1 / 11

Comparison of Fixed Effects and Random Effects Models in Regression Analysis

Learn how to distinguish between within-panel means and Estimate LSDV model for your data analysis. Understand the relevance of group identification for fixed-effects estimation and interpreting R2 variations between group analysis. Store fixed-effects estimates for Hausman test.

amish
Download Presentation

Comparison of Fixed Effects and Random Effects Models in Regression Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Notice R2 and compare to FE in next regression

  2. Areg constructs within Panel means rather than Estimate LSDV model Data must Be sorted by ID Big jump in R2 ID is the variable That identifies Groups for Fixed-effects Number of groups (N in class notation)

  3. To estimate fixed and random effect models, You must defined the dimension of the daya

  4. This the R2 after within panel Means have been subtracted How much variation is left for you To explain? Notice the Z’s have been Dropped from the analysis

  5. Store the fixed-effects estimates to be used in Construction of the Hausman test

  6. Slightly lower R2 than in the OLS model Group information .3782/(.3782+.2342) Most of the variance is between group, not within

  7. Ask for Hausman test after the RE model is estimated Test statistic P-value Easily reject null in this case

  8. Get between group estimates

  9. Summary of Results (X’s)

  10. Summary of Results (Z’s)

  11. Rate of return to tenure • Yit =β0 + Eitβ1 + Eit\2β2 + Titβ3 + Tit2β4 + Unionitβ5 + EDUCiγ1 + Blackiγ2 + εit dY/dT = β3 + 2 Titβ4 Return to tenure OLS FE RE 5 years 0.0232 0.0128 0.0255 10 years 0.0157 0.0078 0.0170 20 years 0.0007 -0.0022 0.0000

More Related