Here are the steps of moderation modelling in time series analysis using EViews, based on a hypothetical scenario:

1. Define Research Objectives and Hypothesis:

   • Clearly state your research question and the moderating effect you’re investigating.
   • For example, you might be studying how marketing affects sales (independent variable) and how economic conditions (moderating variable) influence that relationship.

2. Prepare Data:

   • Import your time series data into EViews.
   • Ensure your data covers a relevant time period for your research question.
   • Consider data transformations (e.g., log transformation) if necessary for normality or stationarity.

3. Specify the Model:

   • Set up the regression equation in EViews. Include your independent variable, dependent variable, and the interaction term.
   • The interaction term is created by multiplying the independent variable by the moderating variable.

4. Estimate the Model:

   • Use the appropriate estimation technique for time series data in EViews.
   • This might involve accounting for factors like seasonality, trends, or autocorrelated errors.

5. Analyze Results:

   • Evaluate the coefficient of the interaction term to determine the significance of the moderating effect.
   • Interpret the results to understand how the moderating variable influences the relationship between the independent and dependent variables.

6. Validate and Refine (Optional):

   • Conduct diagnostic tests to ensure the model assumptions are met.
   • Refine the model as needed based on the diagnostic results or theoretical considerations.

This provides a general roadmap for conducting moderation modelling in EViews with time series data. Remember to consult EViews documentation or additional resources for specific details on estimation techniques and diagnostic tests.

This video illustrates the process: