Predictive Analytics

Exam number: 6815

Semester: from 1st semester

Duration of the module: One semester

Form of the module (i.e. obligatory, elective etc.): Elective

Frequency of module offer: Summer semester 2019

Prerequisites: It is assumed that participants have a basic knowledge of statistical methods, including linear regression models. No a priori knowledge of discrete choice models is needed.
We use the public domain software package R in computer classes.
The course is taught in the first block of the semester. More advanced models and the implementation in optimization problems for decision making are taught in advanced courses in the second block of the semester.

Applicability of module for other study programmes:
Obligatory or elective in other study programmes. For further information check regulations of the study programme.

Person responsible for module: Prof. Dr. Sven Müller

Name of the professor: Prof. Dr. Sven Müller

Language of teaching: English

ECTS-Credits (based on the workload): 6

Workload and its composition (self-study, contact time):
Contact time (Lecture, tutorial etc.): 60 h; self-study: 120 h

Contact hours (per week in semester): 4

Methods and duration of examination:
tba

Emphasis of the grade for the final grade: Please check regulations of the study programme

Aim of the module (expected learning outcomes and competencies to be acquired):
tba

Contents of the module:
Accurate predictions of the demand and market shares are critical for a wide variety of businesses and public organizations. Examples of applications include: predicting demand for a new product under alternative pricing strategies; designing a business plan for new technology; analyzing the impact of a merger on market shares; forecasting the ridership on a new metropolitan transit service; and analyzing competitive scenarios for introducing a new telecommunication service. To accomplish these tasks, discrete choice analysis provides powerful methodological tools. Based on the modeling of individual behavior, it is used to model in detail the structure of a market, and to predict the impact of various scenarios.
This course undertakes the study of discrete choice models and their applications. It provides participants with the practical tools necessary for applying new discrete choice techniques. By examining actual case studies of discrete choice methods students will be familiarized with problems of data collection, model formulation, testing, and forecasting and will gain hands-on application experience by using readily available software to estimate and test discrete choice models from real databases. The course will emphasize applications of discrete choice methods to strategic and tactical marketing and to policy-related problems in transportation and energy industry.
The course will cover the following topics:
1. Fundamental methodology, e.g. the foundations of individual choice modeling, random utility models, discrete choice models (binary and multinomial);
2. Data collection issues, e.g. choice-based samples, enriched samples, stated preferences surveys, conjoint analysis, panel data;
3. Model design issues, e.g. specification of utility functions, generic and alternative specific variables, joint discrete/continuous models, dynamic choice models;
4. Model estimation issues, e.g. statistical estimation, testing procedures, software packages, estimation with individual and grouped data, Bayesian estimation;
5. Forecasting techniques, e.g. aggregate predictions, sample enumeration, micro-simulation, elasticities, pivot-point predictions and transferability of parameters;
6. Examples and case studies, including marketing (e.g., brand choice), housing (e.g., residential location), telecommunications (e.g., choice of residential telephone service), energy (e.g., appliance type), transportation (e.g., mode of travel).

Teaching and learning methods:
tba

Literature (compulsory reading, recommended literature):
tba

Further information:
Registration in Moodle Viadrina required.