Improved estimation methods

Oct 13, 2025 by recoveryshake.com 34 views

Introduction

When working with panel data, fixed effects models are a crucial tool for accounting for individual-specific effects and time-invariant variables. In R, two popular packages for estimating fixed effects models are plm and feols. While both packages can handle fixed effects, they differ in their approach and implementation. In this article, we will delve into the differences between plm() and feols() and provide guidance on when to use each.

What are Fixed Effects Models?

Fixed effects models are a type of regression analysis that accounts for individual-specific effects and time-invariant variables. These models are particularly useful when working with panel data, where the same units (e.g., individuals, firms, or countries) are observed over multiple time periods. The goal of fixed effects models is to estimate the relationship between a dependent variable and a set of independent variables while controlling for individual-specific effects.

plm() vs. feols(): A Brief Overview

plm() is a function from the plm package, which is a comprehensive package for panel data analysis in R. plm() can handle a wide range of fixed effects models, including pooled OLS, fixed effects, and random effects models. feols(), on the other hand, is a function from the fixest package, which is a more recent package for fixed effects models in R. feols() is designed to be more flexible and user-friendly than plm().

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

plm() uses a traditional fixed effects approach, where the individual-specific effects are absorbed into the error term. This approach is based on the assumption that the individual-specific effects are correlated with the independent variables. feols(), on the other hand, uses a more modern approach, where the individual-specific effects are treated as a random effect. This approach is based on the assumption that the individual-specific effects are uncorrelated with the independent variables.

Key Differences between plm() and feols()

While both plm() and feols() can handle fixed effects, there are several key differences between the two functions:

Model specification: plm() requires a more complex model specification, where the user must specify the type of fixed effects model to be estimated. feols(), on the other hand, uses a more flexible model specification, where the user can specify the type of fixed effects model using a simple syntax.
Estimation method: plm() uses a traditional fixed effects approach, where the individual-specific effects are absorbed into the error term. feols(), on the other hand, uses a more modern approach, where the individual-specific effects are treated as a random effect.
Assumptions: plm() assumes that the individual-specific effects are correlated with the independent variables. feols(), on the other hand, assumes that the individual-specific effects are uncorrelated with the independent variables.
Output: plm() produces a more detailed output, including the estimated coefficients, standard errors, and p-values. feols(), on the other hand, produces a more concise output, including the estimated coefficients and standard errors.

Example Use Cases

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

Pooled OLS: plm() can be used to estimate a pooled OLS model, where the individual-specific effects are not accounted for. feols() can also be used to estimate a pooled OLS model, but it is not the recommended approach.
Fixed effects: plm() can be used to estimate a fixed effects model, where the individual-specific effects are absorbed into the error term. feols() can also be used to estimate a fixed effects model, but it is not the recommended approach.
Random effects: feols() can be used to estimate a random effects model, where the individual-specific effects are treated as a random effect.

Conclusion

In conclusion, plm() and feols() are two popular packages for estimating fixed effects models in R. While both packages can handle fixed effects, they differ in their approach and implementation. plm() uses a traditional fixed effects approach, where the individual-specific effects are absorbed into the error term. feols(), on the other hand, uses a more modern approach, where the individual-specific effects are treated as a random effect. When choosing between plm() and feols(), the user should consider the type of fixed effects model to be estimated and the assumptions of the model.

Recommendations

Based on the differences between plm() and feols(), we recommend the following:

Use plm() for traditional fixed effects models: plm() is a good choice for traditional fixed effects models, where the individual-specific effects are absorbed into the error term.
Use feols() for modern fixed effects models: feols() is a good choice for modern fixed effects models, where the individual-specific effects are treated as a random effect.
Use feols() for random effects models: feols() is a good choice for random effects models, where the individual-specific effects are treated as a random effect.

Future Directions

The development of fixed effects models in R is an active area of research. Future developments in this area may include:

Improved estimation methods: New estimation methods may be developed to improve the efficiency and accuracy of fixed effects models.
Increased flexibility: New packages may be developed to provide increased flexibility in the specification of fixed effects models.
Improved output: New packages may be developed to provide improved output, including more detailed and informative results.

References

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models for repeated measures data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(3), 267-283.
Rabe-Hesketh, S., & Skrondal, A. (2005). Multilevel and longitudinal modeling using Stata. Stata Press.
Hox, J. J. (2010). Multilevel analysis: Techniques and applications. Routledge.
Q&A: R plm() or feols()? A Comprehensive Guide to Fixed Effects Models in R ====================================================================================

Introduction

In our previous article, we discussed the differences between plm() and feols() in R, two popular packages for estimating fixed effects models. In this article, we will answer some frequently asked questions (FAQs) about plm() and feols() to help you choose the right package for your research.

Q: What is the main difference between plm() and feols()?

A: The main difference between plm() and feols() is the approach they use to handle fixed effects. plm() uses a traditional fixed effects approach, where the individual-specific effects are absorbed into the error term. feols(), on the other hand, uses a more modern approach, where the individual-specific effects are treated as a random effect.

Q: Which package is more flexible?

A: feols() is more flexible than plm(). feols() allows you to specify the type of fixed effects model using a simple syntax, while plm() requires a more complex model specification.

Q: Which package is more user-friendly?

A: feols() is more user-friendly than plm(). feols() has a more intuitive syntax and produces a more concise output, making it easier to use and understand.

Q: Can I use both plm() and feols() for the same data?

A: Yes, you can use both plm() and feols() for the same data. However, the output may differ due to the different approaches used by each package.

Q: How do I choose between plm() and feols()?

A: To choose between plm() and feols(), consider the type of fixed effects model you want to estimate and the assumptions of the model. If you want to estimate a traditional fixed effects model, use plm(). If you want to estimate a modern fixed effects model or a random effects model, use feols().

Q: Can I use plm() for random effects models?

A: No, plm() is not designed for random effects models. feols() is a better choice for random effects models.

Q: Can I use feols() for pooled OLS models?

A: Yes, feols() can be used for pooled OLS models. However, it is not the recommended approach, as plm() is more suitable for pooled OLS models.

Q: How do I interpret the output of plm() and feols()?

A: The output of plm() and feols() is similar, but the interpretation may differ due to the different approaches used by each package. Consult the documentation and examples for each package to understand how to interpret the output.

Q: Can I use plm() and feols() together in the same analysis?

A: Yes, you can use plm() and feols() together in the same analysis. However, be aware that the output may differ due to the different approaches used by each package.

Q: Are there any limitations of plm() and feols()?

A: Yes, both plm() and feols() have limitations. plm() is limited to traditional fixed effects models, while feols() is limited to modern fixed effects models and random effects models. Additionally, both packages may not handle complex data structures or large datasets efficiently.

Conclusion

In conclusion, plm() and feols() are two popular packages for estimating fixed effects models in R. While both packages can handle fixed effects, they differ in their approach and implementation. By understanding the differences between plm() and feols(), you can choose the right package for your research and produce accurate and reliable results.

Recommendations

Based on the FAQs, we recommend the following:

Use plm() for traditional fixed effects models: plm() is a good choice for traditional fixed effects models, where the individual-specific effects are absorbed into the error term.
Use feols() for modern fixed effects models and random effects models: feols() is a good choice for modern fixed effects models and random effects models, where the individual-specific effects are treated as a random effect.
Use feols() for pooled OLS models: feols() can be used for pooled OLS models, but it is not the recommended approach.
Use plm() and feols() together with caution: While it is possible to use plm() and feols() together in the same analysis, be aware that the output may differ due to the different approaches used by each package.

Future Directions

The development of fixed effects models in R is an active area of research. Future developments in this area may include:

Improved estimation methods: New estimation methods may be developed to improve the efficiency and accuracy of fixed effects models.
Increased flexibility: New packages may be developed to provide increased flexibility in the specification of fixed effects models.
Improved output: New packages may be developed to provide improved output, including more detailed and informative results.

References

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models for repeated measures data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(3), 267-283.
Rabe-Hesketh, S., & Skrondal, A. (2005). Multilevel and longitudinal modeling using Stata. Stata Press.
Hox, J. J. (2010). Multilevel analysis: Techniques and applications. Routledge.