PanelTM (version 1.1)

perfm: Performance measures of the (two-way or three-way) panel threshold regression model estimated on simulated data.

Description

Computes the relative bias, the bias, the root mean squared error, and the relative root mean squared error.

Usage

perfm(truepar,estpar)

Value

An object of S4 class "perfm", which is a list with the following values: the true value of the parameters, the relative bias, the bias, the root relative mean squared error, the relative root relative means squared error.

Arguments

truepar

True value of the parameter.

estpar

Vector of the estimated parameters (one value per each simulated dataset).

Author

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

Selene Perazzini <[e-mail]>

Details

perfm computes the following performance measures: the relative bias, the bias, the root mean squared error, and the relative root mean squared error.

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. Submitted.

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 ptm2, ptm3 and simptm.

Examples

# Simulation of the two-way panel threshold model 
# y_{it} = (-0.7-0.5*x_{it})1(y_{it-1}<=0) + (1.8+0.8*x_{it})1(y_{it-1}>0)
# Two datasets with 20 individuals observed over 15 times with change  
# point at time 12 and an autoregressive error term (with parameter 0.7 
# and unit error variance).

sims <- simptm(n=20, T.=15, J=1, CP=12, gamma=c(0),  
               phi_c=matrix(c(-0.7,1.8), nrow=1, byrow=TRUE), 
               phi_X=matrix(c(-0.5,0.8), nrow=1, byrow=TRUE), sigmau=1, 
               parAR=0.2, B=2, seedstart=1)

# Estimate the two simulated two-way panel threshold models

estimates1 <- ptm3(sims@simulation[[1]], nameI="i", nameT="t", nameJ="j", 
                   nameY="Y", nameXexo="X1", nameXendo=NULL, nameTV=NULL, 
                   nameIV=NULL, trimrate=0.4, ngrid=100, h0=1.5, 
                   Iweight=FALSE, test.lin=FALSE)

estimates2 <- ptm3(sims@simulation[[2]], nameI="i", nameT="t", nameJ="j", 
                   nameY="Y", nameXexo="X1", nameXendo=NULL, nameTV=NULL, 
                   nameIV=NULL, trimrate=0.4, ngrid=100, h0=1.5,
                   Iweight=FALSE, test.lin=FALSE)

# Compute performance measures for the parameter beta of the exogenous 
# regressor (for details see refs)
pm1 <- perfm(truepar=-0.5, estpar=c(estimates1@param[,2],
             estimates2@param[,2]))
pm1

# Compute performance measures for the delta corresponding to the
# difference between the constants of the model (for details see refs)
pm2 <- perfm(truepar=c(1.8+0.7), estpar=c(estimates1@param[,4], 
             estimates2@param[,4]))
pm2

# Compute performance measures for the delta corresponding to the
# difference between the regressors coefficients of the model 
# (for details see refs)
pm3 <- perfm(truepar=c(0.8+0.5), estpar=c(estimates1@param[,6],
             estimates2@param[,6]))
pm3