Per capita disposable income

References

Baltagi Badi H. (2005) Econometric Analysis of Panel Data. John Wiley.

Examples

data(cigaretts)
head(cigaretts)
data(cigaretts)
head(cigaretts)

Annual crime dataset of US counties

Description

Annual crime dataset of US counties

Usage

data(crime)
data(crime)

Format

A data frame of US counties

county: counties index, N
year: Year, t
crmrte: crime rate(crime/population)
prbarr: probability of arrest (arrests/offenses)
prbconv: probability of conviction, given arrest
prbpris: probability of a prison, given conviction
avgsen: sanction severity(average prison sentence in days )
polpc: ability of police force to detect crime(# of police per capita)
density: population density(POP/area)
taxpc: Taxpayment per capita
region: region index of county
smsa: =1 if SAMA, POP>50000; =0 else
pctmin: See Baltagi(2006) for details
wcon: See Baltagi(2006) for details
wtuc: See Baltagi(2006) for details
wtrd: See Baltagi(2006) for details
wfir: See Baltagi(2006) for details
wser: See Baltagi(2006) for details
wmfg: See Baltagi(2006) for details
wfed: See Baltagi(2006) for details
wsta: See Baltagi(2006) for details
wloc: See Baltagi(2006) for details
mix: See Baltagi(2006) for details
pctymle: See Baltagi(2006) for details

References

Baltagi Badi H. (2005) Econometric Analysis of Panel Data. John Wiley. Baltagi Badi H. (2006) Estimating an Economic Model of Crime Using Panel Data from North Carolina. J.of Applied Econometrics 21: 543¡V547.

The cross-country growth data in Durlauf and Johnson(1995)

Description

The Durlauf-Johnson data manupipulated by Hansen(2000),excluding missing variables and oil states

Usage

data(dur_john)
data(dur_john)

Format

A data frame with 19 countries

gdpGrowth: Economic growth measured by GDP of 1960 and 1985
logGDP60: log Per capita GDP in 1960
Inv_GDP: Average ratio of investment (including Government Investment) to GDP from 1960 to 1985
popGrowth: Average growth rate of working-age population 1960 to 1985
School: Average fraction of working-age population enrolled in secondary school from 1960 to 1985
GDP60: Per capita GDP in 1960
Literacy: fraction of the population over 15 years old that is able to read and write in 1960

Details

Steven N. Durlauf and Paul A. Johnson, "Multiple Regimes and Cross-Country Growth Behavior," Journal of Applied Econometrics, Vol. 10, No. 4, 1995, 365-384.

Examples

data(dur_john)
head(dur_john)
data(dur_john)
head(dur_john)

Specification test for panel glm models

Description

This function performs Hausman specification test for panel glm.

Usage

htest_pglm(RE, FE, re.method)
htest_pglm(RE, FE, re.method)

Arguments

RE

Random effect objects. Support pglm, glmer, glmmTMB

FE

Fixed effect objects.

re.method

Method that used to estimate the random effect estimation, in addition to "pglm", it also supports "glmmTMB" of package glmmTMB, and "glmer" of package lme4.

Author(s)

Ho Tsung-wu <[email protected]>, modifed from phtest() of plm

References

Hausman J.A. (1978). Specification Tests in Econometrics. Econometrica, 46, 1251-1271.

Examples

data(ship)
library(pglm)
Eq1="accident  ~  op_75_79+co_65_69+co_70_74+co_75_79"
FE.pois <- pglm(Eq1,data=ship,family = "poisson",model = "within",index = 'ship',R=10)

RE.pois <- pglm::pglm(Eq1,data=ship,family = "poisson", model = "random", index = 'ship')

## Hausman test
htest_pglm(RE=RE.pois,  FE=FE.pois, re.method="pglm")

Eq2=accident  ~  op_75_79+co_65_69+co_70_74+co_75_79 + (1 | ship)
re.glmmTMB=glmmTMB::glmmTMB(Eq2,data=ship, family="poisson")

## Hausman test
htest_pglm(RE=re.glmmTMB,  FE=FE.pois, re.method="glmmTMB")


data(ship)
library(pglm)
Eq1="accident  ~  op_75_79+co_65_69+co_70_74+co_75_79"
FE.pois <- pglm(Eq1,data=ship,family = "poisson",model = "within",index = 'ship',R=10)

RE.pois <- pglm::pglm(Eq1,data=ship,family = "poisson", model = "random", index = 'ship')

## Hausman test
htest_pglm(RE=RE.pois,  FE=FE.pois, re.method="pglm")

Eq2=accident  ~  op_75_79+co_65_69+co_70_74+co_75_79 + (1 | ship)
re.glmmTMB=glmmTMB::glmmTMB(Eq2,data=ship, family="poisson")

## Hausman test
htest_pglm(RE=re.glmmTMB,  FE=FE.pois, re.method="glmmTMB")

Unit root test based on Change(2002)

Description

This function estimates the unit root regression based on instrument generating function of Change(2002) and returns useful outputs.

Usage

IGF(y, maxp, ic, spec)
IGF(y, maxp, ic, spec)

Arguments

y

A univariate time series data

maxp

the max number of lags

ic

Information criteria, either "AIC" or "BIC"

spec

regression model specification.
=0, no intercept and trend.
=1, intercept only.
=2, intercept and trend.

Details

Estimate univariate unit root test of Chang(2002).

Value

tstat.IGF

IGF unit root test

beta

regression coefficients. The first one is the AR(1) coefficient of unit root, and the last one is the intercept or trend

sdev

The IGF standard error for unit root coefficient

cV

The scalar C in IGF equation

p

The optimal number of lag

Author(s)

Ho Tsung-wu <[email protected]>, College of Management, National Taiwan Normal University.

References

Chang, Y. (2002) Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency. Journal of Econometrics, 110, 261-292.

Examples

data(inf19)
y <- inf19[,1]
IGF(y,maxp=35,ic="BIC",spec=2)$tstat.IGF

data(inf19)
y <- inf19[,1]
IGF(y,maxp=35,ic="BIC",spec=2)$tstat.IGF

Monthly inflation time series of 20 countries

Description

Monthly inflation time series of 19 countries, 1971.1~2011.12

Usage

data(inf_M)
data(inf_M)

Format

A data frame with 20 countries

AUSTRALIA: inflation of Austrlia
AUSTRIA: inflation of Austria
BELGIUM: inflation of Belgium
CANADA: inflation of Canada
DENMARK: inflation of Denmark
FINLAND: inflation of Finland
FRANCE: inflation of France
GREECE: inflation of Greece
ICELAND: inflation of Iceland
ITALY: inflation of Italy
JAPAN: inflation of Japan
LUXEMBOURG: inflation of Luxembourg
NETHERLANDS: inflation of Netherlands
NORWAY: inflation of Norway
PORTUGAL: inflation of Portugal
SPAIN: inflation of Spain
SWEDEN: inflation of Sweden
SWITZERLAND: inflation of Switzerland
UK: inflation of UK
USA: inflation of USA

Details

Monthly CIP, seasonaly differenced of log CPI of 20 countries

Examples

data(inf_M)
head(inf_M)
data(inf_M)
head(inf_M)

Quarterly inflation time series of 20 countries

Description

Quarterly inflation time series of 19 countries, 1971Q1~2014Q4

Usage

data(inf_Q)
data(inf_Q)

Format

A data frame with 19 countries

AUSTRLIA: inflation of Austrlia
AUSTRIA: inflation of Austria
BELGIUM: inflation of Belgium
CANADA: inflation of Canada
DENMARK: inflation of Denmark
FINLAND: inflation of Finland
FRANCE: inflation of France
GREECE: inflation of Greece
ICELAND: inflation of Iceland
ITALY: inflation of Italy
JAPAN: inflation of Japan
LUXEMBOURG: inflation of Luxembourg
NETHERLANDS: inflation of Netherlands
NORWAY: inflation of Norway
PORTUGAL: inflation of Portugal
SPAIN: inflation of Spain
SWEDEN: inflation of Sweden
SWITZERLAND: inflation of Switzerland
UK: inflation of UK
USA: inflation of USA

Details

Quarterly CIP, seasonaly differenced of log CPI of 20 countries

Examples

data(inf_Q)
head(inf_Q)
data(inf_Q)
head(inf_Q)

Monthly inflation time series of 19 countries

Description

Monthly inflation time series of 19 countries, 1984.1~2011.3

Usage

data(inf19)
data(inf19)

Format

A data frame with 19 countries

AUSTRIA: inflation of Austria
BELGIUM: inflation of Belgium
CANADA: inflation of Canada
DENMARK: inflation of Denmark
FINLAND: inflation of Finland
FRANCE: inflation of France
GREECE: inflation of Greece
ICELAND: inflation of Iceland
ITALY: inflation of Italy
JAPAN: inflation of Japan
LUXEMBOURG: inflation of Luxembourg
NETHERLANDS: inflation of Netherlands
NORWAY: inflation of Norway
PORTUGAL: inflation of Portugal
SPAIN: inflation of Spain
SWEDEN: inflation of Sweden
SWITZERLAND: inflation of Switzerland
UK: inflation of UK
USA: inflation of USA

Details

Monthly CIP, seasonaly differenced of log CPI of 19 countries

Examples

data(inf19)
head(inf19)
data(inf19)
head(inf19)

investment data of 565 listed companies, 1973-1987

Description

investment data of 565 listed companies, 1973-1987, from Hansen's example

Usage

data(invest)
data(invest)

Format

A pooled data frame

invest[,1]: investment/assets
invest[,2]: Tobin's Q
invest[,3]: cash-flow/assets
invest[,4]: debt/assets

Details

This is a pooled data frame, without date (T) and cross-section(N) ID columes

Examples

#data(invest)
#head(invest)
#data(invest)
#head(invest)

IPS-HEGY seasonal unit root test in panel data, Otero et al.(2007).

Description

This function performs panel data-based HEGY seasonal unit root test, the asymptotics is based upon Otero et al.(2007).

Usage

ipsHEGY(data,deterministic =c(1,0,0),lag.method="AIC",maxlag=12,CIPS = TRUE)

ipsHEGY(data,deterministic =c(1,0,0),lag.method="AIC",maxlag=12,CIPS = TRUE)

Arguments

data

Panel data, T by N

deterministic

Options for deterministic components.
c(1,0,0), intercept only.
c(0,1,0), trend only.
c(0,0,1),, season dummy only.

lag.method

Selection of optimal lags, having for options: "fixed","AIC", "BIC", "AICc".

maxlag

Maximum number of lags for searching optimal criteria.

CIPS

Logical. If TRUE, using Pesaran(2007) to account for cross-section correlation. The default is TRUE.

Details

Mode for selectlags has four options, AIC,AICc and BIC use R built-in functions for linear model and their meanings are popular and straightforward. "fixed" fixes maxlag as the number of lags.
The critical values for panel HEGY are standard normal for individual t-ratios, however,you need to perform simulation for the critical values of F joint test, at pdR 1.3. To this end, you are encouraged to work this out for yourself: using arima.sim() to sample seasonal time series with unit root (1-order difference) and obtain their statistics under the null using ipsHEGY(), then it is streightforward to obtain critical values.
Otero et al. (2007) provide critical values for quarterly frequency.
The univariate HEGY is based on R package uroot, simply modified to include cross-secionaal average.

Value

P_HEGY

Panel HEGY statistics.

U_HEGY

Idividual HEGY statistics of N units.

Author(s)

Ho Tsung-wu <[email protected]>, College of Management, National Taiwan Normal University.

References

Otero, J., Smith, J., and Giulietti, M. (2005) Testing for seasonal unit roots in heterogeneous panels. Economics Letters, 86, 229-235.
Otero, J., Smith, J., and Giulietti, M. (2007) Testing for seasonal unit roots in heterogeneous panels in the presence of cross section dependence. Economics Letters, 86, 179-184.
Pesaran M. Hashem (2007) A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22, 265-312.

Examples

data(inf_M)

#Seasonal dummy only takes quarters 1:3, 
#becasue of the presence of common intercept.

OUT<-ipsHEGY(inf_M, deterministic = c(1,1,1), lag.method = "AIC", maxlag = 12,CIPS=TRUE)
OUT$P_HEGY
OUT$U_HEGY


# Simulation of critical values



data(inf_M)

#Seasonal dummy only takes quarters 1:3, 
#becasue of the presence of common intercept.

OUT<-ipsHEGY(inf_M, deterministic = c(1,1,1), lag.method = "AIC", maxlag = 12,CIPS=TRUE)
OUT$P_HEGY
OUT$U_HEGY


# Simulation of critical values

Select the optimal number of lags, given criteria

Description

Determine the optimal number of lags for dynamic regression

Usage

lagSelect(y, maxp, ic)
lagSelect(y, maxp, ic)

Arguments

y

A univariate time series data

maxp

the max number of lags

ic

Information criteria, either "AIC" or "BIC"

Details

Information criteria "AIC" and "BIC" use the R built-in functions.

Value

It returns an integer, indicating the optimal lags

Author(s)

Ho Tsung-wu <[email protected]>, College of Management, National Taiwan Normal University.

Examples

#library(pdR)
#data(inf19)
#y<-inf19[,1]
#lagSelect(y,maxp=25,ic="BIC")
#library(pdR)
#data(inf19)
#y<-inf19[,1]
#lagSelect(y,maxp=25,ic="BIC")

Estimate specified panel threshold model

Description

This function is the main function estimating threshold regression for function ptm()

Usage

model(r, trim, rep, it, qq1, cf, xt, ct, thresh, tt, qn1, n, qn, cc, yt, ty, k)
model(r, trim, rep, it, qq1, cf, xt, ct, thresh, tt, qn1, n, qn, cc, yt, ty, k)

Arguments

r

vector of threshold estimate(s).

trim

value of trimmed percentage.

rep

number bootstrap repetition.

it

number of regime during computation, used in a for loop.

qq1

defined parameter.

cf

special declaration, e.g. lag().

xt

regime independent variables.

ct

trace of regime dependent variables.

thresh

threshold variable.

tt

length of time period.

qn1

as defined by nrow(qq1).

n

number of cross-section units.

qn

number of quantiles to examine.

cc

as defined by 2*log(1-sqrt(conf_lev)).

yt

vectorized dependent variable.

ty

trace of yt.

k

number of regime-independent independent variables.

Note

Original code offered by Dr. B. E.Hansen (http://www.ssc.wisc.edu/~bhansen/).

References

Hanse B. E. (1999) Threshold effects in non-dynamic panels: Estimation, testing and inference. Journal of Econometrics,93, 345-368.

Panel unit root test of Chang(2002)

Description

Compute the panel unit root test statistic of Chang(2002).

Usage

pIGF(datamat, maxp, ic, spec)
pIGF(datamat, maxp, ic, spec)

Arguments

datamat

T by N panel data.T is the time length,N is the number of cross-section units.

maxp

the max number of lags

ic

Information criteria, either "AIC" or "BIC".

spec

model specification.
=0, no intercept and trend.
=1, intercept only.
=2, intercept and trend.

Details

This function estimates the panel unit root test based on univariate instrument generating function of (Chang,2002).

Value

panel.tstat

panel IGF test statistics

pvalue

P-value of the panel.tstat

Author(s)

Ho Tsung-wu <[email protected]>, College of Management, National Taiwan Normal University.

References

Chang,Y.(2002) Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency. Journal of Econometrics, 110, 261-292.

Examples

data(inf19)
datam <- inf19
pIGF(datam,maxp=25,ic="BIC",spec=2)
data(inf19)
datam <- inf19
pIGF(datam,maxp=25,ic="BIC",spec=2)

Productivity data of 48 US state,1970-1986

Description

Gross state production data

Usage

data(productivity)
data(productivity)

Format

A data frame with US production

state: US state index, 1-48
year: Year index
y_gsp: Gross state product
x1_hwy: Expediture of public utility- highway construction
x2_water: Expediture of public utility- water
x3_other: Expediture of others
x4_private: Private consumption of each state
x5_emp: Employment rate of each state
x6_unemp: Unemployment rate of each state

Examples

data(productivity)
head(productivity)
data(productivity)
head(productivity)

Threshold specification of panel data

Description

A generalized specification for estimating panel threshold model.

Usage

ptm(dep, ind1, ind2, d, bootn, trimn, qn, conf_lev, t, n)
ptm(dep, ind1, ind2, d, bootn, trimn, qn, conf_lev, t, n)

Arguments

dep

Dependent variable

ind1

Independent variables: regime dependent

ind2

Independent variables:regime independent

d

Threshold variable

bootn

Vector of bootstrap repetition

trimn

Vector of trimmed percentage

qn

Number of quantiles to examine

conf_lev

Confidence level

t

Length of time period

n

Number of cross-section units

Details

This code fits only balanced panel data. It generalizes the simple code of Dr. Hansen (http://www.ssc.wisc.edu/~bhansen/), allowing multiple (more-than-one) regime-dependent (ind1) variables. We generalize the original code to better fit general need of threshold modeling in panel data.
bootn and trimn are vector of 3 by 1, indicating numbers of three corresponding regimes.
This version corrects a slight error incurred by argument max_lag, which is used by Hansen to arrange investment data via lags. In this package, users manipulate data to fit personal research to ptm(), hence this argument is omitted lest should degree of freedom will suffer a loss of N.

Author(s)

Ho Tsung-wu <[email protected]>, College of Management, National Taiwan Normal University.

References

Hansen B. E. (1999) Threshold effects in non-dynamic panels: Estimation, testing and inference. Journal of Econometrics,93, 345-368.

Examples

# library(pdR)
#data(invest)
#dat<-invest[1:1500,]    # subsetting the first 1500 obs., #for simplicity
#t <- 15            #Length of time period
#nt <- nrow(dat)
#n <- nt/t           # number of cross-section units

#dep<- as.matrix(dat[,1])       # investment/assets
#th1<- as.matrix(dat[,2])  #Tobin's Q
#th2<- as.matrix(dat[,3])  # cash-flow/assets
#ind1<- cbind(th1,th2)  #regime-dep covariates 
#d <- as.matrix(dat[,4])      # Threshold variable      
#ind2 <- cbind((th1^2),(th1^3),(th1*d)) # regime-indep covariates:
#bootn<-c(100,200,300)  # bootstrapping replications for each  threshold esitmation
#trimn<-c(0.05,0.05,0.05)   #trimmed percentage for each threshold  esitmation

#qn<-400
#conf_lev<-0.95

#Output=ptm(dep,ind1,ind2,d,bootn,trimn,qn,conf_lev,t,n)
#Output[[1]] #Formatted output of 1st threshold, 2 regimes
#Output[[2]] #Formatted output of 2nd threshold, 3 regimes
#Output[[3]] #Formatted output of 3rd threshold, 4 regimes

# In the output, the Regime-dependent Coefficients matrix
# is, from top to bottom, regime-wise.
# library(pdR)
#data(invest)
#dat<-invest[1:1500,]    # subsetting the first 1500 obs., #for simplicity
#t <- 15            #Length of time period
#nt <- nrow(dat)
#n <- nt/t           # number of cross-section units

#dep<- as.matrix(dat[,1])       # investment/assets
#th1<- as.matrix(dat[,2])  #Tobin's Q
#th2<- as.matrix(dat[,3])  # cash-flow/assets
#ind1<- cbind(th1,th2)  #regime-dep covariates 
#d <- as.matrix(dat[,4])      # Threshold variable      
#ind2 <- cbind((th1^2),(th1^3),(th1*d)) # regime-indep covariates:
#bootn<-c(100,200,300)  # bootstrapping replications for each  threshold esitmation
#trimn<-c(0.05,0.05,0.05)   #trimmed percentage for each threshold  esitmation

#qn<-400
#conf_lev<-0.95

#Output=ptm(dep,ind1,ind2,d,bootn,trimn,qn,conf_lev,t,n)
#Output[[1]] #Formatted output of 1st threshold, 2 regimes
#Output[[2]] #Formatted output of 2nd threshold, 3 regimes
#Output[[3]] #Formatted output of 3rd threshold, 4 regimes

# In the output, the Regime-dependent Coefficients matrix
# is, from top to bottom, regime-wise.

A subroutine for model()

Description

This function is a subroutine for model(), estimation procedure.

Usage

r_est(y, r, trim, tt, qq1, qn1, qn, n, cf, xt, ct, thresh)
r_est(y, r, trim, tt, qq1, qn1, qn, n, cf, xt, ct, thresh)

Arguments

y

vector of dependent variable.

r

numer of regime.

trim

value of trimmed percentage.

tt

length of time period.

qq1

parameter defined by as.matrix(unique(thresh)[floor(sq*nrow(as.matrix(sort(unique(thresh)))))]).

qn1

as defined by nrow(qq1).

qn

number of quantiles to examine.

n

parameter of cross-section units.

cf

special declaration, e.g. lag().

xt

regime independent variables.

ct

trace of regime dependent variables.

thresh

threshold variable.

References

Hanse B. E. (1999) Threshold effects in non-dynamic panels: Estimation, testing and inference. Journal of Econometrics,93, 345-368.

Original code from Dr. Hansen (http://www.ssc.wisc.edu/~bhansen/).

Panel data on the number of ship accidents

Description

Panel data on the number of ship accidents, McCullagh and Nelder(1989)

Usage

data("ship")data("ship")

Format

accident: the number of ship accidents
ship: Ship iD
service: the number of months in service
op_75_79: the operating period between 1975 and 1979
co_65_69: consecutive construction periods of 5 years
co_70_74: consecutive construction periods of 5 years
co_75_79: consecutive construction periods of 5 years
yr_con: years of construction
yr_op: years of operation

References

McCullagh, P., and J. A. Nelder (1989) Generalized Linear Models. 2nd ed. London: Chapman and Hall/CRC.

Examples

data(ship)
data(ship)

Estimation of sub-sampled data

Description

A function for estimating the subsampled data.

Usage

SMPLSplit_est(data,dep,indep,th,plot,h=1,nonpar=2)
SMPLSplit_est(data,dep,indep,th,plot,h=1,nonpar=2)

Arguments

data

the data in either data.frame or matrix.

dep

the name of dependent variable.

indep

the name(s) of independent variable(s).

th

the name of threshold variable.

plot

=1, plot; =0, do not plot.

h

h=1, heteroskedasticity-consistent covariance; h=0, homoskedastic case.

nonpar

Indicator for non-parametric method used to estimate nuisance scale in the presence of heteroskedasticity (only relevant if h=1).Set nonpar=1 to estimate regressions using a quadratic.Set nonpar=2 to estimate regressions using an Epanechnikov kernel with automatic bandwidth.

Details

This code estimates the parameters of sub-sampled data. It generalizes the simple code of Dr. Hansen, allowing White Corrected Heteroskedastic Errors.

Value

threshold

values of threshold estimates.

est0

coefficient estimates of global data.

est.low

coefficient estimates of low regime.

est.high

coefficient estimates of high regime.

est0.info

additional information of global data.

est.joint.info

additional information of joint threshods.

est.low.info

additional information of est.low.

est.high.info

additional information of est.high.

Note

Original code offered by Dr. B. E.Hansen (http://www.ssc.wisc.edu/~bhansen/).

References

Hanse B. E. (2000) Sample Splitting and Threshold Estimation. Econometrica, 68, 575-603.

Examples

## Not run, becasue of bootstrap replicaiton takes time. Users may unmark # and run. 
data("dur_john")
rep <- 500
trim_per <- 0.15
dep <- "gdpGrowth"
indep <- colnames(dur_john)[c(2,3,4,5)]

SMPLSplit_est(data=dur_john,dep,indep,th="GDP60",plot=0,h=1,nonpar=2)


## Not run, becasue of bootstrap replicaiton takes time. Users may unmark # and run. 
data("dur_john")
rep <- 500
trim_per <- 0.15
dep <- "gdpGrowth"
indep <- colnames(dur_john)[c(2,3,4,5)]

SMPLSplit_est(data=dur_john,dep,indep,th="GDP60",plot=0,h=1,nonpar=2)

Example code for sample splitting

Description

A sample code for learning sample splitting.

Usage

SMPLSplit_example(data,dep,indep,th1,th2,trim_per,rep,plot)
SMPLSplit_example(data,dep,indep,th1,th2,trim_per,rep,plot)

Arguments

data

the data in either data.frame or matrix.

dep

the name of dependent variable.

indep

the name(s) of independent variable(s)

th1

the first threshold variable.

th2

the second threshold variable.

trim_per

trimmed percentage.

rep

nNumber of bootstrap repetitions.

plot

=1, plot; =0, do not plot.

Details

This code is the learning example for learning Hansen's econometric sample splitting. I detailed the description of each threshold stage.

Note

Original code offered by Dr. B. E.Hansen (http://www.ssc.wisc.edu/~bhansen/).

References

Hanse B. E. (2000) Sample Splitting and Threshold Estimation. Econometrica, 68, 575-603.

Examples

## Not run, becasue of bootstrap replicaiton takes time. Users may unmark # and run. 
data("dur_john")
#rep <- 500
#trim_per <- 0.15
#dep <- "gdpGrowth"
#indep <- colnames(dur_john)[c(2,3,4,5)]
#th1 <- "GDP60"
#th2 <- "Literacy"
#OUT=SMPLSplit_est(data=dur_john,dep,indep,th=th1,plot=0,h=1,nonpar=2)
#OUT$TEST
#OUT$Hypothesis
#OUT$Threshold
#stat=matrix(as.numeric(OUT$TEST),byrow = TRUE,8,2)
#colnames(stat)=c("F-Stat","P-value")
#rownames(stat)=OUT$Hypothesis
#stat

## Not run, becasue of bootstrap replicaiton takes time. Users may unmark # and run. 
data("dur_john")
#rep <- 500
#trim_per <- 0.15
#dep <- "gdpGrowth"
#indep <- colnames(dur_john)[c(2,3,4,5)]
#th1 <- "GDP60"
#th2 <- "Literacy"
#OUT=SMPLSplit_est(data=dur_john,dep,indep,th=th1,plot=0,h=1,nonpar=2)
#OUT$TEST
#OUT$Hypothesis
#OUT$Threshold
#stat=matrix(as.numeric(OUT$TEST),byrow = TRUE,8,2)
#colnames(stat)=c("F-Stat","P-value")
#rownames(stat)=OUT$Hypothesis
#stat

Testing for sample splitting

Description

A function for testing sample split given subsampled data.

Usage

SMPLSplit_het(data,dep,indep,th,trim_per,rep,plot)
SMPLSplit_het(data,dep,indep,th,trim_per,rep,plot)

Arguments

data

the data in either data.frame or matrix

dep

the name of dependent variable.

indep

the name(s) of independent variable(s).

th

the name of threshold variable.

trim_per

trimmed percentage.

rep

number of bootstrap repetition.

plot

=1, plot; =0, do not plot.

Details

This code tests for the presence of threshold. It generalizes the simple code of Dr. Hansen, allowing Heteroskedastic Errors (White Corrected).

Value

fstat

LM-test for no threshold.

pvalue

bootstrap P-Value.

Note

Original code offered by Dr. B. E.Hansen (http://www.ssc.wisc.edu/~bhansen/).

References

Hanse B. E. (2000) Sample Splitting and Threshold Estimation. Econometrica, 68, 575-603.

a subroutine of model()

Description

SSE calculation

Usage

sse_calc(y, x)
sse_calc(y, x)

Arguments

This function is a sub-routine for model(), calculating SSE of each regression

y

vector of dependent variable.

x

matrix of independent variables.

References

Hanse B. E. (1999) Threshold effects in non-dynamic panels: Estimation, testing and inference. Journal of Econometrics,93, 345-368.

Original code from Dr. Hansen (http://www.ssc.wisc.edu/~bhansen/).

Compute the resursive mean

Description

Compute the resursive mean of each series

Usage

 tbar(x)
tbar(x)

Arguments

x

A univariate time series data

Details

This function computes the resursive mean

Author(s)

Ho Tsung-wu <[email protected]>

Examples

data(inf19)
y <- inf19[,1]
tbar(y)

data(inf19)
y <- inf19[,1]
tbar(y)

a subroutine calculating SSE

Description

This function is a sub-routine for model(), calculating SSE of each threshold regression.

Usage

thr_sse(y, q, r, cf, xt, ct, thresh, tt, n)
thr_sse(y, q, r, cf, xt, ct, thresh, tt, n)

Arguments

y

parameter.

q

qq1 in model().

r

parameter.

cf

as defined in model().

xt

as defined in model().

ct

as defined in model().

thresh

as defined in model().

tt

as defined in model().

n

as defined in model().

References

Hanse B. E. (1999) Threshold effects in non-dynamic panels: Estimation, testing and inference. Journal of Econometrics,93, 345-368.

Original code from Dr. Hansen (http://www.ssc.wisc.edu/~bhansen/).

A sub-routine calculating trace

Description

Estimation of trace.

Usage

 tr(y, tt, n)
tr(y, tt, n)

Arguments

This function is a sub-routine for model(), calculating trace of matrix

y

data vector.

tt

time period length.

n

number of cross-section units.

References

Hanse B. E. (1999) Threshold effects in non-dynamic panels: Estimation, testing and inference. Journal of Econometrics,93, 345-368.

Original code from Dr. Hansen (http://www.ssc.wisc.edu/~bhansen/).

Package 'pdR'

Help Index

Panel Data Regression: Threshold Model and Unit Root Tests

Description

Details

Author(s)

References

Panel data of bank,2001Q1~2010Q1

Description

Usage

Format

Examples

Cigaretts consumption of US states

Description

Usage

Format

References

Examples

Annual crime dataset of US counties

Description

Usage

Format

References

The cross-country growth data in Durlauf and Johnson(1995)

Description

Usage

Format

Details

Examples

Specification test for panel glm models

Description

Usage

Arguments

Author(s)

References

Examples

Unit root test based on Change(2002)

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Monthly inflation time series of 20 countries

Description

Usage

Format

Details

Examples

Quarterly inflation time series of 20 countries

Description

Usage

Format

Details

Examples

Monthly inflation time series of 19 countries

Description

Usage

Format

Details

Examples

investment data of 565 listed companies, 1973-1987

Description

Usage

Format

Details

Examples

IPS-HEGY seasonal unit root test in panel data, Otero et al.(2007).

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Select the optimal number of lags, given criteria

Description