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tseffects  

Dynamic (Causal) Inferences from Time Series (with Interactions)
View on CRAN: Click here


Download and install tseffects package within the R console
Install from CRAN:
install.packages("tseffects")

Install from Github:
library("remotes")
install_github("cran/tseffects")

Install by package version:
library("remotes")
install_version("tseffects", "0.1.4")



Attach the package and use:
library("tseffects")
Maintained by
Soren Jordan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-10-09
Latest Update: 2025-10-09
Description:
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.
How to cite:
Soren Jordan (2025). tseffects: Dynamic (Causal) Inferences from Time Series (with Interactions). R package version 0.1.4, https://cran.r-project.org/web/packages/tseffects. Accessed 15 Mar. 2026.
Previous versions and publish date:
0.1.4 (2025-10-09 14:10)
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Complete documentation for tseffects
Functions, R codes and Examples using the tseffects R package
Full tseffects package functions and examples
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