dLagM: 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)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.
| Version: |
1.1.13 |
| Depends: |
graphics, stats, nardl, dynlm, R (≥ 3.6.0) |
| Imports: |
AER, formula.tools, plyr , lmtest, strucchange, wavethresh, MASS, roll, sandwich |
| Published: |
2023-10-02 |
| DOI: |
10.32614/CRAN.package.dLagM |
| Author: |
Haydar Demirhan [aut, cre, cph] (<https://orcid.org/0000-0002-8565-4710>) |
| Maintainer: |
Haydar Demirhan <haydar.demirhan at rmit.edu.au> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Citation: |
dLagM citation info |
| Materials: |
README NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
dLagM results |
Documentation:
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