lse-methods

lse,linearModel-method

It estimates models defined in the package by least squares. At the
moment, it only applies to <code>linearModel</code> objects and it is
estimated using <code><a href="/link/lm?package=momentfit&version=1.0" data-mini-rdoc="momentfit::lm">lm</a></code>.

methods

Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and systems of equation, and the Generalized Empirical Likelihood (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>). Some work is being done to add tools to deal with weak and/or many instruments. This includes K-Class estimators (Limited Information Maximum Likelihood and Fuller), Anderson and Rubin statistic test, etc.

Pierre Chausse

momentfit

Methods of Moments

lse-methods function

<dl>
<dt><code>signature(model = "linearModel")</code></dt>
<dd>It ignores the instruments and simply fits the linear model with LSE.</dd>
</dl>

Methods

It estimates models defined in the package by least squares. At the
moment, it only applies to <code>linearModel</code> objects and it is
estimated using <code><a href='https://rdrr.io/r/stats/lm.html'>lm</a></code>.

Least Squares Methods for Moment Models — lse-methods

<dl>
<dt><code>signature(model = "linearModel")</code></dt>
<dd>It ignores the instruments and simply fits the linear model with LSE.</dd>


</dl>

lse-methods: Least Squares Methods for Moment Models

Description

Arguments

Methods

Examples