Time Series Regression Models with Distributed Lag Models
Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<10.1371>) and Baltagi (2011)(<10.1007>) for more information.10.1007>10.1371>
Version 1.0.6 (14 September 2018)
- The show.summary argument has dropped from the functions dlm(), ardlDlm(), koyckDlm(), and polyDlm(). Now, a call to the classical summary() function summarises the model.
- All the forecast functions have collected under the forecast() method to improve usability of the package.
- To remove the model elements, matrices and vectors have been replaced by list interface. Please see the help files for details.
- Added a functionality to remove lag 0 of any independent series.
- Improved documentation and errors fixed.
- Reduced number of dependencies.
- Improved some functions.
Version 1.0.7 (26 September 2018)
- A bug in pre-model fitting checks fixed.
Version 1.0.8 (15 September 2018)
- A bug in the error messages of pre-model fitting checks for ARDL models fixed.
Version 1.0.9 (21 September 2018)
- A bug in dlm() function fixed.
- More examples added.
- Removed the limitation on the maximum number of lags can be fitted with finiteDLMauto() function.