MKTG 584 B Ð Dynamic Structural Models
Fall
2018
Time & Place:
TBD
Instructor:
Hema Yoganarasimhan
Office: Paccar 481
Course Outline
The goal of the course is to train students to model intertemporal
tradeoffs made by consumers and firms using structural models. In the process,
the course will introduce students to advanced estimation methods, identification,
and problem formulation.
While the focus is on singleagent dynamic models, many of the methods
and some of the papers will cover static games.
The course will
require considerable commitment on the part of students in terms of reading,
understanding many conceptual papers and writing programs. If you are not
willing to make this commitment, this course is not for you.
Class Format
Each class, we will learn one topic. You will be assigned background readings.
The class will consist of a mixture of lecture and class discussion.
Attendance
Students are expected to come to all
classes.
Prerequisites
o
Good understanding of static discrete
choice models.
o
Basic programming skills.
o
Willingness to work and have fun!
Notes
I have written detailed notes for most topics covered in the class.
They are available in PDF format here: Hema_Notes.
Grading
á Assignments
Do the seven assignments and submit them on/before the specified time.
Late submissions will not be graded. This carries 12.5 points per assignment,
with a total of 87.5 points.
á Class participation grade
This comes from two types of class participation:
Class participation counts for a total of 12.5 points.
Assignments
There are seven programing assignments. Links to the assignments are
listed below:
1.
Assignment 1: Data
generation for the Rust bus engine model.
2.
Assignment 2: Estimation
of Rust bus engine using Nested Fixed Point.
3.
Assignment 3: Estimation
of Rust bus engine using the Twostep method.
4.
Assignment 4: Data
generation for the Rust bus engine model with persistent unobservables.
5.
Assignment 5: Estimation
of the Rust bus engine with persistent unobservables using EM & Nested
Fixed Point.
6.
Assignment 6: Estimation
of the Rust bus engine with persistent unobservables using EM & Twostep
method.
7.
Assignment 7: Theoretical
identification proofs.
Notes about the assignments
á You can use any programming language that you are comfortable with.
For each assignment, I will test the correctness of the code submitted using my
dataset or by asking you to generate specific results.
á The code should be simple and clean. You are expected to put detailed
comments in your code for each assignment.
á The instructor will not help in debugging or correcting the code.
á Do not copy code from sources available online. You are welcome to use
these resources to understand how to structure your code, debug it etc., but you are expected to write your
code in the end.
á You are encouraged to discuss the assignments with each other.
However, each person should write his or her own code. If you are not able to
explain your code to me, I will assume it is not yours. In such situations, you
will be automatically given zero points for the assignment.
á Your grade for each assignment will solely depend on the correctness
of the code. If the code produces correct results, you get full points. If it
produces incorrect results, you get zero points. However, if it happens that
the code gives correct answers using incorrect logic or some other flaw in the
system, you will get zero points.
á There is no partial grading. While this may seem harsh, remember that
this is how the publication process works. Incorrect codes and research papers
can significantly harm your career. The goal is to get you to focus on writing
thoughtful, correct code, and get you into the habit of putting in checks in
your code to ensure that it is correct.
FAQ
1. I am not in the marketing department.
Can I still take the course?
Yes, you can. However, please check with the instructor before doing
so.
2. Can I audit the course?
No, auditors are not allowed.
3. What kind of programming skill do I need
for the class?
You are expected to have basic to good programming skills. The class
itself will not cover any programming languages or basics. You need to be
conversant with or willing to learn one good programming language (with an
inbuilt optimizer) to be able to complete the assignments. Depending your
prior knowledge, you could use Stata (or Mata), Matlab,
Python/C in conjunction with Stata etc.
4. Is this a programming class or will we
cover theory too?
This is not a programming
class. We will cover many theoretical aspects of dynamic structural models such
as how to write formal identification proofs and the convergence properties of
estimators. In the end, a good structural model is a good theory model taken to
data and the main goal of these models is to answer interesting substantive
questions. So understanding the theory and the
substantive contribution of the papers discussed is a primary objective of this
class.
5. I have heard a rumor that Prof.
Yoganarasimhan has a policy of only working with students who do well in this
course. Is this true?
It is not a rumor;
it is a fact! I have a very simple policy. If you want to work with me on a
project, or write you a recommendation letter, or serve on your committee, or
be your dissertation advisor, then you have to: 1) Take this course with
grading system 2 and do well in class, and 2) answer my qualification exam
question based on this class and get a high pass. If you are outside the
Marketing department (IS/Economics), these rules still apply.
For more details, please read the ÒDoctoral
Mentoring PolicyÓ document: Hema_doctoral_mentoring_policies.
Class Schedule
CLASS 
DATE 
TOPIC 
ASSIGNMENT DUE 
1 
September 28 
Introduction to Discrete Choice Models 

2 
October 5 (TB rescheduled) 
Introduction to Single Agent Dynamic Models &
Nested Fixed Point 

3 
October 12 
Twostep Methods Ð I 
1 
4 
October 19 
Twostep Methods Ð II 
2 
October 26 
No class 
3 

5 
November 2 
Persistent Unobservables
& Nested Fixed Point with EM 
4 
6 
November 9 
Guest Speaker 

7 
November 16 
Twostep methods with EM 
5 
8 
November 30 
Identification 
6 
9 
December 7 (TB rescheduled) 
MPEC
Methods 
7 
Class Readings
Class 1: Introduction to Discrete Choice Models
á Chapters 15 of Kenneth TrainÕs textbook Discrete Choice Methods with
Simulation available online.
á Stata notes posted on Canvas
á Application Papers
o
Guadagni &
Little, ÒA Logit Model of Brand Choice Calibrated on Scanner Data,Ó Marketing
Science, (1983)
o
Kamakura & Russell, "A Probabilistic
Choice Model for Market Segmentation and Elasticity Structure," Journal of
Marketing Research, (1989)
Class 2: Introduction to Single Agent Dynamic Models & Nested
Fixed Point
á Chapters 1 and 2 of my notes: Hema_Notes
á Rust, "Optimal Replacement of GMC Bus
Engines: An Empirical Model of Harold Zurcher," Econometrica, (1987)
á Application Papers
o
Hendel & Nevo, ÒMeasuring
the Implications of Sales and Consumer Inventory Behavior,Ó Econometric (2006)
o
Song & Chintagunta, ÒA Micromodel of New Product Adoption with
Heterogeneous and ForwardLooking Consumers: Application to the Digital Camera
Category,Ó QME (2003)
o
Erdem, Imai, & Keane, ÒBrand and Quantity Choice Dynamics Under Price
UncertaintyÓ QME (2003)
Classes 3 and 4: Twostep methods
á Hotz & Miller, ÒConditional Choice Probabilities
and the Estimation of Dynamic Models,Ó The Review of Economic Studies (1993)
á Bajari, Benkard, & Levin, ÒEstimating Dynamic Models of
Imperfect Competition,Ó Econometrica (2007)
á
Arcidiacono & Ellickson, ÒPractical Methods for Estimation of Dynamic
Discrete Choice Models,Ó Annual Review of Economics, (2011). (Read
only the parts without persistent unobservables for
this class)
á Notes posted on Canvas
á Application Papers
o
Ellickson & Misra,
ÒSupermarket Pricing Strategies,Ó Marketing Science (2008)
o
Ryan & Tucker, ÒHeterogeneity and the dynamics of technology
adoption,Ó QME (2012)
Class 5: Persistent Unobservables &
Nested Fixed Point with EM
á Chapter 5 of my notes: Hema_Notes
á Arcidiacono & Jones, ÒFinite mixture
distributions, sequential likelihood and the EM algorithm,Ó Econometrica
(2003)
á
Get background
reading on EM algorithm. In this class, you should know what an EM algorithm.
Start with DempsterÕs classic paper from 1977.
á Application Papers
o
Arcidiacono,
ÒAffirmative action in higher education: How do admission and financial
aid rules affect future earnings?Ó Econometrica
(2005)
o
Arcidiacono, Sieg, Sloan, ÒLiving Rationally Under the Volcano? An
Empirical Analysis of Heavy Drinking and SmokingÓ International Economic Review
(2007)
Class 7: Twostep methods with EM
á Chapter 6 of my notes: Hema_Notes
á
Arcidiacono &
Miller, ÒConditional choice probability estimation of dynamic discrete choice
models with unobserved heterogeneity,Ó Econometrica
(2011)
á
Arcidiacono & Ellickson, ÒPractical Methods for Estimation of Dynamic
Discrete Choice Models,Ó Annual Review of Economics, 2011. (Finish
the parts with persistent unobservables for this
class)
á
Application Papers
o Yoganarasimhan, ÒThe Value of Reputation in an Online
Freelance Marketplace,Ó Marketing Science (2013)
o Yoganarasimhan, ÒEstimation of Beauty Contest Auctions,Ó
Marketing Science (2016)
Class 8: Identification
á Chapter 7 of my notes: Hema_Notes
á Magnac & Thesmar,
ÒIdentifying Dynamic Discrete Decision Processes,Ó Econometrica (2002)
á Kasahara
& Shimotsu, ÒNonparametric Identification of
Finite Mixture Models of Dynamic Discrete Choices,Ó Econometrica (2009)
á
Application Papers
o Read the identification proof in the Web
Appendix of Yoganarasimhan, ÒThe Value of Reputation
in an Online Freelance Marketplace,Ó Marketing Science (2013)
o Read the identification proof in the Web
Appendix of Yoganarasimhan, ÒEstimation of Beauty
Contest Auctions,Ó Marketing Science (2016)
Class 9: MPEC Methods
á Chapter 8 of my notes: Hema_Notes
á Su & Judd, ÒConstrained Optimization
Approaches to Estimation of Structural Models,Ó Econometrica (2012)
á Application Papers
o
Berry, Levinsohn,
Pakes, ÒAutomobile Prices in Market EquilibriumÓ Econometrica (1995)
o
Dube, Fox, & Su, ÒImproving the Numerical Performance of Static
and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation,Ó Econometrica (2012)
á Background
Reading:
o Luo,
Pang, Ralph, ÒMathematical Programs with Equilibrium Constraints,Ó Cambridge University
Press (1996)
Some survey papers and/or notes that may
help you.
á
Aguirregabiria & Mira, ÒDynamic discrete choice structural models: A survey,Ó 2010
á
The class notes of Matthew Shum
available at: http://people.hss.caltech.edu/~mshum/gradio/ioclass.html
á
AguirregabiriaÕs class notes available at: http://www.iub.edu/~caepr/visitors/2007/files/2007020103.pdf