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Heteroskedasticity Lecture 17 Lecture 17

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**Today’s plan How to test for it: graphs, Park and Glejser tests**

What we can do if we find heteroskedasticity How to estimate in the presence of heteroskedasticity Lecture 17

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**Palm Beach County revisited**

How far is Palm Beach an outlier? Can the outlier be explained by heteroskedasticity? If so, what are the consequences? Heteroskedasticity will affect the variance of the regression line It will consequently affect the variance of the estimated coefficients and estimated 95 percent confidence interval for the prediction (see Lecture 10). L17.xls provides an example of how to work through a problem like this using Excel Lecture 17

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**Palm Beach County revisited (2)**

Palm Beach is a good example to use since there are scale effects in the data The voting pattern shows that the voting behavior and number of registered voters are related to the population in each county As the county gets larger, voting patterns may diverge from what would be assumed given the number of registered voters Note from the graph: as we move away from the origin, the difference between registered Reform voters and Reform votes cast increases We’ll hypothesize that this will have an affect on heteroskedasticity Lecture 17

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Notation Heteroskedasticity is observed as cross-section variability in the data data across units at point in time In our notation, heteroskedasticity is: E(ei2) 2 We can also write: E(ei2) = i2 This means that we expect variable variance: the variance changes with each unit of observation Lecture 17

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**Consequences When heteroskedasticity is present:**

1) OLS estimator is still linear 2) OLS estimator is still unbiased 3) OLS estimator is not efficient - the minimum variance property no longer holds 4) Estimates of the variances are biased 5) is not an unbiased estimator of sYX2 6) We can’t trust the confidence intervals or hypothesis tests (t-tests & F-tests): we may draw the wrong conclusions Lecture 17

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Consequences (2) When BLUE holds and there is homoskedasticity, the first-order condition gives: With heteroskedasticity, we have: If we substitute the equation for ci to both equations, we find: where Lecture 17

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Cases With homoskedasticity: around each point, the variance around the regression line is constant With heteroskedasticity: around each point, the variance around the regression line varies with each value of the independent variable (with each i) Lecture 17

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**Detecting heteroskedasticity**

There are three ways of detecting heteroskedastiticy: 1) Graphically 2) Park Test 3) Glejser Test Lecture 17

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Graphical detection Graph the errors (or error squared) against the independent variable(s). Note: you can use either e or e2 on the y-axis. With homoskedasticity we have E(ei, X) = 0 : The errors are independent of the independent variables With heteroskedasticity we can get a variety of patterns The errors show a systematic relationship with the independent variables Lecture 17

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**Graphical detection (2)**

Using the Palm Beach example (L17.xls), the estimated regression equation was: The errors of this equation, can be graphed against the number of registered Reform party voters, (the independent variable) Graph shows that the errors increasing with the number of registered reform voters While the graphs may be convincing, we also want to use a test to confirm this. We have two: Lecture 17

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Park Test Procedure: 1) Run regression Yi = a + bXi + ei despite the heteroskedasticity problem (it can also be multivariate) 2) Obtain residuals (ei), square them (ei2), and take their logs (ln ei2) 3) Run a spurious regression: 4) Do a hypothesis test on with H0: g1 = 0 5) Look at the results of the hypothesis test: reject the null: you have heteroskedasticity fail to reject the null: homoskedasticity, or which is a constant Lecture 17

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Glejser Test When we use the Glejser, we’re looking for a scaling effect The procedure: 1) Run the regression (it can also be multivariate) 2) Collect ei terms 3) Take the absolute value of the errors 4) Regress |ei| against independent variable(s) you can run different kinds of regressions: Lecture 17

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**Glejser Test (2) 4) [continued]**

If heteroskedasticity takes one of these forms, this will suggest an appropriate transformation of the model The null hypothesis is still H0: g1 = 0 since we’re testing for a relationship between the errors and the independent variables We reach the same conclusions as in the Park Test Lecture 17

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A cautionary note The errors in the Park Test (vi) and the Glejser Test (ui) might also be heteroskedastic. If this is the case, we cannot trust the hypothesis test H0: g1 = 0 or the t-test If we find heteroskedastic disturbances in the data, what can we do? Estimate the model Yi = a + bXi + ei using weighted least squares We’ll look at two examples of weighted least squares: one where we know the true variance, and one where we don’t Lecture 17

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**Correction with known i2**

Given that the true variance is known and our model is: Yi = a + bXi + ei Consider the following transformation of the model: In the transformed model, let So the expected value of the error squared is: Lecture 17

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**Correction with known i2 (2)**

Given that there is heteroskedasticity, E(ei2) = i2 thus: In this simplistic example, we re-weighted model by the constant i What this example shows: when the variance is known, we must transform our model to obtain a homoskedastic error term. Lecture 17

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**Correction with unknown i2**

Given an unknown variance, we need to state the ad-hoc but plausible assumptions with our variance i2 (how the errors vary with the independent variable) For example: we can assert that E(ei2) = 2Xi Remember: Glejser Test allows us to choose a relationship between the errors and the independent variable Lecture 17

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**Correction with unknown i2 (2)**

In this example you would transform the estimating equation by dividing through by to get: Letting: The expected value of this error squared is: Lecture 17

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**Correction with unknown i2 (3)**

Recalling an earlier assumption, we find: When we don’t know the true variance we re-scale the estimating equation by the independent variable Lecture 17

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**Returning to Palm Beach**

On L17.xls we have presidential election data by county in Florida To get a correct estimating equation, we can run a regression without Palm Beach if we think it’s an outlier. Then we can see if we can obtain a prediction for the number of reform votes cast in Palm Beach We can perform a Glejser Test for the regression excluding Palm Beach We run a regression of the absolute value of the errors (|ei|)against registered Reform voters (Xi) Lecture 17

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**Returning to Palm Beach (2)**

The t-test rejects the null this indicates the presence of heteroskedasticity We can re-scale the model in different ways or introduce a new independent variable (such as the total number of registered voters by county) Keep transforming the model and running the Glejser Test When we fail to reject the null: there is no longer heteroskedasticity in the model Lecture 17

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Robust estimation Heteroskedastic tests not used any more. Most software reports robust standard errors. Note that this is also the approach of the text book. Have looked at tests for heteroskedasticity to get you used to weighted least squares. Important for the topics to come. Robust standard errors report approximations to the estimation of the variance for the coefficient when there is a non-constant variance. It only holds for large samples. Know that for a homoskedastic error term Var(ui|Xi) = s2: Var(b) = s2/Sxi2 Lecture 17

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Robust estimation (2) Using analogous arguments, we can state that for the heteroskedastic case: Var(ui|Xi) = si2: Var(b) = si2 Sxi2 /(Sxi2)2 This can be approximated (in the bi-variate model case) by: Var(b) = Sxi2ui2 /(Sxi2)2 See L17_robust.xls and hetero.pdf to compare the results from calculating the robust standard error on the spreadsheet using EXCEL and the results from STATA for robust estimation. Lecture 17

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Summary Even with re-weighted equations, we might still have heteroskedastic errors so we have to rerun the Glejser Test until we cannot reject the null If we cannot reject the null, we may have to rethink our model transformation if we suspect a scale effect, we may want to introduce new scaling variables Variables from the re-scaled equation are comparable with the coefficients from the original model Lecture 17

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