1 / 21

Understanding Average Effects in Nonlinear Models and Dummy Variables in Economics

This guide focuses on the average effect of changes in variables within nonlinear models and the use of dummy variables in regression analysis, essential concepts for upcoming oral presentations scheduled for April 24th and April 26th. The average effect of changing independent variables while holding others constant will be explored, along with practical examples of linear-log, log-linear, and log-log functional forms. Additionally, dummy variables and their implications in regression models will be analyzed, providing a comprehensive overview for students preparing for their presentations.

gerodi
Download Presentation

Understanding Average Effects in Nonlinear Models and Dummy Variables in Economics

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. Economics 105: Statistics Go over GH 22 GH 23 due Monday Individual Oral Presentations … see RAP handout. Dates are Tue April 24th and Thur April 26th in lab. But we can’t fit them all into 75 minutes … so extra sessions to be announced.

  2. Average Effect on Y of a change in X in Nonlinear Models • Consider a change in X1 of ΔX1 • X2 is held constant! • Average effect on Y is difference in pop reg models • Estimate of this pop difference is

  3. Example

  4. Example • What is the average effect of an increase in Age from 30 to 40 years? 40 to 50 years? • 2.03*(40-30) - .02*(1600 – 900) = 20.3 – 14 = 6.3 • 2.03*(50-40) - .02*(2500 – 1600) = 20.3 – 18 = 2.3 • Units?!

  5. http://xkcd.com/985/

  6. Example

  7. Example

  8. Log Functional Forms • Linear-Log • Log-linear • Log-log • Log of a variable means interpretation is a percentage change in the variable • (don’t forget Mark’s pet peeve)

  9. Log Functional Forms • Here’s why: ln(x+x) – ln(x) = • calculus: • Numerically: ln(1.01) = .00995 = .01 • ln(1.10) = .0953 = .10 (sort of)

  10. Linear-Log Functional Form

  11. Linear-Log Functional Form

  12. Log-Linear Functional Form

  13. Log-Linear Functional Form

  14. Log-Log Functional Form

  15. Log-Log Functional Form

  16. Examples

  17. Examples

  18. Examples

  19. Examples

  20. Dummy Variables • A dummy variable is a categorical explanatory variable with two levels: • yes or no, on or off, male or female • coded as 0’s and 1’s • Regression intercepts are different if the variable is significant • Assumes equal slopes for other explanatory variables • If more than two categories, the number of dummy variables included is (number of categories - 1)

  21. Dummy Variable Example (with 2 categories) • E[ GPA | EconMajor = 1] = ? • E[ GPA | EconMajor = 0] = ? • Take the difference to interpret EconMajor

More Related