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Two-Stage Least Squares (Statistical Associates Blue Book Series 40) by [Garson, G. David]

Two-Stage Least Squares (Statistical Associates Blue Book Series 40) Kindle Edition

by G. David Garson (Author)

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Two-stage least squares regression (2SLS) is a method of extending regression to cover models which violate ordinary least squares (OLS) regression's assumption that there is no correlated error between one or more predictor variables and the disturbance term of the dependent variable. Correlated error may arise for three major reasons, each of which 2SLS may address:
1.Non-recursive models, which are ones in which there is reciprocal causation (simultaneity bias).
2.Unobserved variables which are correlated with a predictor variable (specification bias).
3.The sample itself is biased on variables affecting the dependent variable (selection bias)
All three situations involve the effect of unmeasured effects not specified in the model. In each situation, 2SLS may be more appropriate than OLS regression if suitable instrumental variables can be identified.

Table of Contents

Two-Stage Least Squares Regression Analysis (2SLS)5
Overview5
Key Terms and Concepts5
The recursivity assumption.6
Endogenous vs exogenous variables.6
Disturbance terms7
Two stages7
Stage 17
Stage 29
Example10
Data10
The Model10
2SLS in Stata11
Stata syntax11
Default Stata output12
Comparing types of instrumental variable estimation14
Comparing 2SLS and OLS with the Hausman test15
Testing for weak instruments17
Testing for endogeneity19
Testing for overidentifying restrictions20
Additional Stata output20
Saving estimates in Stata21
2SLS in SPSS22
SPSS user interface22
Default SPSS output23
Diagnostic tests in SPSS25
Saving estimates in SPSS26
2SLS in SAS27
SAS syntax27
Estimation methods in SAS28
Default SAS output29
Testing for heteroskedasticity30
Diagnostic plots31
Testing for overidentifying restrictions32
Testing for weak instruments33
Assumptions34
Data level34
Uncorrelated exogenous variables34
Sample size34
Multivariate normality35
Normally distributed error35
Multivariate equivariance35
Linearity35
No complete nonrecursivity35
No under-identification35
Regression model assumptions35
Testing assumptions36
Frequently Asked Questions36
Will 2SLS parameters be much different from OLS coefficients for the same data?36
How do I create lagged variables for use in 2SLS?36
Could I do 2SLS manually?37
What computer software supports 2SLS?38
Why is ML estimation generally preferred to 2SLS in estimating path parameters?38
In SEM, is there any reason to use 2SLS instead of ML?38
How is 2SLS used to test for selection bias?39
How is the intercept interpreted in 2SLS?40
May one apply 2SLS to cointegrated time series?41
Bibliography42
Pagecount 45

Length: 53 pages

Enhanced Typesetting: Enabled

Page Flip: Enabled

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Product details

File Size: 2145 KB
Print Length: 53 pages
Publisher: Statistical Associates Publishers; 2013 edition (February 11, 2013)
Publication Date: February 11, 2013
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00BEWGCYG
Text-to-Speech: Enabled

X-Ray:

Not Enabled

Word Wise: Not Enabled
Lending: Enabled
Screen Reader: Supported

Enhanced Typesetting: Enabled

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