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


Reference manual: dLagM.pdf


Package source: dLagM_1.1.13.tar.gz
Windows binaries: r-devel: dLagM_1.1.13.zip, r-release: dLagM_1.1.13.zip, r-oldrel: dLagM_1.1.13.zip
macOS binaries: r-release (arm64): dLagM_1.1.13.tgz, r-oldrel (arm64): dLagM_1.1.13.tgz, r-release (x86_64): dLagM_1.1.13.tgz, r-oldrel (x86_64): dLagM_1.1.13.tgz
Old sources: dLagM archive


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