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About Generalized Least Squares - Lecture Notes
The document discusses methods for dealing with heteroskedasticity in regression models, including generalized least squares (GLS), weighted least squares (WLS), and feasible generalized least squares (FGLS). GLS estimates the model after pre-multiplying by a matrix P to make the errors homoskedastic. WLS weights the variables using assumed variances. FGLS first estimates the error variances from an OLS model, then uses those estimates to weight an WLS model. Examples demonstrate implementing WLS and FGLS in Stata to account for heteroskedasticity in a wage equation.
Detailed Information
| Author: | ['Mohar Sen'] |
|---|---|
| Publication Year: | 2013 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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