Not suitable for small datasets

Oct 13, 2025 by magicalceylontours.com 12 views

Introduction

When working with panel data, fixed effects models are a crucial tool for accounting for individual-specific effects that can affect the outcome variable. In R, two popular packages for estimating fixed effects models are plm and feols. While both packages can be used to estimate fixed effects models, they handle fixed effects in different ways, which can lead to different results. In this article, we will compare and contrast the plm() and feols() functions, highlighting their differences and providing guidance on when to use each.

What are Fixed Effects Models?

Fixed effects models are a type of regression model that accounts for individual-specific effects that can affect the outcome variable. These effects can be due to various factors, such as individual characteristics, time-invariant variables, or other unobserved heterogeneity. Fixed effects models are particularly useful when working with panel data, where the same individuals or units are observed over multiple time periods.

How do plm() and feols() Handle Fixed Effects?

The plm() function in the plm package estimates fixed effects models using a within transformation, which involves subtracting the individual-specific means from the data. This transformation eliminates the individual-specific effects, allowing the model to focus on the time-varying effects. The feols() function in the fixest package, on the other hand, estimates fixed effects models using a direct estimation approach, which involves estimating the fixed effects directly from the data.

Key Differences between plm() and feols()

While both plm() and feols() can be used to estimate fixed effects models, there are several key differences between the two functions:

Estimation approach: plm() uses a within transformation, while feols() uses a direct estimation approach.
Fixed effects estimation: plm() estimates fixed effects using a least squares dummy variable (LSDV) approach, while feols() estimates fixed effects using a direct estimation approach.
Model specification: plm() requires the user to specify the model formula, while feols() allows the user to specify the model formula or use a default formula.
Output: The output of plm() and feols() can differ, particularly in terms of the estimated fixed effects and the standard errors.

When to Use plm() and When to Use feols()?

So, when should you use plm() and when should you use feols()? Here are some general guidelines:

Use plm() when:
- You have a large dataset with many time periods and individuals.
- You want to estimate fixed effects models using a within transformation.
- You need to estimate the individual-specific effects directly.
Use feols() when:
- You have a small to medium-sized dataset.
- You want to estimate fixed effects models using a direct estimation approach.
- You need to estimate the fixed effects directly from the data.

Example Use Cases

Here are some example use cases for plm() and feols():

Example 1: Estimating a Fixed Effects Model using plm()

# Load the plm package
library(plm)
data("Produc", package = "plm")

































































Q: What is the main difference between plm() and feols()?
A: The main difference between plm() and feols() is the estimation approach used to estimate fixed effects models. plm() uses a within transformation, while feols() uses a direct estimation approach.
Q: When should I use plm() and when should I use feols()?
A: You should use plm() when you have a large dataset with many time periods and individuals, and you want to estimate fixed effects models using a within transformation. You should use feols() when you have a small to medium-sized dataset, and you want to estimate fixed effects models using a direct estimation approach.
Q: What are the advantages and disadvantages of using plm()?
A: The advantages of using plm() include:

Efficient estimation: plm() uses a within transformation, which can lead to more efficient estimation of the fixed effects.
Easy to use: plm() is a simple and easy-to-use function that can be used to estimate fixed effects models.

The disadvantages of using plm() include:

Limited flexibility: plm() can only be used to estimate fixed effects models using a within transformation.
Not suitable for small datasets: plm() is not suitable for small datasets, as it can lead to inefficient estimation of the fixed effects.

Q: What are the advantages and disadvantages of using feols()?
A: The advantages of using feols() include:

Flexible estimation: feols() can be used to estimate fixed effects models using a direct estimation approach, which can be more flexible than the within transformation used by plm().
Suitable for small datasets: feols() is suitable for small datasets, as it can lead to more efficient estimation of the fixed effects.

The disadvantages of using feols() include:

More complex to use: feols() is a more complex function than plm(), and can be more difficult to use.
May not be as efficient as plm(): feols() may not be as efficient as plm() in terms of estimation, particularly for large datasets.

Q: How do I choose between plm() and feols()?
A: To choose between plm() and feols(), you should consider the following factors:

Dataset size: If you have a large dataset, you may want to use plm(). If you have a small dataset, you may want to use feols().
Estimation approach: If you want to use a within transformation, you may want to use plm(). If you want to use a direct estimation approach, you may want to use feols().
Flexibility: If you need to estimate fixed effects models using a more flexible approach, you may want to use feols().

Q: Can I use both plm() and feols() in the same analysis?
A: Yes, you can use both plm() and feols() in the same analysis. However, you should be aware that the results may differ, particularly in terms of the estimated fixed effects and the standard errors.
Q: How do I interpret the results of plm() and feols()?
A: To interpret the results of plm() and feols(), you should follow the same steps as for any other regression analysis:

Check the assumptions: Check the assumptions of the model, including linearity, homoscedasticity, and normality.
Check the residuals: Check the residuals to ensure that they are randomly distributed and not correlated with the predictors.
Check the coefficients: Check the coefficients to ensure that they are statistically significant and make sense in the context of the analysis.
Check the standard errors: Check the standard errors to ensure that they are reasonable and not too large.

By following these steps, you can ensure that your analysis is accurate and reliable, and that you can interpret the results of plm() and feols() correctly.