DPTS {DPTM}

The Dynamic panel threshold model with multiple thresholds

Description

DPTS This is a dynamic panel threshold model with fixed effects, which allows multiple thresholds, time trend term or time fixed effects.

Usage

DPTS(
  y,
  y1 = NULL,
  x = NULL,
  q,
  cvs = NULL,
  time_trend = FALSE,
  time_fix_effects = FALSE,
  x1 = NULL,
  tt,
  nn,
  Th = 1,
  ms = 1000,
  burnin = 1000,
  types = "DREAMzs",
  ADs = FALSE,
  r0x = NULL,
  r1x = NULL,
  NoY = FALSE,
  restart = FALSE,
  Only_b = FALSE,
  w = NULL,
  var_u = NULL,
  delty0 = NULL,
  nCR = 3,
  autoburnin = TRUE,
  sro = 0.1,
  display = TRUE
)

Arguments

y

the dependent variable; vector type input.

y1

the lag dependent variable; vector type input; By default, y1 is NULL, and then y1 will be computed by y automatically.

x

the independent variable; matrix type input.

q

the threshold variable; vector type input.

cvs

the set of control variables; matrix type input;By default, cvs is NULL.

time_trend

the time trend; By default, it is FALSE.

time_fix_effects

the time fixed effects; By default, it is FALSE.

x1

the initial values of independent variable; matrix type input. By default, x1 is NULL, and thus x1 will be computed by x automatically.

tt

the length of time period.

nn

the number of individuals.

Th

the number of thresholds.

ms

the length of MCMC chains after burn-in.

burnin

the length of burn-in.

types

the type of MCMC used; More details see BayesianTools::runMCMC.

ADs

the options for MCMC; More details see BayesianTools::runMCMC.

r0x

the lower bound of thresholds; By default, r0x is NULL, and thus r0x will be computed by q automatically.

r1x

the upper bound of thresholds; By default, r0x is NULL, and thus r1x will be computed by q automatically.

NoY

the option of threshold effects on the lag dependent variable; By default, NoY is False, and thus there will be threshold effects on y1.

restart

the option of iterations; By default, restart is FALSE, if encounters iteration failure, please set restart as TRUE.

Only_b

the option of initial equation;By default, Only_b is FALSE, and if Only_b is TRUE, initial delta y will be a constant C.; Please see Hsiao (2002) and Ramírez-Rondán (2020) for more details.

w

the variance ratio; By default, is NULL; It must be greater than 1.

var_u

the option of variance of error term; By default, is NULL; It must be greater than 0; When meet relevant ERROR, please change the var_u.

delty0

the option of delta_y; By default, delty0 is NULL; Please do not change delty0.

nCR

parameter determining the number of cross-over proposals of DREAM MCMC. If nCR = 1 all parameters are updated jointly.

autoburnin

a logical flag indicating of the Gelman and Rubin's convergence diagnostic, whether variables in x should be transformed to improve the normality of the distribution. If set to TRUE, a log transform or logit transform, as appropriate, will be applied.

sro

the least ratio of sample in regimes.

display

the option of whether to print the messages of estimated results; By default, the display is TRUE.

Value

A list containing the following components:

ssemin

the negaive log-likelihood function value

Ths

a vector of multiple thresholds in order

Ths_IC

a matrix of confidence intervals of all thresholds

Coefs

parameter estimates containing t-values

MCMC_Convergence_Diagnostic

the Gelman and Rubin's convergence diagnostic results of MCMC sample

model

a list of results of DMPL

MCMC

an object of class mcmcSampler (if one chain is run) or mcmcSamplerList, more details see BayesianTools::runMCMC

Author(s)

Hujie Bai

References

Ramírez-Rondán, N. R. (2020). Maximum likelihood estimation of dynamic panel threshold models. Econometric Reviews, 39(3), 260-276.

Hsiao, C., Pesaran, M. H., & Tahmiscioglu, A. K. (2002). Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods. Journal of econometrics, 109(1), 107-150.

Examples

data("data", package = "DPTM")
y <- data$data_test$y
q <-data$data_test$q
x <- as.matrix(data$data_test$x)
z <- as.matrix(data$data_test$z)
tt <- data$data_test$tt
nn <- data$data_test$nn
m1 <- DPTS(y=y,q=q,x=x,cvs = z,tt=tt,nn=nn,Th=1,ms = 100,burnin = 100)
m1$Ths
m1$Ths_IC
m1$Coefs
m1$MCMC_Convergence_Diagnostic
plot(m1$MCMC)