approval

A dataset from: Cavari, Amnon. 2019. "Evaluating the President on Your Priorities: Issue Priorities, Policy Performance, and Presidential Approval, 1981–2016." Presidential Studies Quarterly 49(4): 798-826.

datasets

Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) 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. These tools apply to traditional time-series quantities of interest: especially instantaneous effects for any period and cumulative effects for any period (including the long-run effect). They also allow for a variety of shock histories to be applied to the independent variable (beyond just a one-time, one-unit increase) as well as the recovery of inferences in levels for shocks applies to (in)dependent variables in differences (what we call the Generalized Dynamic Response Function). These effects are also available for the general conditional dynamic model advocated by Warner, Vande Kamp, and Jordan (2026 <doi:10.1017/psrm.2026.10087>). We also provide the actual formulae for these effects.

Soren Jordan

tseffects

Dynamic Inferences from Time Series (with Interactions)

Garrett N. Vande Kamp 

Reshi Rajan 

approval function

A data frame with 140 rows and 14 variables:<dl>
 <dt>APPROVE</dt>
<dd>Presidential approval</dd> <dt>APPROVE_ECONOMY</dt>
<dd>Presidential approval: economy</dd> <dt>APPROVE_FOREIGN</dt>
<dd>Presidential approval: foreign affairs</dd> <dt>MIP_MACROECONOMICS</dt>
<dd>Salience (Most Important Problem): economy</dd> <dt>MIP_FOREIGN</dt>
<dd>Salience (Most Important Problem): foreign affairs</dd> <dt>PARTY_IN</dt>
<dd>Macropartisanship (in-party)</dd> <dt>PARTY_OUT</dt>
<dd>Macropartisanship (out-party)</dd> <dt>PRESIDENT</dt>
<dd>Numeric indicator for president</dd> <dt>DIVIDEDGOV</dt>
<dd>Dummy variable for divided government</dd> <dt>ELECTION</dt>
<dd>Dummy variable for election years</dd> <dt>HONEYMOON</dt>
<dd>Dummy variable for honeymoon period</dd> <dt>UMCSENT</dt>
<dd>Consumer sentiment</dd> <dt>UNRATE</dt>
<dd>Unemployment rate</dd> <dt>APPROVE_L1</dt>
<dd>Lagged presidential approval</dd>
</dl>

Format

Data on US Presidential Approval — approval

A data frame with 140 rows and 14 variables:<dl>
 <dt>APPROVE</dt>
<dd>Presidential approval</dd>

 <dt>APPROVE_ECONOMY</dt>
<dd>Presidential approval: economy</dd>

 <dt>APPROVE_FOREIGN</dt>
<dd>Presidential approval: foreign affairs</dd>

 <dt>MIP_MACROECONOMICS</dt>
<dd>Salience (Most Important Problem): economy</dd>

 <dt>MIP_FOREIGN</dt>
<dd>Salience (Most Important Problem): foreign affairs</dd>

 <dt>PARTY_IN</dt>
<dd>Macropartisanship (in-party)</dd>

 <dt>PARTY_OUT</dt>
<dd>Macropartisanship (out-party)</dd>

 <dt>PRESIDENT</dt>
<dd>Numeric indicator for president</dd>

 <dt>DIVIDEDGOV</dt>
<dd>Dummy variable for divided government</dd>

 <dt>ELECTION</dt>
<dd>Dummy variable for election years</dd>

 <dt>HONEYMOON</dt>
<dd>Dummy variable for honeymoon period</dd>

 <dt>UMCSENT</dt>
<dd>Consumer sentiment</dd>

 <dt>UNRATE</dt>
<dd>Unemployment rate</dd>

 <dt>APPROVE_L1</dt>
<dd>Lagged presidential approval</dd>


</dl>

approval: Data on US Presidential Approval

Description

Usage

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Format