data_sim

It contains a dataset simulated from the dynamic logit model

datasets

panel data

Estimation, based on conditional maximum likelihood, of the quadratic exponential
model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531>
and of a simplified and a modified version of this model. The quadratic exponential model
is suitable for the analysis of binary longitudinal data when state dependence (further
to the effect of the covariates and a time-fixed individual intercept) has to be taken
into account. Therefore, this is an alternative to the dynamic logit model having the
advantage of easily allowing conditional inference in order to eliminate the individual
intercepts and then getting consistent estimates of the parameters of main interest
(for the covariates and the lagged response). The simplified version of this model
does not distinguish, as the original model does, between the last time occasion
and the previous occasions. The modified version formulates in a different way the
interaction terms and it may be used to test in a easy way state dependence as shown
in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>.
The package also includes estimation of the dynamic logit model by a pseudo conditional
estimator based on the quadratic exponential model, as proposed by
Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>.
For large time dimensions of the panel, the computation of the proposed models involves a
recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal
Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C.
(2021, Computational Economics <DOI:10.1007/s10614-021-10218-2>.

Francesco Bartolucci

cquad

Conditional Maximum Likelihood for Quadratic Exponential Models
for Binary Panel Data

data_sim function

The observations are for 1000 sample units at 5 five time occasions:<dl>
 <dt><code>id</code></dt>
<dd>list of the reference unit of each observation</dd> <dt><code>time</code></dt>
<dd>number of the time occasion</dd> <dt><code>X1</code></dt>
<dd>first covariate</dd> <dt><code>X2</code></dt>
<dd>second covariate</dd> <dt><code>y</code></dt>
<dd>response</dd> 
</dl>

Format

Simulated dataset — data_sim

The observations are for 1000 sample units at 5 five time occasions:<dl>
 <dt><code>id</code></dt>
<dd>list of the reference unit of each observation</dd>

 <dt><code>time</code></dt>
<dd>number of the time occasion</dd>

 <dt><code>X1</code></dt>
<dd>first covariate</dd>

 <dt><code>X2</code></dt>
<dd>second covariate</dd>

 <dt><code>y</code></dt>
<dd>response</dd>

 
</dl>

data_sim: Simulated dataset

Description

Usage

Arguments

Format

Examples