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How to correct Heteroscedasticity with Robust Standard Errors using Eviews

To correct heteroskedasticity using robust standard errors in EViews, follow these expanded steps:

Step-by-Step Guide:

  1. Run the Initial OLS Regression:
    • Open EViews and load your dataset.
    • Navigate to Quick -> Estimate Equation.
    • Enter the regression equation in the form of Dependent Variable = C(1) + C(2)*Independent Variable + ....
    • Click OK to estimate the equation using Ordinary Least Squares (OLS).
  2. Diagnose Heteroskedasticity:
    • After estimating the equation, check for heteroskedasticity. Go to View -> Residual Diagnostics -> Heteroskedasticity Tests.
    • Choose a specific test like the Breusch-Pagan-Godfrey test or the White test.
    • Analyze the test results to confirm the presence of heteroskedasticity.
  3. Apply Robust Standard Errors:
    • To adjust for heteroskedasticity, use robust standard errors. Go to View -> Coefficient Diagnostics -> Heteroskedasticity Consistent Covariance.
    • Select the desired robust option, such as White’s method (default robust standard errors) or Newey-West HAC (heteroskedasticity and autocorrelation consistent).
    • Click OK. EViews will re-estimate the standard errors of your coefficients, adjusting for heteroskedasticity.
  4. Interpret Results:
    • Review the new output, which includes robust standard errors. These adjusted standard errors are used to assess the statistical significance of the regression coefficients.
    • Compare the p-values with your significance level to determine which predictors are significant, accounting for heteroskedasticity.

Detailed Explanation:

  • Robust Standard Errors:
    • Robust standard errors provide a way to correct for heteroskedasticity by adjusting the standard errors of the regression coefficients. This adjustment does not change the coefficient estimates themselves but alters the precision measures (standard errors).
    • In the presence of heteroskedasticity, the usual OLS standard errors can be biased, leading to incorrect inferences. Robust standard errors correct this bias, giving more reliable confidence intervals and hypothesis tests.
  • Heteroskedasticity Tests:
    • Tests like Breusch-Pagan and White help detect heteroskedasticity by examining whether the variance of the errors varies systematically with the independent variables.
    • A significant test result suggests heteroskedasticity is present, necessitating correction.
  • Covariance Options:
    • White’s method: This is the most commonly used approach for robust standard errors and is appropriate when dealing with general forms of heteroskedasticity.
    • Newey-West method: This is useful when there is both heteroskedasticity and autocorrelation in the error terms, making it a more comprehensive adjustment.

Practical Implications:

Using robust standard errors ensures that your statistical inferences are valid even when the assumption of homoskedasticity (constant variance of errors) is violated. This is crucial in empirical research where heteroskedasticity is a common issue. By following these steps in EViews, you ensure that your regression results are more robust and reliable.


Suppose you have a regression model: GDP=β0+β1Investment+β2Consumption+ϵ\text{GDP} = \beta_0 + \beta_1 \text{Investment} + \beta_2 \text{Consumption} + \epsilon

  • Run the OLS regression to get initial estimates.
  • Test for heteroskedasticity using the Breusch-Pagan test.
  • If heteroskedasticity is detected, apply robust standard errors using White’s method.
  • Review the output to see the adjusted standard errors and reassess the significance of investment and consumption on GDP.

By doing so, you correct for heteroskedasticity, ensuring your confidence intervals and hypothesis tests are accurate.

This video will explain the process:

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