Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Bayesian NARDL: Nonlinear ARDL na Tathmini ya Bayesian× | Modeli wa Kawaida wa Autoregressive Distributed Lag (Panel NARDL)× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2014 (NARDL); Bayesian extension c. 2015–2020 | 2014–2018 |
| Mwanzilishi≠ | Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature | Shin, Yu & Greenwood-Nimmo (2014), extended to panel settings by subsequent authors |
| Aina≠ | Nonlinear cointegrating model with Bayesian inference | Nonlinear dynamic panel model |
| Chanzo asilia≠ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer. link ↗ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer. DOI ↗ |
| Majina mbadala | Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL | Panel Nonlinear ARDL, panel asymmetric ARDL, panel NARDL bounds test, nonlinear panel cointegration model |
| Zinazohusiana≠ | 6 | 4 |
| Muhtasari≠ | Bayesian NARDL combines the Nonlinear Autoregressive Distributed Lag framework of Shin, Yu, and Greenwood-Nimmo (2014) with Bayesian posterior inference. It models asymmetric long-run cointegration — allowing positive and negative shocks to a regressor to have different equilibrium effects — while incorporating prior knowledge and producing full posterior distributions over all parameters, including the asymmetry gap. | Panel NARDL extends the time-series NARDL framework of Shin, Yu and Greenwood-Nimmo (2014) to a panel data setting, allowing researchers to detect asymmetric long-run and short-run relationships between variables across multiple cross-sections simultaneously. By decomposing the regressor into positive and negative partial sums, the model tests whether increases and decreases in an explanatory variable have different effects on the outcome. |
| ScholarGateSeti ya data ↗ |
|
|