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About ARE 213 Notes
This document discusses linear regression and the conditional expectation function (CEF). It makes three key points: 1) Linear regression estimates the CEF when the CEF is linear. Even when the CEF is nonlinear, regression provides the best linear approximation to the CEF. 2) Regression finds the best linear predictor of the dependent variable given the regressors. It minimizes the mean squared error when predicting within the range of the data. 3) Regression approximates the CEF without requiring assumptions about the data distribution, independence, or whether regressors are fixed or random. It provides a useful tool for estimating relationships in empirical work.
Detailed Information
| Author: | ['Kelly Zhang'] |
|---|---|
| Publication Year: | 2014 |
| Pages: | 227 |
| Language: | English |
| Format: | |
| Price: | FREE |
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