Running seemingly unrelated regression in R is like having four unique personalities in a group, each with their quirks and traits, but secretly connected through their shared experiences. It’s all about finding the individual equations for each unit while acknowledging the underlying connections. Just like a group of friends, they might seem unrelated, but their shared stories bring them together in unexpected ways. πππ
Introduction π
In this tutorial, we will explore how to run seemingly unrelated regression in R. This method applies a GLS method to exploit the correlation in the errors across the cross-section units. The random effect model results in a particular type of correlation among the errors, making it an equi-correlated model in the SUR model. The errors are independent over time but correlated across the cross-section units.
Understanding Seemingly Unrelated Regression π
The covariance of u, u comma UJS is equal to Sigma IG if T is equal to S is equal to Zer if T is not equal to S. The coefficient estimates obtained in the previous model are under the assumption that the variances of the forms are equal, and the errors for form one and two in the same year are uncorrelated.
Running Seemingly Unrelated Regression in R π
To run seemingly unrelated regression in R, we first need to import the dataset and attach it to R. Then, we’ll require some libraries and packages such as LM test, T Series, PLM, and system fit. Once the data is converted into panel data, we can run the seemingly unrelated regression using the system fit command.
Interpreting the Results π
The regression equations for all cross-sections are different, indicating that they are related through the error terms in seemingly unrelated regression. This method allows for different regression equations to be unrelated, yet assumed to be related through the error terms.
Conclusion π
Seemingly unrelated regression in R is a powerful tool for analyzing data with correlated errors across cross-section units. By following the steps outlined in this tutorial, you can effectively run SUR in R and interpret the results for your own datasets.
Key Takeaways:
- Seemingly unrelated regression exploits correlation in errors across cross-section units
- Running SUR in R requires importing the dataset, attaching it, and using the system fit command
- The method allows for different regression equations to be unrelated, yet assumed to be related through the error terms
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