DPTS {DPTM} | R Documentation |
The dynamic panel multiple threshold model with fixed effects
Description
DPTS This is the dynamic panel multiple 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 Z-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)