TTE6308/6501
Statistical and Econometric Methods II
Instructor:
Fred Mannering, Professor of Civil and Environmental Engineering University of South Florida 4202 E. Fowler Avenue, ENC 3506 Tampa, FL 33620; e-mail: Fred Mannering; links: personal homepage.
Course Summary:
The objective of this course is to solidify students' understanding of the material taught in TTE6307 (Statistical and Econometric Methods) and to extend students' knowledge with the presentation of new model estimation techniques not covered in TTE6307. Specifically, we will undertake detailed assessment of simultaneous equations models (seemingly unrelated regressions and three-stage least squares), generalized extreme value models (nested logit models estimated by full information maximum likelihood), mixed logit models (to account for variations in parameters across the sample population), latent-class models, models with fixed and random effects, zero-inflated count data models, and multivariate models.
Mandatory Prerequisite:
TTE6307 - Statistical and Econometric Methods
Time and Location:
Spring Semester, Wednesdays 2:00-4:45pm, Microsoft Teams and room ENC 2002; ENC2002 Compter Room Pass Key
Textbook:
Washington, S., M. Karlaftis, F. Mannering, P. Anastasopoulos (2020) Statistical and econometric methods for transportation data analysis, Third Edition, Chapman & Hall/CRC, Boca Raton, FL, 478 pages. Best online price
General Downloads and Links:
| Course syllabus | ENC2002 Classroom pass code | TTE6307 website | UQ Transport Data Course | Text Website (Third Edition) |
| Course syllabus | ENC2002 Classroom pass code | TTE6307 website | UQ Transport Data Course | Text Website (Third Edition) |
Overview of Statistical and Econometric Methods I & II:
| The two-course methods-application table | Faces of TTE6308 pioneers |
| The two-course methods-application table | Faces of TTE6308 pioneers |
Research Paper (Due Monday May 6, 9:00am):
| Guidelines for Research Paper | Examples of terms not to be used | Course Review |
| Guidelines for Research Paper | Examples of terms not to be used | Course Review |
Assignments:
| Assignment #0 | Assignment #1 | Assignment #2 | Assignment #3 | Assignment #3A | Assignment #4 | Assignment #5 | Assignment #5a | Assignment #6 | Assignment #6A | Assignment #7 | Assignment #8 | Assignment #9 |
| Assignment #0 | Assignment #1 | Assignment #2 | Assignment #3 | Assignment #3A | Assignment #4 | Assignment #5 | Assignment #5a | Assignment #6 | Assignment #6A | Assignment #7 | Assignment #8 | Assignment #9 |
Data Downloads:
| Assignment #0 Data | Assignment #1 Data | Assignment #2: Class survey data file 2017 (39 variables, 226 observations) | Assignment #3 & #3A Data | Assignment #4 Data | Assignment #5 Data | Assignment #5a Data | Assignment #6A Data | Assignment #7 Data | Assignment #8 Data | Assignment #9 Data |
| Assignment #0 Data | Assignment #1 Data | Assignment #2: Class survey data file 2017 (39 variables, 226 observations) | Assignment #3 & #3A Data | Assignment #4 Data | Assignment #5 Data | Assignment #5a Data | Assignment #6A Data | Assignment #7 Data | Assignment #8 Data | Assignment #9 Data |
Software Command-File Downloads:
| Assignment #0 | Assignment #1 | Assignment #2 | Assignment #3 | Assignment #3A multivariate alternative | Assignment #4 | Assignment #5 | Assignment #5a | Assignment #6 | Assignment #6A | Assignment #7 | Assignment #8 | Assignment #9 |
| Assignment #0 | Assignment #1 | Assignment #2 | Assignment #3 | Assignment #3A multivariate alternative | Assignment #4 | Assignment #5 | Assignment #5a | Assignment #6 | Assignment #6A | Assignment #7 | Assignment #8 | Assignment #9 |
Notes and References:
Support materials 0: | Assignment #0 table template |
Support materials 1: | Assignment #1 presentation slides | ASCE Paper |
Support materials 2:| Simultaneous equation models | SURE speed presentation | 3SLS Paper | Random Parameters SURE Speed Paper |
Support materials 3:| Bivariate/multivariate probit slides | Original HOV project report |
Support materials 4: | Welfare impacts of Copyright law and the music industry | Welfare impacts of Copyright law paper | OTA report |
Support materials 5: | ZIP-ZINB slides | Fingerprint ZINB |
Support materials 6: | Heterogeneity-in-means-variances slides | Heterogeneity-in-means paper | Heterogeneity in means and variances paper |
Support materials 7: | Risk Compensation | Automobile leasing presentation |
Support materials 8: | Unobserved heterogeneity presentation | Unobserved heterogeneity paper |
Support materials 9: | Mixed-Logit with frequencies slides | Example 16.1 |
Support materials 10: | Tobit slides | Bivariate Correlated RPTOBIT | Tobit paper showing zero/non-zero marginal effects |
Support materials 11: | HOPIT slides | HOPIT paper |
Support materials 12: | Temporal instability presentation | Temporal instability paper |
Support materials 13: | USNews presentation | USNews paper |
Support materials 14: | Latent Class Slides | Correlated parameters paper |
Support materials 15: | Machine learning vs. statistics presentation | Machine learning vs. statistics paper |
Support materials 16: | Motorcycle research presentation |
Support materials 17: | 2022 Northwestern presentation |
Support materials 18: | 2023 AI/ML presentation |
Support materials 19: | 2024 UF presentation |