Panel Data Regression Tutorial with EViews

EViews (Econometric Views) is highly robust industry-standard software for processing panel data models. Its Windows-based interface combined with Command Line features makes it a favorite for students unaccustomed to pure coding syntax like R or Python.

Stage 1: Preparing Data Structure (Excel)

The biggest mistake EViews beginners make happens even before opening the program: incorrectly formatting the spreadsheet. Ensure your Excel file is in Stacked Data format:

Column 1: Company Code (Cross-section identifier, e.g., AAPL)
Column 2: Year (Time identifier, e.g., 2021)
Column 3 onwards: Your research variables (ROA, DER, SIZE, etc.)

Stage 2: Data Import Process (Import to Workfile)

Open EViews, then follow these steps:

Click File > Import > Import from file... and select your Excel file.
At the Basic structure stage, EViews will automatically detect your format as Dated Panel. If not, use the drop-down menu to set it to "Dated Panel".
Determine the "Cross section ID series" column (Select the Company Code column) and "Date series" (Select the Year column).
Click Finish. You now have a *Workfile* ready to be processed.

Stage 3: Regression Equation Estimation

There are two ways to regress in EViews. The manual way via menus (Quick > Estimate Equation), or using the white Command window at the top. The *Command* approach is far more efficient:

// Estimating Pooled OLS (Common Effect)
ls roa c der size

// Estimating Fixed Effect Model (FEM)
ls(cx=f) roa c der size

// Estimating Random Effect Model (REM)
ls(cx=r) roa c der size

Note: `ls` is Least Squares, `roa` is the dependent, `c` is the constant, the rest are independent.

Troubleshooting: Common Error Messages

1. Error "Near singular matrix"
This message appears when there is perfect multicollinearity. It usually occurs if you input two variables with exactly identical formulas, or input a *dummy* variable where the number of categories exceeds the limit (Dummy Trap). Solution: Remove one of the overlapping independent variables.

2. Error "Insufficient number of observations"
Occurs when your number of observations is too few compared to the number of variables. If you use *Fixed Effect*, degrees of freedom decrease drastically because each company consumes 1 parameter. Solution: Add year/company samples, or use Random/Common Effect models.