simnetwork

<code>simnetwork</code> generates adjacency matrices based on specified probabilities.

Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan, 2024; <doi:10.2139/ssrn.3721250>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).

Aristide Houndetoungan

CDatanet

Econometrics of Network Data

simnetwork function

<dl><dt>dnetwork</dt>
<dd>A list of sub-network matrices, where the (i, j)-th position of the m-th matrix represents the probability that individual <code>i</code> is connected to individual <code>j</code> in the m-th network.</dd>
<dt>normalise</dt>
<dd>A boolean indicating whether the returned matrices should be row-normalized (<code>TRUE</code>) or not (<code>FALSE</code>).</dd></dl>

Arguments

Simulating Network Data — simnetwork

<dl>

<dt>dnetwork</dt>
<dd>A list of sub-network matrices, where the (i, j)-th position of the m-th matrix represents the probability that individual <code>i</code> is connected to individual <code>j</code> in the m-th network.</dd>


<dt>normalise</dt>
<dd>A boolean indicating whether the returned matrices should be row-normalized (<code>TRUE</code>) or not (<code>FALSE</code>).</dd>

</dl>

simnetwork: Simulating Network Data

Description

Usage

Value

Arguments

Examples