[下载]Introduction to Stata 8

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Introduction to Stata 8

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hanszhu

2005-4-12 07:43:00

A Visual Guide to Stata Graphics,: by Michael Mitchell

An Introduction to Survival Analysis Using Stata, revised edition,: by Mario Cleves, William Gould, and Roberto Gutierrez

Regression Models for Categorical Dependent Variables Using Stata, revised edition,: by J. Scott Long and Jeremy Freese

Maximum Likelihood Estimation with Stata, second edition,: by William Gould, Jeffrey Pitblado, and William Sribney

Generalized Linear Models and Extensions,: by James Hardin and Joseph Hilbe

Stata Longitudinal/Panel Data Reference Manual; Stata Multivariate Statistics Reference Manual; Stata Programming Reference Manual; Stata Survey Data Reference Manual; Stata Survival Analysis and Epidemiological Tables Reference Manual; Stata Time-Series Reference Manual

[此贴子已经被作者于2005-4-12 7:59:08编辑过]

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hanszhu

2005-4-12 08:00:00

UCLA Stat Computing Portal

http://statcomp.ats.ucla.edu/stata/

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hanszhu

2005-4-12 08:02:00

Resources based on Stata 7

Getting Started with Stata for MS Windows: A Brief Introduction, Robert Yaffee, New York University, USA
An introduction to Stata in PDF format.

e-Tutorial on Stata, UIUC, USA
Electronic notes and tutorials by the teaching assistant of an applied econometrics course focusing on the basics of Stata and R.

Introducing Stata: A Statistical Program For Socio-economic Analysis, Peter Gruhn, Agrifood Consulting International
Training material prepared to introduce researchers in Nepal to the analysis of socioeconomic data using Stata.

Stata Tutorial, University of Essex
Tutorial on basic procedures in Stata.

[此贴子已经被作者于2005-4-12 8:11:52编辑过]

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hanszhu

2005-4-12 08:03:00

Resources based on Stata 9

Resources to help you learn and use Stata, UCLA Academic Technology Services, USA: An extensive resource of Stata information, including FAQs, learning modules, a quick-reference guide, annotated output, textbook examples, and more. New users may want to visit the Stata Starter Kit section of the UCLA site. Don't miss the Stata Web Books and the movies.

SSC Archive, Boston College, USA: A complete archive of programs exchanged on the Stata listserver and other programs for Stata. All these programs can also be installed directly from within Stata by typing net from http://fmwww.bc.edu/RePEc/bocode/ at the Stata prompt or use the ssc command.

Site includes full search capabilities.

UCLA Stata Portal, UCLA Academic Technology Services, USA: A web site that links and searches across Stata sites around the world, and more. This is a place where developers of Stata resources collaborate to create an infrastructure to help their own Stata community while providing a service to Stata users all over the world.

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hanszhu

2005-4-12 08:04:00

Resources based on Stata 8

Resources to help you learn and use Stata, UCLA Academic Technology Services, USA: An extensive resource of Stata information, including FAQs, learning modules, a quick-reference guide, annotated output, textbook examples, and more. New users may want to visit the Stata Starter Kit section of the UCLA site. Don't miss the Stata Web Books and the movies.

South Africa Distance Learning Project on Stata, University of Michigan, USA

An extensive teaching web site dedicated to Stata. Some of the topics covered are

Introduction to surveys
Understanding distributions
Multiple-regression analysis
Graphing with Stata 8

Survival Analysis with Stata: Course EC968, Stephen Jenkins, Institute for Social and Economic Research, University of Essex, UK: Lessons, programs, do-files, and a PDF book about survival analysis in Stata.

Topics in Economic Analysis – Stata course, London School of Economics, UK: Class notes, tutorials, sample data, and do-files from an economics course at the London School of Economics. In particular, see the excellent Introduction to Stata.

An Introduction to Stata (pdf), IT Support at the LSE Research Laboratory, UK: An introduction to Stata and various commands.

Introduction to Stata 8, Svend Juul, Department of Epidemiology and Social Medicine, University of Aarhus, Denmark: A 72-page introduction describing the basics of Stata 8, including a guide to using the new Stata 8 graphics.

Graduate statistics lecture notes, Richard Williams, Sociology Department, University of Notre Dame: Extensive lecture notes covering applied statistical topics from probability distribution through logistic regression, with examples using Stata.

Applied Econometrics lecture notes, Carlos Lamarche, Econometrics Group, University of Illinois at Urbana-Champaign: Lecture notes covering introductory econometrics topics, including Box-Cox transformation, dynamic models, bootstrapping techniques, Granger causality, Monte Carlo simulation and nonlinear regression, and simultaneous equation models, with examples using Stata.

Stata Introduction, Princeton University, USA: An extensive introduction to Stata covering general information about Stata and for learning Stata.

Introduction to Stata 8 (pdf), Christopher F. Baum, Boston College, USA: A 67-page description of Stata, its key features and benefits and other useful information.

LAB1: Introduction to working with Stata 8 (pdf), Karolinska Institutet, Sweden: A brief introduction, including course notes and exercises, on handling data in Stata.

Introduction to Stata (pdf), Dr. Joachim Winter, Universität Mannheim, Germany: A brief overview of the Stata interface, command syntax, and capabilities.

An Introduction to Stata: Part I (pdf), Thomas Lumley, University of Washington, USA: An introduction to Stata, including basic commands and features, for a biostatistics course.

A Quick Guide to Stata 8 for Windows (pdf), Kurt Schmidheiny, Université de Lausanne, HEC, Switzerland: Course notes on the basics of Stata, including important features and functions.

Data management tutorial, Carolina Population Center, University of North Carolina at Chapel Hill, USA: Tutorial, examples, and question-and-answer sessions on managing data in Stata, with an emphasis on survey data.

UCLA Stata Portal, UCLA Academic Technology Services, USA: A web site that links and searches across Stata sites around the world, and more. This is a place where developers of Stata resources collaborate to create an infrastructure to help their own Stata community while providing a service to Stata users all over the world.

Stata tutorials for the American Economic Association Summer Program 2004, Duke University in partnership with North Carolina A&T State University: Class notes, tutorials, and do-files by Stas Kolenikov for the American Economic Association Summer Program at Duke University.

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hanszhu

2005-4-12 08:45:00

[下载]Ebook.Stata Press.Regression Models For Categorical Dependent Variables

12042.rar
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Ebook.Stata Press.Regression Models For Categorical Dependent Variables.pdf

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hanszhu

2005-4-12 09:07:00

Regression Models for Categorical Dependent Variables using Stata

Part I General Information

1 Introduction

1.1 What is this book about?

1.2 Which models are considered?

1.3 Who is this book for?

1.4 How is the book organized?

1.5 What software do you need?

1.5.1 Updating Stata 8

1.5.2 Installing SPost

Installing SPost using net search

Installing SPost using net install

1.5.3 What if commands do not work?

1.5.4 Uninstalling SPost

1.5.5 Additional files available on the web site

1.6 Where can I learn more about the models?

2 Introduction to Stata

2.1 The Stata interface

Changing the scrollback buffer size

Changing the display of variable names in the Variables window

2.2 Abbreviations

2.3 How to get help

2.3.1 Online help

2.3.2 Manuals

2.3.3 Other resources

2.4 The working directory

2.5 Stata file types

2.6 Saving output to log files

Options

2.6.1 Closing a log file

2.6.2 Viewing a log file

2.6.3 Converting from SMCL to plain text or PostScript

2.7 Using and saving datasets

2.7.1 Data in Stata format

2.7.2 Data in other formats

2.7.3 Entering data by hand

2.8 Size limitations on datasets

2.9 do-files

2.9.1 Adding comments

2.9.2 Long lines

2.9.3 Stopping a do-file while it is running

2.9.4 Creating do-files

Using Stata's do-file editor

Using other editors to create do-files

2.9.5 A recommended structure for do-files

2.10 Using Stata for serious data analysis

2.11 The syntax of Stata commands

2.11.1 Commands

2.11.2 Variable lists

2.11.3 if and in qualifiers

Examples of if qualifier

2.11.4 Options

2.12 Managing data

2.12.1 Looking at your data

2.12.2 Getting information about variables

2.12.3 Missing values

2.12.4 Selecting observations

2.12.5 Selecting variables

2.13 Creating new variables

2.13.1 generate command

2.13.2 replace command

2.13.3 recode command

2.13.4 Common transformations for RHS variables

Breaking a categorical variable into a set of binary variables

More examples of creating binary variables

Nonlinear transformations

Interaction terms

2.14 Labeling variables and values

2.14.1 Variable labels

2.14.2 Value labels

2.14.3 notes command

2.15 Global and local macros

2.16 Graphics

2.16.1 The graph command

2.16.2 Displaying previously drawn graphs

2.16.3 Printing graphs

2.16.4 Combining graphs

2.17 A brief tutorial

A batch version

3 Estimation, Testing, Fit, and Interpretation

3.1 Estimation

3.1.1 Stata's output for ML estimation

3.1.2 ML and sample size

3.1.3 Problems in obtaining Ml estimates

3.1.4 The syntax of estimation commands

Variable lists

Specifying the estimation sample

Options

3.1.5 Reading the output

Header

Estimates and standard errors

3.1.6 Reformatting output with estimates table

3.1.7 Reformatting output with outreg

3.1.8 Alternative output with listcoef

Options for types of coefficients

Other options

Standardized coefficients

Factor and percent change

3.1.9 Storing estimation results

3.2 Post-estimation analysis

3.3 Testing

3.3.1 Wald tests

The accumulate option

3.3.2 LR tests

Avoiding invalid LR tests

3.4 Measures of fit

Syntax of fitstat

Options

Models and measures

Example of fitstat

Methods and formulas for fitstat

3.5 Interpretation

3.5.1 Approaches to interpretation

3.5.2 Predictions using predict

3.5.3 Overview of prchange, prgen, prtab, and prvalue

Specifying the levels of variables

Options for controlling output

3.5.4 Syntax for prchange

Options

3.5.5 Syntax for prgen

Options

Variables generated

3.5.6 Syntax for prtab

Options

3.5.7 Syntax for prvalue

Options

3.5.8 Computing marginal effects using mfx compute

3.6 Next steps

Part II Models for Specific Kinds of Outcomes

4 Models for Binary Outcomes

4.1 The statistical model

4.1.1 A latent variable model

4.1.2 A nonlinear probability model

4.2 Estimation using logit and probit

Variable lists

Specifying the estimation sample

Weights

Options

Example

4.2.1 Observations predicted perfectly

4.3 Hypothesis testing with test and lrtest

4.3.1 Testing individual coefficients

One and two-tailed tests

Testing single coefficients using test

Testing single coefficients using lrtest

4.3.2 Testing multiple coefficients

Testing multiple coefficients using test

Testing multiple coefficients using lrtest

4.3.3 Comparing LR and Wald tests

4.4 Residuals and influence using predict

4.4.1 Residuals

Example

4.4.2 Influential cases

4.5 Scalar measures of fit using fitstat

Example

4.6 Interpretation using predicted values

4.6.1 Predicted probabilities with predict

4.6.2 Individual predicted probabilities with prvalue

4.6.3 Tables of predicted probabilities with prtab

4.6.4 Graphing predicted probabilities with prgen

4.6.5 Changes in predicted probabilities

Marginal change

Discrete change

4.7 Interpretation using odds ratios with listcoef

Multiplicative coefficients

Effect of the base probability

Percent change in the odds

4.8 Other commands for binary outcomes

5 Models for Ordinal Outcomes

5.1 The statistical model

5.1.1 A latent variable model

5.1.2 A nonlinear probability model

5.2 Estimation using ologit and oprobit

Variable lists

Specifying the estimation sample

Weights

Options

5.2.1 Example of attitudes toward working mothers

5.2.2 Predicting perfectly

5.3 Hypothesis testing with test and lrtest

5.3.1 Testing individual coefficients

5.3.2 Testing multiple coefficients

5.4 Scalar measures of fit using fitstat

5.5 Converting to a different parameterization

5.6 The parallel regression assumption

5.7 Residuals and outliers using predict

5.8 Interpretation

5.8.1 Marginal change in y

5.8.2 Predicted probabilities

5.8.3 Predicted probabilities with predict

5.8.4 Individual predicted probabilities with prvalue

5.8.5 Tables of predicted probabilities with prtab

5.8.6 Graphing predicted probabilities with prgen

5.8.7 Changes in predicted probabilities

Marginal change with prchange

Marginal change with mfx compute

Discrete change with prchange

Computing discrete change for a 10-year increase in age

Odds ratios using listcoef

5.8.8 Odds ratios using listcoef

5.9 Less-common models for ordinal outcomes

5.9.1 Generalized ordered logit model

5.9.2 The stereotype model

5.9.3 The continuation ratio model

6 Models for Nominal Outcomes

6.1 The multinomial logit model

6.1.1 Formal statement of the model

6.2 Estimation using mlogit

Variable lists

Specifying the estimation sample

Weights

Options

6.2.1 Example of occupational attainment

6.2.2 Using different base categories

6.2.3 Predicting perfectly

6.3 Hypothesis testing of coefficients

6.3.1 mlogtest for tests of the MNLM

Options

6.3.2 Testing the effects of the independent variables

A likelihood-ratio test

A Wald test

Testing multiple independent variables

6.3.3 Tests for combining dependent categories

A Wald test for combining outcomes

Using test [category]

An LR test for combining outcomes

Using constraint with lrtest

6.4 Independence of irrelevant alternatives

Hausman test of IIA

Small and Hsiao test of IIA

Conclusions regarding tests of IIA

6.5 Measures of fit

6.6 Interpretation

6.6.1 Predicted probabilities

6.6.2 Predicted probabilities with predict

Using predict to compare mlogit and ologit

6.6.3 Individual predicted probabilities with prvalue

6.6.4 Tables of predicted probabilities with prtab

6.6.5 Graphing predicted probabilities with prgen

Plotting probabilities for one outcome and two groups

Graphing probabilities for all outcomes for one group

6.6.6 Changes in predicted probabilities

Computing marginal and discrete change with prchange

Marginal change with mfx compute

6.6.7 Plotting discrete changes with prchange and mlogview

6.6.8 Odds ratios using listcoef and mlogview

Listing odds ratios with listcoef

Plotting odds ratios

6.6.9 Using mlogplot

6.6.10 Plotting estimates from matrices with mlogplot

Options for using matrices with mlogplot

Global macros and matrices used by mlogplot

Example

6.7 The conditional logit model

6.7.1 Data arrangement for conditional logit

6.7.2 Fitting the conditional logit model

Options

Example of the clogit model

6.7.3 Interpreting results from clogit

Using odds ratios

Using predicted probabilities

6.7.4 Fitting the multinomial logit model using clogit

Setting up the data

Creating interactions

Fitting the model

6.7.5 Using clogit to fit mixed models

7 Models for Count Outcomes

7.1 The Poisson distribution

7.1.1 Fitting the Poisson distribution with the poisson command

7.1.2 Computing predicted probabilities with prcounts

Syntax

Options

Variables generated

7.1.3 Comparing observed and predicted counts with prcounts

7.2 The Poisson regression model

7.2.1 Estimating the PRM with poisson

Variable lists

Specifying the estimation sample

Weights

Options

7.2.2 Example of fitting the PRM

7.2.3 Interpretation using the rate µ

Factor change in E(y|x)

Percent change in E(y|x)

Example of factor and percent change

Marginal change in E(y|x)

Example of marginal change using prchange

Example of marginal change using mfx compute

Discrete change in E(y|x)

Example of discrete change using prchange

7.2.4 Interpretation using predicted probabilities

Example of predicted probabilities using prvalue

Example of predicted probabilities using prgen

Example of predicted probabilities using prcounts

7.2.5 Exposure time

7.3 The negative binomial regression model

7.3.1 Fitting the NBRM with nbreg

7.3.2 Example of fitting the NBRM

Comparing the PRM and NBRM using estimates table

7.3.3 Testing for overdispersion

7.3.4 Interpretation using the rate µ

7.3.5 Interpretation using predicted probabilities

7.4 Zero-inflated count models

7.4.1 Estimation of zero-inflated models with zinb and zip

Variable lists

Options

7.4.2 Example of fitting the ZIP and ZINB models

7.4.3 Interpretation of coefficients

7.4.4 Interpretation of predicted probabilities

Predicted probabilities with prvalue

Predicted probabilities with prgen

7.5 Comparisons among count models

7.5.1 Comparing mean probabilities

7.5.2 Tests to compare count models

LR tests of a

Vuong test non-nested models

8 Additional Topics

8.1 Ordinal and nominal independent variables

8.1.1 Coding a categorical independent variable as a set of dummy variables

8.1.2 Estimation and interpretation with categorical independent variables

8.1.3 Tests with categorical independent variables

Testing the effect of membership in one category versus the reference category

Testing the effect of membership in two nonreference categories

Testing that a categorical independent variable has no effect

Testing whether treating an ordinal variable as interval loses information

8.1.4 Discrete change for categorical independent variables

Computing discrete change with prchange

Computing discrete change with prvalue

8.2 Interactions

8.2.1 Computing gender differences in predictions with interactions

8.2.2 Computing gender differences in discrete change with interactions

8.3 Nonlinear nonlinear models

8.3.1 Adding nonlinearities to linear predictors

8.3.2 Discrete change in nonlinear nonlinear models

8.4 Using praccum and forvalues to plot predictions

Options

8.4.1 Example using age and age-squared

8.4.2 Using forvalues with praccum

8.4.3 Using praccum for graphing a transformed variable

8.4.4 Using praccum to graph interactions

8.5 Extending SPost to other estimation commands

8.6 Using Stata more efficiently

8.6.1 profile.do

8.6.2 Changing screen fonts and window preferences

8.6.3 Using ado-files for changing directories

8.6.4 me.hlp file

8.6.5 Scrolling in the Results Window in Windows

8.7 Conclusions

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romae

2005-4-12 10:36:00

老大文件格式是什么怎么打不开谢谢！

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hanszhu

2005-4-12 11:06:00

You can unzip by Winrar. Maybe you did not pay the $50 and you got a uncomplete file, and it is also possible that I did not set the 出售帖 properly.

[此贴子已经被作者于2005-4-12 11:10:38编辑过]

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hanszhu

2005-4-12 11:22:00

Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences) by J. Scott Long

Comment from the Stata technical group
While regression models for categorical dependent variables are ubiquitous, a discussion of how to interpret these models has been sorely lacking. Regression Models for Categorical Dependent Variables Using Stata, Revised Edition, fills this void. This book discusses how to fit and interpret regression models for categorical data with Stata and includes some commands written by the authors. Hypothesis testing and goodness-of-fit statistics are also discussed.
The book begins with a lucid introduction to Stata and then provides a general treatment of estimation, testing, fit, and interpretation in this class of models. Binary, ordinal, nominal, and count outcomes are covered in detail in separate chapters. The final chapter discusses how to fit and interpret models with special characteristics, such as ordinal and nominal independent variables, interaction, and nonlinear terms. One appendix discusses the syntax of the author-written commands, and a second gives details of the datasets used by the authors in the book.
This book is filled with concrete examples. Because all the examples, datasets, and author-written commands are available from the authors at their web site, readers can easily replicate the examples using Stata. This book is ideal for students or applied researchers who want to know how to fit this type of model and understand its output.
The revised edition uses the new Stata graphics system throughout the book. In addition, the revised edition discusses multiple missing-value codes and contains updated output throughout the text.

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蓝色

2005-4-12 19:34:00

good .

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zhutongji

2006-3-11 10:50:00

谢谢

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liuzhi438518

2009-3-10 17:07:00

怎么全是乱码

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wangxy

2015-5-22 12:15:53

hanszhu 发表于 2005-4-12 11:22
Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques ...

看看是什几版。

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Resources based on Stata 7

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Resources based on Stata 9

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Resources based on Stata 8

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[下载]Ebook.Stata Press.Regression Models For Categorical Dependent Variables

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Comment from the Stata technical group

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