Discrete Choice modeling through theory and practice
Details
Introduction
Human life is full of choices to select from on daily basis. Hence, discrete choice models analyses individual choice behavior and solves problems in many fields such as agriculture, economics, accounting, health, engineering, environmental management, urban planning, tourism and transportation among other fields. For example, discrete choice modeling is used in agriculture to inform on the best technology or innovations that are beneficial to farmers. In terms of health, discrete choice modeling informs on the preference of health and healthcare. In market research, discrete choice modeling can guide product positioning, pricing, product concept testing. This 5 days course will equip participants with skills on how to use databases to estimate and test discrete choice models as well as gain hands-on experience in using discrete choice techniques for practical applications.
Duration
5 days
Outline
Course contents
Module1: Basic statistical terms and concepts
· Introduction to statistical concepts
· Descriptive Statistics
· Inferential statistics
· Research design
· Sampling
Theoretical foundations of discrete choice models: Theories of choice
· Random utility theory
· Lancaster’s theory of characteristics
· Neoclassic economics
Module 2: Introduction to behavior modeling
· Analysis of revealed and stated preferences sampling
· Learning how to use Stata/R software:
· Binary choice models
· Probabilistic choice models
ü Logit model
ü Specification of the Logit/Probit model,
ü Estimation of Logit/Probit parameters, the validation process, and their application.
ü Nested logit
Module 3: Choice with multiple alternatives
· Multinomial logit model
· Ordered probit/ordered logit model
· Specification of the Multinomial Logit/Probit and ordered logit/ordered logit models
· Estimation of Multinomial Logit/Probit and ordered logit/ordered logit parameters, the validation process, and their application.
· Case studies with real data sets,
· Nested logit
Module 4: Data management and analysis
· Data analysis using Stata or R
· Model applications
· Case studies on estimation of binary choice model with real data sets,
· Case studies on estimation of Multinomial Logit/Probit and ordered logit/ordered logit with real data sets,
Module 5: Interpretation and discussion of results
· Case study: Interpretation and discussion of results from real data analyzed during training
Schedules
Weekdays | 09:00 AM — 05:00 PM |
Weekdays | 09:00 AM — 05:00 PM |
No. of Days: | 5 |
Total Hours: | 8 |
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