# EC220 - Introduction to econometrics (chapter 3)

## Presentation on theme: "EC220 - Introduction to econometrics (chapter 3)"— Presentation transcript:

EC220 - Introduction to econometrics (chapter 3)
Christopher Dougherty EC220 - Introduction to econometrics (chapter 3) Slideshow: properties of the multiple regression coefficients Original citation: Dougherty, C. (2012) EC220 - Introduction to econometrics (chapter 3). [Teaching Resource] © 2012 The Author This version available at: Available in LSE Learning Resources Online: May 2012 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This license allows the user to remix, tweak, and build upon the work even for commercial purposes, as long as the user credits the author and licenses their new creations under the identical terms.

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
A.1 The model is linear in parameters and correctly specified. A.2 There does not exist an exact linear relationship among the regressors in the sample. A.3 The disturbance term has zero expectation A.4 The disturbance term is homoscedastic A.5 The values of the disturbance term have independent distributions A.6 The disturbance term has a normal distribution Moving from the simple to the multiple regression model, we start by restating the regression model assumptions. 1

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
A.1 The model is linear in parameters and correctly specified. A.2 There does not exist an exact linear relationship among the regressors in the sample. A.3 The disturbance term has zero expectation A.4 The disturbance term is homoscedastic A.5 The values of the disturbance term have independent distributions A.6 The disturbance term has a normal distribution Only A.2 is different. Previously it stated that there must be some variation in the X variable. We will explain the difference in one of the following slideshows. 2

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
A.1 The model is linear in parameters and correctly specified. A.2 There does not exist an exact linear relationship among the regressors in the sample. A.3 The disturbance term has zero expectation A.4 The disturbance term is homoscedastic A.5 The values of the disturbance term have independent distributions A.6 The disturbance term has a normal distribution Provided that the regression model assumptions are valid, the OLS estimators in the multiple regression model are unbiased and efficient, as in the simple regression model. 3

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
We will not attempt to prove efficiency. We will however outline a proof of unbiasedness. 4

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
The first step, as always, is to substitute for Y from the true relationship. The Y ingredients of b2 are actually in the form of Yi minus its mean, so it is convenient to obtain an expression for this. 5

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
After substituting, and simplifying, we find that b2 can be decomposed into the true value b2 plus a weighted linear combination of the values of the disturbance term in the sample. 6

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
This is what we found in the simple regression model. The difference is that the expression for the weights, which depend on all the values of X2 and X3 in the sample, is considerably more complicated. 7

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
Having reached this point, proving unbiasedness is easy. Taking expectations, b2 is unaffected, being a constant. The expectation of a sum is equal to the sum of expectations. 8

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
The a* terms are nonstochastic since they depend only on the values of X2 and X3, and these are assumed to be nonstochastic. Hence the a* terms may be taken out of the expectations as factors. 9

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
By Assumption A.3, E(ui) = 0 for all i. Hence E(b2) is equal to b2 and so b2 is an unbiased estimator. Similarly b3 is an unbiased estimator of b3. 10

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
Finally we will show that b1 is an unbiased estimator of b1. This is quite simple, so you should attempt to do this yourself, before looking at the rest of this sequence. 11

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
First substitute for the sample mean of Y. 12

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
Now take expectations. The first three terms are nonstochastic, so they are unaffected by taking expectations. 13

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
The expected value of the mean of the disturbance term is zero since E(u) is zero in each observation. We have just shown that E(b2) is equal to b2 and that E(b3) is equal to b3. 14

PROPERTIES OF THE MULTIPLE REGRESSION COEFFICIENTS
Hence b1 is an unbiased estimator of b1. 15