dptm

Dynamic Panel Multiple Threshold Model with Fixed Effects Based on Maximum Likelihood Estiamtion and MCMC

https://github.com/hujiebai/dptm

Science Score: 26.0%

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Low similarity (10.5%) to scientific vocabulary

Keywords

dynamic panel-data r thresholds

Last synced: 6 months ago · JSON representation

Repository

Dynamic Panel Multiple Threshold Model with Fixed Effects Based on Maximum Likelihood Estiamtion and MCMC

Basic Info

Host: GitHub
Owner: HujieBai
License: gpl-3.0
Language: R
Default Branch: main
Homepage:
Size: 12.2 MB

Statistics

Stars: 2
Watchers: 1
Forks: 2
Open Issues: 0
Releases: 1

Topics

dynamic panel-data r thresholds

Created almost 2 years ago · Last pushed 11 months ago

Metadata Files

Readme Changelog License

README.md

DPTM

Dynamic Panel Multiple Threshold Model with Fixed Effects

Q1: Why do you need to use it?

The DPTM can supply the estimation and test for the dynamic panel threshold model with multiple thresholds while there is no software or tools that can do this.
The DPTM is based on maximum likelihood (ML) estimation, while most dynamic panel threshold models use IVs or GMM, which improves performance and is more convenient to use.
The DPTM is based on the Markov Chain Monte Carlo (MCMC) method, while most threshold models use a grid search method, which improves performance and avoids selecting the grid's step size.
The DPTM not only allows fixed effects but also allows the time fixed effects, which is more suitable for reality and application.
Various forms of threshold models are available。

Q2: How can you use it?

Please see the specific example in the Help Pages of functions in the DPTM.

Q3: How can you get it?

Use "devtools::install_github("HujieBai/DPTM")" for installation.
The R package available for local installation is "DPTM_x.x.x.tar.gz."

2025.3.22 (Version 3.0.2)

Rewrote the entire package in OOP (R6 class).
Detailed instructions and examples have been added to make it more user-friendly.
The formula interface of the R language is exploited, which is more standardized and easy to use.

Please use?DPTM::DPTS to see more useful details.

(I'm a PhD candidate at Lingnan College of Sun Yat-sen University, thank you very much for your recognition of my work!)

Owner

Name: Hujie Bai
Login: HujieBai
Kind: user

Repositories: 1
Profile: https://github.com/HujieBai

A R user.

GitHub Events

Total

Issues event: 3
Watch event: 1
Issue comment event: 1
Push event: 12
Fork event: 1

Last Year

Issues event: 3
Watch event: 1
Issue comment event: 1
Push event: 12
Fork event: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 2
Total pull requests: 0
Average time to close issues: 6 months
Average time to close pull requests: N/A
Total issue authors: 1
Total pull request authors: 0
Average comments per issue: 0.5
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 1
Pull requests: 0
Average time to close issues: about 14 hours
Average time to close pull requests: N/A
Issue authors: 1
Pull request authors: 0
Average comments per issue: 1.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

HujieBai (2)

Pull Request Authors

Top Labels

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Packages

Total packages: 1
Total downloads:
- cran 206 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 4
Total maintainers: 1

cran.r-project.org: DPTM

Dynamic Panel Multiple Threshold Model with Fixed Effects

Homepage: https://github.com/HujieBai/DPTM
Documentation: http://cran.r-project.org/web/packages/DPTM/DPTM.pdf
License: GPL (≥ 3)
Latest release: 3.0.2
published 11 months ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 206 Last month

Rankings

Dependent packages count: 28.0%

Dependent repos count: 36.0%

Average: 49.7%

Downloads: 85.1%

Maintainers (1)

hujiebai@163.com