tseffects: Dynamic (Causal) Inferences from Time Series (with Interactions)
Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) (see De Boef and Keele 2008 <doi:10.1111/j.1540-5907.2007.00307.x>) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences in three forms: causal inferences from ADL models, traditional time series quantities of interest (short- and long-run effects), and dynamic conditional relationships.
| Version: |
0.1.4 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
mpoly, car, ggplot2, sandwich, stats, utils |
| Suggests: |
knitr, rmarkdown, vdiffr, testthat (≥ 3.0.0) |
| Published: |
2025-10-09 |
| DOI: |
10.32614/CRAN.package.tseffects |
| Author: |
Soren Jordan 
[aut, cre, cph],
Garrett N. Vande Kamp [aut],
Reshi Rajan [aut] |
| Maintainer: |
Soren Jordan <sorenjordanpols at gmail.com> |
| BugReports: |
https://github.com/sorenjordan/tseffects/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://sorenjordan.github.io/tseffects/,
https://github.com/sorenjordan/tseffects |
| NeedsCompilation: |
no |
| CRAN checks: |
tseffects results |
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