[讨论]Stata: Resources For Learning Stata

1. Preliminaries ?Introduction to Lessons and Stata (ec968st1)
   
2. The shapes of hazard and survival functions (ec968st2)
   
3. Preparing survival time data for analysis and estimation (ec968st3)
   
4. Estimation of the (integrated) hazard and survivor functions: Kaplan-Meier product-limit and lifetable methods (ec968st4)
   
5. Estimation: (i) continuous time models ?parametric and Cox (ec968st5)
   
6. Estimation: (ii) discrete time models (ec968st6)
   
7. Unobserved heterogeneity ('frailty') (ec968st7)
   
8. Competing risks models (ec968st8)
   
9. Assorted other topics (ec968st9)

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IMPERFECT LABOR MARKETS

Academic Year 2005-2006

Syllabus

The Aggregate Matching Function (Lecture Notes 1)

Lect1.do (Stata do-file, 2kb)

Matching Function Data: UK (Stata do-file, 13kb)

Calibration of a MP Model with Exogenous JD (Lecture Notes 2)

MP with Endogenous Job Destruction (Lecture Notes 3)

Simulating the Effects of LM Institutions

MP with Quantity and Price Rigidities (Lecture Notes 4)

Some Facts about LM Rigidities

Wage Posting (Lecture Notes 5)

[PDF] Introduction to Stata

Chapter	Browse	Print
2. Linear Models for Continuous Data	html	pdf (332 KB)
3. Logit Models for Binary Data	html	pdf (261 KB)
4. Poisson Models for Count Data*	html	pdf (123 KB)
5. Log-Linear Models for Contingency Tables	html	pdf (146KB)
6. Multinomial Response Models	html	pdf (166 KB
7. Survival Models	html	pdf (214 KB)
A. Review of Likelihood Theory	html	pdf (114 KB)
B. Generalized Linear Model Theory	html	pdf (126 KB)

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Chapter	Browse	Print
1. Introduction	html	pdf (81 KB)
2. Linear Models for Continuous Data	html	pdf (313 KB)
3. Logit Models for Binary Data	html	pdf (153 KB)
4. Poisson Models for Count Data	html	pdf (30 KB)
5. Log-Linear Models for Contingency Tables	html	pdf (22 KB)
6. Multinomial Response Models	html	pdf (34 KB)
7. Survival Models	html	pdf (25 KB)

Stata tutorial in Russian

Applied econometric analysis with Stata 6 (in Russian) is a 110-page introduction into econometric uses of regression with Stata 6 written in Russian. The initial purpose of this book was to serve as the lecture notes on the author's weekly seminars on Stata. The organization of the package and the main data handling commands are given. The basic econometric methods, techniques and tests are discussed, and their Stata counterparts are mentioned.

http://www.komkon.org/~tacik/Stata6Ec.pdf

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http://www3.grips.ac.jp/~yamanota/All%20You%20Need%20to%20Know%20about%20STATA.pdf

Lecture Notes:Basic Statistics

Viewing Stat 7 Documents

How to Read and Save Data

• Descriptive Statistics

• Probability

• Data Collection

• One and Two-Sample Methods

• Regression and ANOVA

Biostatistics I: Lecture Notes

Overview, Descriptive Statistics: Sep. 10

OSA example: Sep. 10

Probability: Sep. 17

Probability Distributions: Sep. 19

Discrete Probability Distributions

Continuous Probability Distributions

Birthweight Example: Sep. 24

Estimation & Random Sampling, Sept.24

Large Sample Interval Estimation, Sept. 26

Large Sample Significance Tests", Oct. 1

Small Sample Inference for Means, Oct. 8

T-test Examples - Oct. 8

Checking Assumptions - Oct. 10

Unequal Variance Example - Oct. 10

Exact Inference for Proportions- Oct. 15

More on 2 x 2 tables, Oct. 17

Comparing paired binomial samples, Oct. 22

Relationships between continuous variables, Oct. 22

Making generalizations - least squares, Oct. 24

Applying regression results, Oct. 31

Comparing More Than 2 Means, Nov. 5

Comparing More Than 2 Means: Example, Nov. 5

Cross-classified Samples: Comparing Means, Nov. 7

Means in Cross-classified Samples: Examples, Nov. 7

Factorial Experiments, Nov. 14

Factorial Experiments: Example, Nov. 14

Comparing Proportions in Three or More Samples, Nov. 14

Power and Sample Size, Nov. 19

Multiple 2 x 2 Tables, Nov. 21

Confounding Example, Nov. 21

Time to Event Data, Nov. 26

Survival Data Example, Nov. 26

Measuring Agreement, Nov. 26

Measuring Agreement Examples, Nov. 26

Analysing Rates, Nov. 28

Rates, Non-parametric tests in Stata, Nov. 28

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Quantitative Methods

Autumn term 2004

Course proprietor: Mike Barrow

• Introduction
• The general linear model: estimation and properties of estimates
• The general linear model: inference
• Maximum likelihood estimation and testing
• Tests of multiple and non-linear hypotheses
• Further aspects of the GLM: Multicollinearity, omitted variables, stability and prediction tests, dummy variables
• Heteroscedasticity
• Autocorrelation
• Stochastic regressors and IV estimation
• Partial adjustment and adaptive expectations model
• Time series modelling - testing for unit roots

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