PanelTM (version 1.1)

cpoint: Computation of the regime switch time in a (two-way or three-way) panel threshold regression model.

Description

A measure for computing the time at which a regime changes, i.e. the change point.

Usage

cpoint(data., nameI=NA, nameT=NA, nameJ=NA, nameY=NA, thresholds)

Value

An object of S4 class "cpoint", which is a matrix reporting the change point per each statistical unit \(i\) and each \(j\).

Arguments

data.

A (two-way or three-way) panel dataset. It can be either a \(N\times T\) matrix of the dependent variable(s) where the row represents the statistical unit (\(i\)) and the column the time (\(t\)) or a data.frame containing columns nameI, nameT, nameJ, and nameY, which should all be specified.

nameI

If data. is a data.frame, the name of the (numerical) variable that identifies the statistical units. Otherwise, it is NULL.

nameT

If data. is a data.frame, the name of the (numerical) time variable. Otherwise, it is NULL.

nameJ

If data. is a data.frame, the name of the (numerical/categorical) variable that indicates the third dimension. Otherwise, it is NULL.

nameY

If data. is a data.frame, the name of the (numerical) dependent variable. Otherwise, it is NULL.

thresholds

The vector of estimated threshold value(s). If data. is a matrix, the object should contain one value \(\gamma\). If data. is a data.frame, the vector should contain one threshold value per each value of j, i.e. \(\gamma_j\).

Author

Francesca Marta Lilja Di Lascio <[e-mail]>

Selene Perazzini <[e-mail]>

Details

cpoint computes the change point on the dependent variable (\(Y\)) for each statistical unit i given the time series of \(Y\) and the estimated threshold value \(\gamma_{ij}\). If the observed value \(Y\) is greater (lower) than the estimated threshold parameter, \(t=4\) belongs to the upper (lower) regime, and the change point is identified as the time \(t\) after which the longest sequence of days in the lower (upper) regime is observed.

References

Di Lascio, F.M.L. and Perazzini, S. (202x) A three-way dynamic panel threshold regression model for change point detection in bioimpedance data. WP BEMPS <https://repec.unibz.it/bemps104.pdf>.

Di Lascio, F.M.L. and Perazzini, S. (2022) Change point detection in fruit bioimpedance using a three-way panel model. Book of short papers - SIS2022, p.1184-1189, Eds. A. Balzanella, M. Bini, C. Cavicchia, R. Verde. Pearson. ISBN: 978-88-9193-231-0.

See Also

See also banana, ptm2, and ptm3.

Examples

# Import data
data(banana)

## Example 1: Application to a three-way panel data

# Select data for all the three levels of the third way 
bioimp.df2 <- banana

# Estimation: three-way ptm on bioimpedance data with the lagged 
# dependent variable as transition variable
estimates2 <- ptm3(data.=bioimp.df2, nameI="i", nameT="t",  
                   nameJ="j", nameY="bioimpedance", nameTV=NULL, 
                   nameXexo="weight", nameIV=NULL, trimrate=0.4, 
                   ngrid=100, h0=1.5, Iweight=FALSE, 
                   test.lin=FALSE)

# Change point computation using a data frame (unique input choice)
CP3 <- cpoint(data.=bioimp.df2, nameI="i", nameT="t", nameJ="j", 
              nameY="bioimpedance", 
              thresholds=estimates2@"threshold"[,2])

## NOT RUN           
### Example 2: Application to a two-way panel data
#
## Select data for a specific level of the third way 
#bioimp.df <- banana[which(banana$j==1),]
#
## Prepare input matrix for ptm2
#bioimp.m <-  matrix(bioimp.df$bioimpedance, 
#                    ncol=length(unique(bioimp.df$t)), byrow=TRUE)
#
## Estimation: two-way ptm on bioimpedance data with the lagged 
## dependent variable as transition variable
#estimates <- ptm2(Y=bioimp.m, TV=NULL, Xendo=NULL, Xexo=NULL,  
#                  IV=NULL, trimrate=0.4, ngrid=100, h0=1.5, 
#                  Iweight=FALSE, test.lin=FALSE)
#
## Change point computation using a data frame as input
#CP1 <- cpoint(data.=bioimp.df, nameI="i", nameT="t", nameJ="j", 
#              nameY="bioimpedance", 
#              thresholds=estimates@"threshold"[[1]])
#    
## Change point computation using a data matrix as input
#CP2 <- cpoint(data.=bioimp.m, nameI=NA, nameT=NA, nameJ=NA, 
#              nameY=NA, thresholds=estimates@"threshold"[[1]])
##