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

### Step-by-Step Guide:

**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).

**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).

**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.

- Go to
**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.

- Go to

**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: $ϵ_{i}=α_{0}+α_{1}X_{i}+α_{2}X_{i}+…+u_{i}$

**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: $ϵ_{i}=α_{0}+α_{1}X_{i}+α_{2}X_{i}+α_{3}X_{i}+α_{4}X_{i}+α_{5}X_{i}X_{i}+…+u_{i}$

### Practical Example:

Suppose you have a model: $Y=β_{0}+β_{1}X_{1}+β_{2}X_{2}+ϵ$

**Run OLS Regression**:- Estimate the model in EViews.

**Plot Residuals**:- Check the residual plot for any heteroskedasticity pattern.

**Conduct Tests**:- Perform the Breusch-Pagan-Godfrey test and/or the White test.

**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.