DPTS {DPTM} | R Documentation |

## 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)
```

*DPTM*version 1.3.8 Index]