How to Detect Heteroscedasticity using Eviews

Detecting heteroskedasticity in EViews involves running specific tests and analyzing residual plots. Here are the steps:

Step-by-Step Guide:

1. Run Initial OLS Regression:
• Load your dataset in EViews.
• Go to Quick -> Estimate Equation.
• Enter your regression equation (e.g., Y = C(1) + C(2)*X1 + C(3)*X2).
• Click OK to estimate using Ordinary Least Squares (OLS).
2. Plot Residuals:
• Visual inspection is a preliminary step. Plot the residuals to see if there’s any visible pattern.
• Go to View -> Actual, Fitted, Residual -> Residual Graph.
• Examine the plot for any patterns (funnel shapes indicate heteroskedasticity).
3. Conduct Heteroskedasticity Tests:
• Breusch-Pagan-Godfrey Test:
• Go to View -> Residual Diagnostics -> Heteroskedasticity Tests.
• Select Breusch-Pagan-Godfrey and click OK.
• Review the test results. If the p-value is low (typically < 0.05), reject the null hypothesis of homoskedasticity.
• White Test:
• Go to View -> Residual Diagnostics -> Heteroskedasticity Tests.
• Select White and click OK.
• Similar to the Breusch-Pagan test, a low p-value indicates heteroskedasticity.
4. Analyze Test Results:
• Look at the p-values of the tests. If the p-value is below your significance level (e.g., 0.05), there is evidence of heteroskedasticity.
• Examine the auxiliary regression output (the regression of squared residuals on the original regressors and their squares/interactions) for additional insights.

Detailed Explanation:

• Breusch-Pagan-Godfrey Test:
• This test regresses the squared residuals from the original OLS regression on the independent variables.
• It tests if the variance of the errors is dependent on the independent variables.
• Formula: ϵi2=α0+α1X1i+α2X2i+…+ui\epsilon_i^2 = \alpha_0 + \alpha_1X_{1i} + \alpha_2X_{2i} + … + u_i
• White Test:
• This test is more general and does not assume a specific form of heteroskedasticity.
• It includes squares and cross-products of the independent variables.
• Formula: ϵi2=α0+α1X1i+α2X2i+α3X1i2+α4X2i2+α5X1iX2i+…+ui\epsilon_i^2 = \alpha_0 + \alpha_1X_{1i} + \alpha_2X_{2i} + \alpha_3X_{1i}^2 + \alpha_4X_{2i}^2 + \alpha_5X_{1i}X_{2i} + … + u_i

Practical Example:

Suppose you have a model: Y=β0+β1X1+β2X2+ϵY = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \epsilon

1. Run OLS Regression:
• Estimate the model in EViews.
2. Plot Residuals:
• Check the residual plot for any heteroskedasticity pattern.
3. Conduct Tests:
• Perform the Breusch-Pagan-Godfrey test and/or the White test.
4. Interpret Results:
• If tests show significant p-values, heteroskedasticity is present, indicating the need for correction.

By following these steps, you can effectively detect heteroskedasticity in your regression model using EViews.

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