Panel study, multiple linear regression, assumptions

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SUMMARY

This discussion centers on the application of multiple linear regression analysis in a panel study involving a set of companies over a 7-year period. The key assumptions for using fixed or random effects models include normal distribution of variables, linear relationships between independent and dependent variables, homoscedasticity, and independent normally distributed residuals. Additionally, it is essential to test for correlation due to the time index present in the model. The Hausman test will be utilized to determine the appropriate model choice between fixed or random effects.

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  • Understanding of multiple linear regression analysis
  • Knowledge of fixed and random effects models
  • Familiarity with the Hausman test
  • Concepts of homoscedasticity and residual analysis
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  • Research the assumptions of multiple linear regression in depth
  • Learn how to conduct the Hausman test for model selection
  • Explore methods for testing for correlation in panel data
  • Study techniques for diagnosing homoscedasticity in regression models
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Researchers, data analysts, and statisticians involved in panel data analysis and multiple linear regression modeling, particularly those focusing on fixed and random effects methodologies.

monsmatglad
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Hey.

I am doing a project where I am studying a set of companies over a 7-year period. I am doing a multiple linear regression analysis either with fixed or random effects (so, it's a panel study). What I am wondering is if the general assumptions/requirements apply when using the fixed/random effects technique, so that I should test for them to ensure they are fulfilled?

The assumptions I am referring to are:

- The variables are normally distributed
- The relation between the independent variables and the dependent variables are linear
- homoscedasticity
- independent and normally distributed residuals

(I plan to use a Hausman's test to decide on whether to use the fixed or random effects model)

Thanks in advance
Mons
 
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Yes and you should also test for correlation since you have a time index in your model. (While that may be assumed by iid portion I find that not many people actually test for it).
 
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