[讨论]Stata: Resources For Learning Stata

Paul Allison: Fixed Effects Negative Binomial Regression Models

This paper demonstrates that the conditional negative binomial model for panel data, proposed by Hausman, Hall and Griliches ( 1984), is not a true fixed- effects method. This method — which has been implemented in both Stata and LIMDEP — does not, in fact, control for all stable covariates. Three alternative methods are explored. A negative multinomial model yields the same estimator as the conditional Poisson estimator and, hence, does not provide any additional leverage for dealing with overdispersion. On the other hand, a simulation study yields good results from applying an unconditional negative binomial regression estimator with dummy variables to represent the fixed effects. There is no evidence for any incidental parameters bias in the coefficients, and downward bias in the standard error estimates can be easily and effectively corrected using the deviance statistic. Finally, an approximate conditional method is found to perform at about the same level as the unconditional estimator.

51791.pdf (83.35 KB)

2006-5-8 04:25:00 上传

[讨论]Stata: Resources For Learning Stata

FACTORTEST: Stata module to perform tests for appropriateness of factor analysis

Joao Pedro Azevedo (

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j.p.azevedo@ncl.ac.uk) (University of Newcastle-upon-Tyne, UK)

51793.pdf (8.93 KB)

2006-5-8 06:37:00 上传

[此贴子已经被作者于2006-5-8 6:37:34编辑过]

What is New in Stata 9

http://www.timberlake.co.uk/stata/software/Newin9.html#Anchor-Multinomia-15805

[此贴子已经被作者于2006-5-8 6:42:20编辑过]

Factor Analysis in Stata

Factor analysis (and its relative, principal components analysis) are performed in Stata using the 4 basic commands (factor, greigen, rotate, score) listed below.

factor [varlist],[algorithm] [modifiers]

[varlist] Choose variables to analyze.


[algorithm] Choose type of analysis (algorithm):


pc - principal components


pcf - principal component factors


pf - principal factors (default)


ipf - iterated principal factors


ml - maximum likelihood

[modifiers] Choose appropriate modifiers:


factors (#) Select maximum # of common factors.


mineigen (#) Set eigenvalue minimum for common factor.


covariance Analyze covariance matrix (in pc only).

greigen Graph eigenvalues for "scree plot".

rotate [,option]

varimax Choose orthogonal rotation.


promax (#) Choose nonorthogonal (oblique) rotation. (# defines correlation)

score "newvarnames" [,option]

bartlett Use (unbiased) Bartlett method rather than default regression
method (smaller MSE).


norotate Score unrotated factors.

Sample analysis

STATA Commands:


factor x1 x2 x3 x4 x5 x6 x7 x8 x9, ipf


greigen


factor x1 x2 x3 x4 x5 x6 x7 x8 x9, ipf factor (3)


rotate, varimax


score "factor1" "factor2" "factor 3", bartlett

Notations:



The first command factor analyzes 9 variables (x1 through x9) using iterated principal factor analysis.


Command 2 graphs the eigenvalues by factor number.


Based on the results of the first two command, a decision is made to retain
three common factors with command 3.


The fourth command provides an orthogonally rotated (varimax) solution.


The last command produces factor scores for each of the three factors using Bartlett's algorithm.

[PPT] Introduction to Factor Analysis

[此贴子已经被作者于2006-5-8 6:52:27编辑过]

		Stata		SAS		SPSS		Mplus
Regression Models
Robust Regression		Stata
Models for Binary and Categorical Outcomes
Logit Regression		Stata		SAS		SPSS
Multinomial Logit Regression		Stata
Ordinal Logit Regression		Stata
Probit Regression		Stata
Censored and Truncated Regression
Tobit Regression		Stata
Truncated Regression		Stata
Interval Regression		Stata
Other
Latent Class Analysis								Mplus

Implementation of quasi-least squares using xtgee in Stata

Liang and Zeger's original formulation of generalized estimating equations (GEE) has been widely applied since its introduction in 1986 because it extends the application of generalized linear models to clustered data. In this presentation we discuss a method, quasi-least squares (QLS), that is in the framework of GEE and builds on this popular approach by allowing for consideration of correlation matrices that were previously difficult to apply. In particular, we describe how to QLS in a straight-forward fashion by making use of Stata's xtgee procedure. We then discuss some data analysis examples.

http://repec.org/nasug2004/Shults_Stata_2004.ppt

Stata 9 manuals

Stata Longitudinal/Panel Data Reference Manual Copyright 2005 ISBN 1-59718-001-7 Pages 349 Price $45.00

See a larger photo of the front cover

Overview of the Stata documentation

Table of contents

Introduction to longitudinal/panel data reference manual (pdf)

Introduction to xt commands (pdf)

Sample entries (pdf):

[此贴子已经被作者于2006-5-8 10:44:52编辑过]

XTIVREG2: Stata module to perform extended IV/2SLS, GMM and AC/HAC, LIML and k-class regression for panel data models

Mark E Schaffer

Abstract

xtivreg2 implements IV/GMM estimation of the fixed-effects and first-differences panel data models with possibly endogenous regressors. It is essentially a wrapper for ivreg2, which must be installed for xtivreg2 to run (version 2.1.15 or above of ivreg2 is required: ssc install ivreg2, replace). xtivreg2 supports all the estimation and reporting options of ivreg2; see help ivreg2 for full descriptions and examples. In particular, all the statistics available with ivreg2 (heteroskedastic, cluster- and autocorrelation-robust covariance matrix and standard errors, overidentification and orthogonality tests, first-stage and weak/underidentification statistics, etc.) are also supported by xtivreg2 and will be reported with any degrees-of-freedom adjustments required for a panel data estimation.

File URL: http://fmwww.bc.edu/repec/bocode/x/xtivreg2.ado

[此贴子已经被作者于2006-5-9 23:40:44编辑过]

这个帖子应该置顶！！

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