# CEF - Conditional Expectations and Linear Regressions...

This preview shows pages 1–5. Sign up to view the full content.

Conditional Expectations and Linear Regressions Walter Sosa-Escudero Econ 507. Econometric Analysis. Spring 2009 March 31, 2009 Walter Sosa-Escudero Conditional Expectations and Linear Regressions

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
‘All models are wrong, but some are useful’ (George E. P. Box) Box, G. E. P. and Draper, N., 1987, Empirical Model-Building and Response Surfaces , Wiley, New York, p. 424. Walter Sosa-Escudero Conditional Expectations and Linear Regressions
Motivation Our last attempt with the linear model So far we have assumed we ‘know’ the model and its structure. The OLS (or the GMM) estimator consistently estimates the unknown parameters. What is OLS estimating if the underlying model is completeley unknown (possibly non-linear, endogenous, heteroskedastic, etc.) We will argue that the OLS estimator provides a good linear aproximation of the (possibly non-linear) conditional expectation. Note: this lecture is highly inspired by Angrist and Pischke (2009). Walter Sosa-Escudero Conditional Expectations and Linear Regressions

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
E ( y | x ) gives the expected value of y for given values of x . It provides a reasonable representation of how x alters y . If x is random, E ( y | x ) is a random function. LIE: E ( y ) = E [ E ( y | x )] . We need two more properties. Walter Sosa-Escudero
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 02/27/2012 for the course STATS 315A taught by Professor Tibshirani,r during the Spring '10 term at Stanford.

### Page1 / 17

CEF - Conditional Expectations and Linear Regressions...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online