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Step two of the wizard lets you decide whether to include a White specification. Custom structures are entered in pairs of lags. By Roberto Pedace In econometrics, an extremely common test for heteroskedasticity is the White test, which begins by allowing the heteroskedasticity process to be a function of one or more of your independent variables. For example, the square of a dummy variable is the dummy variable itself, so EViews drops the squared term to avoid perfect collinearity.

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If you do not elect to include a White specification and click on Next, EViews will skip the White Specification page, and continue on to the next section of the wizard. The Custom Test Wizard lets you combine or specify in greater detail the various tests. This statistic is distributed as a with degrees of freedom equal to the number of variables in. Not all of these tests are available for every specification. If not, you fail to reject the null hypothesis of homoskedasticity.

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The individual tests are outlined below. The third statistic, an LM statistic, is the explained sum of squares from the auxiliary regression divided by. If the original equation was non-linear this button will add the coefficient gradients from that equation. The ARCH specification lets you specify a lag structure. In our example we choose to add the variable Y to the auxiliary regression: Next we can add ARCH terms to the auxiliary regression. We begin by regressing money supply M1 on a constant, contemporaneous industrial production IP and three lags of IP using the equation specification m1 c ip 0 to -3 The serial correlation LM test results for this equation with 2 lags in the test equation strongly reject the null of no serial correlation: Heteroskedasticity Tests This set of tests allows you to test for a range of specifications of heteroskedasticity in the residuals of your equation.

Koenker's statistic is also distributed as a with degrees Melissa standish enumclaw wa newspaper freedom equal to the number of variables in. This is a regression of the squared residuals on a constant and lagged squared residuals up to order. Note that if you have already included a White specification and your original equation had a constant term.

Note if you when you provide a set of variables that differs from those in the original equation. This is a regression of the residuals on the original regressors and lagged residuals up to order. This test results in the following output: This output contains both the set of test statistics, and the the test is no longer a White test, but based.

**Kazralkree**

The test belongs to the class of asymptotic large sample tests known as Lagrange multiplier LM tests. If you do not elect to include a White specification and click on Next, EViews will skip the White Specification page, and continue on to the next section of the wizard. The Jarque-Bera statistic has a distribution with two degrees of freedom under the null hypothesis of normally distributed errors. For our choices, the final specification looks like this: Our ARCH specification with lags of 1, 2, 3, 6 is shown first, followed by the White specification, and then the additional term, Y. If the test indicates serial correlation in the residuals, LS standard errors are invalid and should not be used for inference. Failure of any one of these conditions could lead to a significant test statistic.

**Nesho**

This is a regression of the squared residuals on a constant and lagged squared residuals up to order. The Custom Test Wizard lets you combine or specify in greater detail the various tests.

**Vugar**

The exact finite sample distribution of the F-statistic under is not known, but the LM test statistic is asymptotically distributed as a under quite general conditions.

**Metaur**

Therefore, we recommend its use in preference to the DW statistic whenever you are concerned with the possibility that your errors exhibit autocorrelation.

**Dorn**

All three statistics reject the null hypothesis of homoskedasticity. This is no longer the case—level values are only included if the original regression included a constant. The ARCH specification lets you specify a lag structure. When you plug this information into STATA which lets you run a White test via a specialized command , the program retains the predicted Y values, estimates the auxiliary regression internally, and reports the chi-squared test.

**Vitilar**

Conversely, a non-significant test statistic implies that none of the three conditions is violated. The F-statistic is an omitted variable test for the joint significance of all lagged squared residuals.

**Julkree**

For Dummies: The Podcast. The exact finite sample distribution of the F-statistic under is not known, but the LM test statistic is asymptotically distributed as a under quite general conditions. In our example we choose to add the variable Y to the auxiliary regression: Next we can add ARCH terms to the auxiliary regression. Estimate the model using OLS: Retain the R-squared value from this regression: Calculate the F-statistic or the chi-squared statistic: The degrees of freedom for the F-test are equal to 2 in the numerator and n — 3 in the denominator. Note that if you have already included a White specification and your original equation had a constant term, your auxiliary regression will already include level values of the original equation regressors since the cross-product of the constant term and those regressors is their level values.

**Ninos**

In our example we choose to add the variable Y to the auxiliary regression: Next we can add ARCH terms to the auxiliary regression. Because the omitted variables are residuals and not independent variables, the exact finite sample distribution of the F-statistic under is still not known, but we present the F-statistic for comparison purposes. The test statistic is then based on the auxiliary regression: This view is available for equations estimated by least squares, two-stage least squares, and nonlinear least squares estimation. This approach does not affect the asymptotic distribution of the statistic, and Davidson and MacKinnon argue that doing so provides a test statistic which has better finite sample properties than an approach which drops the initial observations.

**Shaktizragore**

If not, you fail to reject the null hypothesis of homoskedasticity. First, suppose you have estimated the regression; The exact finite sample distribution of the F-statistic under is not known, but the LM test statistic is asymptotically distributed as a under quite general conditions. Not all of these tests are available for every specification. Therefore, we recommend its use in preference to the DW statistic whenever you are concerned with the possibility that your errors exhibit autocorrelation. If you elect to do so, EViews will display a dialog prompting you to add additional regressors.

**Akinot**

The ARCH specification lets you specify a lag structure. The test is performed by completing an auxiliary regression of the squared residuals from the original equation on. The Custom Test Wizard lets you combine or specify in greater detail the various tests. In our example we choose to add the variable Y to the auxiliary regression: Next we can add ARCH terms to the auxiliary regression. To test for this form of heteroskedasticity, an auxiliary regression of the log of the original equation's squared residuals on is performed. Each of these tests involve performing an auxiliary regression using the residuals from the original equation.