cigDemand

Balanced panel dataset on annual cigarette consumption in the
US for the 48 continental states in the years 1985 until 1995.
The dataset is available from Stock2003;textualpdynmc
and used in SW2019;textualpdynmc and
FriPuaSch2022;textualpdynmc.
Gratitude is owed to Jonathan Gruber of MIT for
providing the data.

datasets

Linear dynamic panel data modeling based on linear and
nonlinear moment conditions as proposed by
Holtz-Eakin, Newey, and Rosen (1988) <doi:10.2307/1913103>,
Ahn and Schmidt (1995) <doi:10.1016/0304-4076(94)01641-C>,
and Arellano and Bover (1995) <doi:10.1016/0304-4076(94)01642-D>.
Estimation of the model parameters relies on the Generalized
Method of Moments (GMM) and instrumental variables (IV) estimation,
numerical optimization (when nonlinear moment conditions are
employed) and the computation of closed form solutions (when
estimation is based on linear moment conditions). One-step,
two-step and iterated estimation is available. For inference
and specification
testing, Windmeijer (2005) <doi:10.1016/j.jeconom.2004.02.005>
and doubly corrected standard errors
(Hwang, Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>)
are available. Additionally, serial correlation tests, tests for
overidentification, and Wald tests are provided. Functions for
visualizing panel data structures and modeling results obtained
from GMM estimation are also available. The plot methods include
functions to plot unbalanced panel structure, coefficient ranges
and coefficient paths across GMM iterations (the latter is
implemented according to the plot shown in
Hansen and Lee, 2021 <doi:10.3982/ECTA16274>).
For a more detailed description of the GMM-based functionality,
please see Fritsch, Pua, Schnurbus (2021) <doi:10.32614/RJ-2021-035>.
For more details on the IV-based estimation routines,
see Fritsch, Pua, and Schnurbus (WP, 2024) and
Han and Phillips (2010) <doi:10.1017/S026646660909063X>.

Markus Fritsch

pdynmc

Moment Condition Based Estimation of Linear Dynamic Panel Data
Models

Joachim Schnurbus

Andrew Adrian Yu Pua

cigDemand function

A dataset with 528 rows and 9 variables containing:<dl>
<dt>state</dt>
<dd>state</dd><dt>year</dt>
<dd>year</dd><dt>cpi</dt>
<dd>consumer price index (US)</dd><dt>pop</dt>
<dd>state population</dd><dt>packpc</dt>
<dd>number of cigarette packs sold per capita and year</dd><dt>income</dt>
<dd>state personal income (total, nominal)</dd><dt>tax</dt>
<dd>average federal, state, and local excise taxes on cigarettes for fiscal year in cents per pack</dd><dt>avgprs</dt>
<dd>average price during fiscal year in cents per pack (including sales taxes)</dd><dt>taxs</dt>
<dd>average excise tax for fiscal year in cents per pack (including sales taxes)</dd>
</dl>

Format

Cigarette consumption in the US — cigDemand

A dataset with 528 rows and 9 variables containing:<dl>
<dt>state</dt>
<dd>state</dd>

<dt>year</dt>
<dd>year</dd>

<dt>cpi</dt>
<dd>consumer price index (US)</dd>

<dt>pop</dt>
<dd>state population</dd>

<dt>packpc</dt>
<dd>number of cigarette packs sold per capita and year</dd>

<dt>income</dt>
<dd>state personal income (total, nominal)</dd>

<dt>tax</dt>
<dd>average federal, state, and local excise taxes on cigarettes for fiscal year in cents per pack</dd>

<dt>avgprs</dt>
<dd>average price during fiscal year in cents per pack (including sales taxes)</dd>

<dt>taxs</dt>
<dd>average excise tax for fiscal year in cents per pack (including sales taxes)</dd>


</dl>

cigDemand: Cigarette consumption in the US

Description

Usage

Arguments

Format

References