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  "Title": "Dynamic Effects from Single-Equation Time Series Models (with\nInteractions)",
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  "Authors@R": "c(person(\"Soren\", \"Jordan\", comment = c(ORCID = \"0000-0003-4201-1085\"), email = \"sorenjordanpols@gmail.com\", role = c(\"aut\", \"cre\", \"cph\")), person(\"Garrett N. Vande Kamp\", email = \"garrettvandekamp@uga.edu\", role = c(\"aut\")), person(\"Reshi Rajan\", email = \"rrajan@tamu.edu\", role = c(\"aut\")))",
  "Maintainer": "Soren Jordan <sorenjordanpols@gmail.com>",
  "Description": "Autoregressive distributed lag (A[R]DL) models (and their\nreparameterized equivalent, the Generalized Error-Correction\nModel [GECM]) are the workhorse dynamic linear models in\nuncovering dynamic inferences. ADL models are simple to\nestimate; this is what makes them attractive. Once these models\nare estimated, what is less clear is how to uncover a rich set\nof dynamic inferences from these models. We provide tools for\nrecovering those inferences. These tools apply to traditional\ntime-series quantities of interest and are built from the\nImpulse Response Function and Step Response Function (sometimes\ndescribed as a pulse effect or a cumulative effect). They also\nallow for a variety of shock histories to be applied to the\nindependent variable (beyond just a one-time, one-unit\nincrease) as well as the recovery of inferences in levels for\nshocks applied to (in)dependent variables in differences\nthrough the Generalized Dynamic Response Function. These tools\nare also available for the general conditional dynamic model\nadvocated by Warner, Vande Kamp, and Jordan (2026\n<doi:10.1017/psrm.2026.10087>).",
  "URL": "https://sorenjordan.github.io/tseffects/,\nhttps://github.com/sorenjordan/tseffects",
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  "Date/Publication": "2026-10-19 17:34:54 UTC",
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    },
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      "title": "Data on US Presidential Approval",
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      "topics": [
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        "gecm.plot"
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        "Dynamic conditional effects: interact.adl.plot",
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