Spring 2017
Course Listings (PHD)
= Cancelled |
= New Class Added |
= Professor Change |
= Rescheduled (day/time change) |
Accounting/Taxation
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ACCT-GB.4320 Empirical Research in Financial Accounting III (3)
Course Description:
This course explores analytical models both in the financial and in the managerial accounting literature. The format is highly interactive; students study assigned papers in depth and present them in class. Analytical research design issues are emphasized. The linkages between the analytical models and the testing of their implications are elucidated. In this context, occasional empirical papers that test well-articulated analytical models' implications are also discussed.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
|
01/30-05/08 |
Lev,B |
PhD students only |
Economics
-
Course Description:
This course is intended to develop the toolbox of PhD students intending to pursue research in strategy (or other business related fields). It focuses on the set of tools that are provided by the discipline of economics, hence a focus on models of oligopoly and contract theory, and a focus on empirical tools such as the measurement and identification of treatment effects and causal inference.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
R
10:00 am
- 12:00 pm
|
02/02-05/04 |
Moser,P |
PhD students only |
Finance
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Course Description:
This is the third course in the theory of financial decision making. The first half of this course deals with issues in corporate finance. Topics include agency theory, signaling and asymmetric information models, taxes, dividends, and capital structure. The second half of the course focuses on the pricing of options, futures, and other derivative securities instruments.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
FR
1:00 pm
- 4:00 pm
|
01/30-05/08 |
Gabaix,X/Schnabl,P |
PhD students only |
-
Course Description:
This course is an introduction to empirical research in asset pricing. Topics include tests of asset pricing models, return predictability in the time-series and the cross-section, empirical studies of asset market imperfections, and studies of individual and professional investor behavior. The aim is to familiarize students with essential econometric methods and with important empirical facts and areas of current research interest.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
T
09:00 am
- 11:50 am
|
01/31-05/02 |
Koijen,R |
PhD students only |
-
Course Description:
The class covers advanced topics on the interaction of macroeconomics, monetary economics, banking, and international finance. The first half of the class (section 1 and 2) will cover the most important building blocks of macro-finance. The second half of the class will discuss topics for ongoing and future research.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
R
09:00 am
- 12:00 pm
|
02/02-05/04 |
Philippon,T |
PhD students only |
For more courses that count toward Finance click here.
Information Systems
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Course Description:
This course introduces students to scientific paradigms and research perspectives related to the economics of information technologies. Topics in 2012 include information goods, piracy, digital rights management, network economics, sponsored search auctions, user-generated content, contagion in networks, technological innovation, IT productivity, the digital commons and online privacy.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
M
5:00 pm
- 8:00 pm
|
01/30-05/08 |
Sundararajan,A |
PhD students only |
-
Course Description:
In this course we will take a deep dive into selected topics in data science. The focus will be two-fold. First, we will read textbook segments, classic papers, and new research, with the goal of understanding research in data science. Second, we will study the actual practical application of data science methods to extract knowledge from large-scale data. We will cover topics such as machine learning, data mining, information retrieval, text classification, sentiment analysis, similarity analysis, network analysis, graphical models, Bayesian models, topic models, model evaluation, crowd-sourcing and micro-outsourcing, massive-scale data processing, reducing data for analytic purposes, and more. The selection of which topics are covered in a particular semester will be based on: (i) the current research and business environments, (ii) the research interests of the IS faculty, and (iii) the interests of the students in that semester. We also will discuss applications that are of current interest, such as recommender systems, social-network marketing, online advertising, Mechanical Turking, and more.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
R
09:20 am
- 12:20 pm
|
02/02-05/04 |
Provost,F |
PhD students only |
Interarea
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Course Description:
This is an intermediate level, Ph.D. course in the area of Applied Econometrics dealing with Panel Data. The range of topics covered in the course will span a large part of econometrics generally, though we are particularly interested in those techniques as they are adapted to the analysis of 'panel' or 'longitudinal' data sets. Topics to be studied include specification, estimation, and inference in the context of models that include individual (firm, person, etc.) effects. We will begin with a development of the standard linear regression model, then extend it to panel data settings involving 'fixed' and 'random' effects. The asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models will be reviewed or developed as we proceed.. We will then turn to instrumental variables, maximum likelihood, generalized method of moments (GMM), and two step estimation methods. The linear model will be extended to dynamic models and recently developed GMM and instrumental variables techniques. The classical methods of maximum likelihood and GMM and Bayesian methods, expecially MCMC techniques, are applied to models with individual effects. The last third of the course will focus on nonlinear models. Theoretical developments will focus on heterogeneity in models including random parameter variation, latent class (finite mixture) and 'mixed' and hierarchical models. We will also visit the theory for techniques for optimization in the setting of nonlinear models. We will consider numerous applications from the literature, including static and dynamic regression models, heterogeneous parameters models (e.g., Fama-Macbeth), random parameter variation, and specific nonlinear models such as binary and multinomial choice and models for count data.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
TR
12:30 pm
- 2:00 pm
|
01/31-05/04 |
Greene,W |
PhD students only |
Management and Organizations
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MGMT-GB.3182 Organizational Behavior Advanced PhD Seminar (1.5)
Course Description:
In the field of organization science, much has been written about levels of analysis-individual, group, and organization. In between individual and group is dyad, which is almost never discussed in these terms. This seems odd because the dyad is a key building block of organizations, and, indeed, there has been a growing trend toward relational perspectives on organizations. In social psychology, the relationship took a backseat to the individual during the cognitive revolution of the 1960s/70s, and history risks repeating itself with the recent advance of neuroscience. This too is puzzling as relationships are essential to social life. A countervailing force in social psychology has been the rise of research on close relationships, or relationship science. In this course, we will stitch together organizational research that has a relational component and psychological research on close relationships and see what novel insights we can generate
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
|
01/30-03/08 |
Leslie,L |
PhD students only |
-
Course Description:
Organizations operate in dynamic environments. This course introduces doctoral students to the principal theoretical perspectives and empirical findings used to explain relationships among environments, organizational strategies, designs, and performance. Students are expected to develop expertise in the analysis of environments and organizations from several theoretical perspectives, such as resource dependence theory, institutional theory, organizational ecology, and industrial organization economics. The seminar stresses the competitive and mutual dimensions of environments that propel managers to enact business, corporate, and collective strategies, structures, processes, and systems to enhance their firms' effectiveness. Both theoretical and empirical research are examined to illustrate how different theoretical perspectives require different empirical research methodologies.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
|
01/30-05/08 |
Bechky,B |
PhD students only |
-
Course Description:
The field of Strategy is motivated by a simple question: What allows certain firms to earn positive economic profits while others deliver negative returns? It offers a set of complicated answers: differences in industry structure, internal capabilities, superior managerial decision making, vertical and horizontal scope and so on. In the past 3 decades, Strategy has emerged as an important area of study in Management. The Business Policy and Strategy division now boasts of the largest membership in all of AOM. In practice, the field of Strategy is the only area that speaks exclusively to the highest level of corporations- the leaders, the CEOs, and the movers and shakers of the modern firm. It transcends functional areas such as finance or marketing, as it brings functional knowledge to bear on the most critical issues faced by the firm's key decision makers. While there is a strong degree of core consensus among scholars, Strategy is a young discipline with many unresolved theoretical puzzles and empirical challenges. Far from being obstacles, these gaps present attractive and ample opportunities for fledgling scholars to make a mark. Whether you aspire to contribute directly to the scholarship of Strategy or are simply curious about how Strategy may relate to your area (whether it be Information Systems, Marketing or Public Policy), this course offers an overview of classic concepts and ideas and introduce you to current research in Strategy.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
|
01/30-05/08 |
Eggers,J |
PhD students only |
Marketing
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Course Description:
The first part of this course covers communication and public policy issues in consumer behavior. It examines recent theory and research bearing on how communications and situational factors affect consumer behavior. In the process, students also become familiar with some related public policy issues, including deceptive and corrective advertising, and trademark-related matters. The second part of the course introduces students to behavioral decision research. Topics covered include judgment under uncertainty, risk taking, and conflicting values.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
|
01/30-05/08 |
Morwitz,V |
PhD students only |
-
Course Description:
This course is intended as an introduction for PhD students who want to design and analyze behavioral experiments. The course has three objectives: to learn how to test research ideas with rigorous, unconfounded experimental designs, to learn how to analyze these designs (using SAS), and to learn how to interpret and evaluate experimental work by other researchers. The topics covered include between subjects, within-subject, and mixed designs, factorial designs, and analysis of covariance. Please keep in mind that this course is not a statistics course. While we will cover some of the calculations behind the analyses, the emphasis is on learning to use the techniques and interpreting applications.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
|
01/30-05/08 |
Meyvis,T |
PhD students only |
Operations Management
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Course Description:
Provides advanced doctoral students with exposure to the spectrum of academic-style research pursued by corporate entities, towards gaining an appreciation of the similarities and differences between inquiry undertaken by industry labs/research groups and the corresponding work done within a university setting. The internship provides an opportunity for students to put theory into practice. Students registered for the course will be required to collaborate with a suitably identified industry partner, often in the form of a short internship. Internships are closely supervised by a Stern faculty member, and students will be expected to submit a research paper that summarizes the outputs of the collaboration.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
|
01/30-05/08 |
Cohen,M |
PhD only |
-
Course Description:
This is an intermediate-level graduate course on discrete choice models and their application to problems in operations and marketing. Choice-based demand models have recently become very popular within the operations and revenue management areas as a way to capture interactions between products. They have also been extensively studied within marketing and econometrics. Recognizing the widespread use of choice models, this course is designed to provide a ground- up introduction to choice models, so that students develop not only the ability to identify the situations where choice models can be applied, but also the required tools to build their own models and estimation and optimization techniques for the problem at hand.
The course emphasizes mathematical rigor and hands-on implementation. All the methods studied in the course will be analyzed to establish their statistical and computational properties, so that the students can determine the "right" methods to use for the problem and the data set at hand. The course also requires students to complete several programming assignments that require them to implement the methods they learn in the class.
The course is divided into three parts. The first part is dedicated to the de- velopment of choice models from first principles rooted in rationality and utility theory. The development will stitch together-through a unified framework-the diverse approaches taken in the literature over the past several decades. The aim is to provide a solid understanding of the strengths and limitation of the various model classes.
The second part focuses on building various techniques to estimate the param- eters of the models from commonly available choice data. The techniques will be analyzed to derive necessary and sufficient conditions the data should satisfy to ensure identification of model parameters.
The third part will deal with solving decision problems using choice models. The particular focus will be on the assortment and pricing problems. These de- cision problems will be studied in the context of retail assortment planning and yiled management problems.
The detailed syllabus is available at http://pages.stern.nyu.edu/~sjagabat/Syllabus_ChoiceModelsInOps.pdf
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
FR
2:00 pm
- 5:00 pm
|
02/03-05/05 |
Jagabathula,S |
PhD students only |
Statistics
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Course Description:
Frequency Domain Time Series is an advanced course on foundations and applications of time series. Methods involving periodograms and spectral densities are emphasized. Linear filtering and spectral representations (stochastic integrals)for stationary time series are used as unifying themes. The second half of the course considers GARCH models, fractals, long memory and fractional cointegration. Again, emphasis is on insights gained from the frequency domain viewpoint.
The mathematics used in the course is Fourier analysis, a useful tool for all technically-oriented students. All mathematical results are presented in a self-contained manner.
The course grades are based on homework assignments (70% of the grade)and an in-class open-book final exam (30% of the grade). Homeworks can be re-submitted for further credit, at any time.
There is a clear need for advanced students in statistics, finance and economics to have a deep understanding of time series in the frequency domain. Increasingly, frequency domain methods and models are being used by practitioners.
Section |
Meeting Times |
Dates |
Instructor |
Notes |
20 |
T
1:00 pm
- 4:00 pm
|
01/31-05/09 |
Hurvich,C |
by permission for non-PhD |
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