Loading in 5 sec....

3.4 The Components of the OLS Variances: MulticollinearityPowerPoint Presentation

3.4 The Components of the OLS Variances: Multicollinearity

Download Presentation

3.4 The Components of the OLS Variances: Multicollinearity

Loading in 2 Seconds...

- 77 Views
- Uploaded on
- Presentation posted in: General

3.4 The Components of the OLS Variances: Multicollinearity

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

We see in (3.51) that the variance of Bjhat depends on three factors: σ2, SSTj and Rj2:

- The error variance, σ2
Larger error variance = Larger OLS variance

-more “noise” in the equation makes it more difficult to accurately estimate partial effects of the variables

-one can reduce the error variance by adding (valid) variables to the equation

2) The Total Sample Variation in xj, SSTj

Larger xj variance – Smaller OLSj variance

-increasing sample size keeps increasing SSTj since

-This still assumes that we have a random sample

3) Linear relationships among x variables: Rj2

Larger correlation in x’s – Bigger OLSj variance

-Rj2 is the most difficult component to understand

- Rj2 differs from the typical R2 in that it measures the goodness of fit of:

-Where xj itself is not considered an explanatory variable

3) Linear relationships among x variables: Rj2

-In general, Rj2 is the total variation in xj that is explained by the other independent variables

-If Rj2=1, MLR.3 (and OLS) fails due to perfect multicollinearity (xj is a perfect linear combination of the other x’s)

Note that:

-High (but not perfect) correlation between independent variables is MULTICOLLINEARITY

-Note that an Rj2 close to 1 DOES NOT violate MLR. 3

-unfortunately, the “problem” of multicollinearity is hard to define

-No Rj2 is accepted as being too high

-A high Rj2 can always be offset by a high SSTj or a low σ2

-Ultimately, how big is Bjhat relative to its standard error?

-Ceteris Paribus, it is best to have little correlation between xj and all other independent variables

-Dropping independent variables will reduce multicollinearity

-But if these variables are valid, we have created bias

-Multicollinearity can always be fought by collecting more data

-Sometimes multicollinearity is due to over specifying independent variables:

-In a study of heart disease, our economic model is:

Heart disease=f(fast food, junk food, other)

-Unfortunately, Rfast food2 is high, showing a high correlation between fast food and other x variables (especially junk food)

-since fast food and junk food are so correlated, they should be examined together; their separate effects are difficult to calculate

-Breaking up variables that can be added together can often cause Multicollinearity

-it is important to note that multicollinearity may not affect ALL OLS estimates

-take the following equation:

-if x2 and x3 are correlated, Var(B2hat) and Var(B3hat) will be large (due to multicollinearity)

-HOWEVER, from (3.51), if x1 is fully uncorrelated with x2 and x3, R12=0 and

-Whether or not to include an independent variable is a balance between bias and variance:

-take the following equation:

-where both variables, x1 and x2, are included

-Compare to the following equation with x2 omitted:

If the true B2≠0 and x1 and x2 have ANY correlation, B1tilde is biased

-Focusing on bias, B1hat is preferred

-Considering variance complicates things

-From (3.51), we know that:

-Modifying a proof from chapter 2, we know that:

-It is evident that unless x1 and x2 are uncorrelated in the sample, Var(B1tilde) is always smaller than Var(B1hat).

-Obviously, if x1 and x2 aren’t correlated, we have no bias and no multicollinearity

-If x1 and x2 are correlated:

1) If B2≠0, B1tilde is biased, B1hat is unbiased

Var(B1tilde)< Var(B1hat)

2) If B2≠0, B1tilde is unbiased, B1hat is unbiased

Var(B1tilde)< Var(B1hat)

-Obviously in the second situation omit x2. If it has no real impact on y, adding it only causes multicollinearity and reduces OLS’s efficiency

-Never include irrelevant variables

-In the first case (B2≠0), leaving x2 out of the model results in a biased estimator of B1

-If the bias is small compared to the variance advantages, traditional econometricians have omitted x2

-However, 2 points argue for including x2:

- Bias doesn’t shrink with n, but variance does
- Error variance increases with omitted variables

- Sample size, bias and variance
-from discussion on (3.45), roughly bias doesn’t increase with sample size

-from (3.51), increasing sample size increases SSTj and therefore decreases variance:

-One can avoid bias and fight multicollinearity by increasing sample size

2) Error variance and omitted variables

-When x2 is omitted and B2≠0, (3.55) underestimates error

-Without including x2 in the model, x2’s variance is added to the error’s variance

-higher error variance increases Bjhat’s variance

-In order to obtain unbiased estimators of Var(Bjhat), we must first find an unbiased estimator of σ2.

-Since we know that σ2=E(u2), an unbiased estimator of σ2 would be:

-Unfortunately, this is not a true estimator as we do not observe the errors ui.

-We know that errors and residuals can be written as:

Therefore a natural estimate of σ2 would replace u with uhat

-However, as seen in the bivariate case, this leads to bias, and we had to divide by n-2 to become a consistent estimator

-To make our estimate of σ2 consistent, we divide by the degrees of freedom n-k-1:

Where k is the number of independent variables

-Notice in the bivariate case k=1 and the denominator is n-2. Also note:

-Technically, n-k-1 comes from the fact that E(SSR=(n-k-1)σ2

-Intuitively, from OLS’s first order conditions:

There are therefore k+1 restrictions on OLS residuals (j=1,2,…k)

-If we therefore have n-(k+1) residuals we can use these restrictions to find the remaining residuals

Under the Gauss-Markov Assumptions MLR. 1 through MLR. 5,

Note: This proof requires matrix algebra and is found in Appendix E

-the positive square root of σhat2, σhat, is called the STANDARD ERROR OF THE REGRESSION (SER), or the STANDARD ERROR OF THE ESTIMATE

-SER is an estimator of the standard deviation of the error term

-when another independent variable is added to the equation, both SSR and the degrees of freedom fall

-Therefore an additional variable may increase or decrease SER

In order to construct confidence intervals and perform hypothesis tests, we need the STANDARD DEVIATION OF BJHAT:

Since σ is unknown, we replace it with its estimator,

σhat, to give us the STANDARD ERROR OF BJHAT:

-since the standard error depends on σhat, it has a sampling distribution

-Furthermore, standard error comes from the variance formula, which relies on homoskedasticity (MLR.5)

-While heteroskedasticity doesn’t cause bias in Bjhat, it does affect its variance and therefore cause bias in its standard errors

-Chapter 8 covers how to correct for heteroskedasticity

-MLR. 1 through MLR. 4 show that OLS is unbiased, but many unbiased estimators exist

-HOWEVER, using MLR.1 through MLR.5, OLS’s estimate Bjhat of Bj is BLUE:

Best

Linear

Unbiased

Estimator

Estimator

-OLS is an estimator as “it is a rule that can be applied to any sample of data to produce an estimate”

Unbiased

-Since OLS’s estimate has the property

OLS is unbiased

Linear

-OLS’s estimates are linear since Bjhat can be expressed as a linear function of the data on the dependent variable

Where wij is a function of independent variables

-This is evident from equation (3.22)

Best

-OLS is best since it has the smallest variance of all linear unbiased estimators

The Gauss-Markov theorem states that, given assumptions MLR. 1 through MLR.5, for any other estimator Bjtilde that is linear and unbiased:

And this equality is generally strict

Under the Assumptions MLR. 1 through MLR. 5,

Are respectively the best linear unbiased estimators (BLUE’s) of

-if our assumptions hold, no linear unbiased estimator will be a better choice than OLS

-if we find any other unbiased linear estimator, its variance will be at least as big as OLS’s

-If MLR.4 fails, OLS is biased and Theorem 3.4 fails

-If MLR.5 (homoskedasticity) fails, OLS is not biased but no longer has the smallest variance, it is LUE