minAlgoStd-class

Class for standard algorithm to minimize multivariate functions. By
standard, we mean algorithms with the main function and its gradient
input separately. Specifically, it follows <code><a href="/link/optim?package=momentfit&version=1.0" data-mini-rdoc="momentfit::optim">optim</a></code>, not
<code><a href="/link/nlm?package=momentfit&version=1.0" data-mini-rdoc="momentfit::nlm">nlm</a></code>.

classes

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

minAlgoStd-class function

Objects can be created by calls of the form <code>new("minAlgoStd",
 ...)</code>. It is generated my <code>algoObj</code>.

Objects from the Class

<dl>
 <dt><code>algo</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd> <dt><code>start</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd> <dt><code>fct</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd> <dt><code>grad</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd> <dt><code>solution</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd> <dt><code>value</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd> <dt><code>message</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd> <dt><code>convergence</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd> 
</dl>

Slots

Class <code>"minAlgo"</code>, directly.

Extends

<dl>
 <dt>minFit</dt>
<dd><code>signature(object = "minAlgoStd")</code>: ...</dd> 
</dl>

Methods

Class for standard algorithm to minimize multivariate functions. By
standard, we mean algorithms with the main function and its gradient
input separately. Specifically, it follows <code><a href='https://rdrr.io/r/stats/optim.html'>optim</a></code>, not
<code><a href='https://rdrr.io/r/stats/nlm.html'>nlm</a></code>.

Class "minAlgoStd" — minAlgoStd-class

<dl>
 <dt><code>algo</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd>

 <dt><code>start</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd>

 <dt><code>fct</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd>

 <dt><code>grad</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd>

 <dt><code>solution</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd>

 <dt><code>value</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd>

 <dt><code>message</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd>

 <dt><code>convergence</code>:</dt>
<dd>Object of class <code>"character"</code> ~~</dd>

 
</dl>

<dl>
 <dt>minFit</dt>
<dd><code>signature(object = "minAlgoStd")</code>: ...</dd>

 
</dl>

minAlgoStd-class: Class `"minAlgoStd"`

Description

Arguments

Objects from the Class

Slots

Extends

Methods

Examples