Panel study, multiple linear regression, assumptions

In summary, the speaker is conducting a project on a group of companies over a 7-year period and is using multiple linear regression analysis with either fixed or random effects. They are wondering if the general assumptions for this technique apply, including normal distribution of variables, linear relationship between independent and dependent variables, homoscedasticity, and independent and normally distributed residuals. They also plan to use a Hausman's test for deciding between fixed or random effects. It is suggested to also test for correlation due to the time index in the model.
  • #1
monsmatglad
76
0
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
 
Physics news on Phys.org
  • #2
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).
 
  • Like
Likes monsmatglad

1. What is a panel study?

A panel study is a type of research design in which data is collected from the same group of individuals over a period of time, typically to study changes in behavior or attitudes over time.

2. What is multiple linear regression?

Multiple linear regression is a statistical method used to analyze the relationship between two or more independent variables and a dependent variable. It allows researchers to determine how much of the variation in the dependent variable can be explained by the independent variables.

3. What are the assumptions of multiple linear regression?

The main assumptions of multiple linear regression include linearity, normality, homoscedasticity, and independence of errors. Linearity assumes that the relationship between the independent and dependent variables is linear. Normality assumes that the residuals (differences between predicted and actual values) are normally distributed. Homoscedasticity assumes that the variability of the residuals is constant across all values of the independent variables. Independence of errors assumes that the residuals are not correlated with each other.

4. Why is it important to check assumptions in multiple linear regression?

Checking assumptions in multiple linear regression is important because violations of these assumptions can lead to biased and inaccurate results. If the assumptions are not met, the results of the regression may not be valid and could lead to incorrect conclusions.

5. How can I check if the assumptions of multiple linear regression are met?

There are several ways to check if the assumptions of multiple linear regression are met. These include visual inspection of residual plots, statistical tests such as the Shapiro-Wilk test for normality and the Breusch-Pagan test for homoscedasticity, and examining scatterplots and correlation matrices for evidence of linearity and independence of errors. It is important to check these assumptions before interpreting the results of a multiple linear regression analysis.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
30
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
349
  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
23
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
658
  • Set Theory, Logic, Probability, Statistics
Replies
13
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
281
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
Back
Top