Rohit Aggarwal

November 20, 2007

Some of the courses I took during the Ph.D. program

Filed under: Econometrics,Research,Teaching — Rohit Aggarwal @ 11:35 pm

Advanced Statistical Methods
Distribution and density functions of random variables , conditional probability and independence, moment generating functions and moments, common families of distributions, multi-parameter exponential family, multiple random variables, change-of-variable techniques, models of convergence, central limit theorem, distribution of order statistics, sufficiency principle, minimal sufficiency, ancillarity, completeness, likelihood principle, point estimation, interval estimation, hypothesis testing, evaluation of estimators and tests.

 

Applied Statistics

Statistics from a data analytic viewpoint incorporating parametric and nonparametric methods, exploratory data analysis, graphical methods, one-sample problems, jackknifing, bootstrapping, robustness, two-sample problems, k-sample problems including one-way ANOVA, randomized block designs, two-way ANOVA, additivity, simple linear regression, multiple linear regression, analysis of covariance, categorical data.

 

Analysis of Experiments

Straight-line regression, multiple regression, regression diagnostics, transformations, dummy variables, one-way and two-way analysis of variance, analysis of covariance, stepwise regression.

 

Applied Multivariate Analysis
Multinormal techniques with applications, topics covered: Hotelling’s T2 test, multivariate analysis of variance, discriminant analysis, principal components, factor analysis, cluster analysis, introduction to and use of SAS computer package.

 

Decision Theory

Game theory, statistical decision, Bayesian statistics

 

Econometrics 2

Review of conditional expectations and basic asymptotic theory, Ordinary least squares and instrumental variables, Generated regressors and specification testing, Generalized method of moments (GMM), Systems of equations, Linear unobserved e®ects panel data models

 

Econometrics 3

I. Basic asymptotic theory

II. General approaches to estimation and testing: M-estimation, Maximum likelihood, Generalized method of moments and Minimum distance

III. Applications of II: Systems of equations, Instrumental variables, Simultaneous equations, Discrete response, Censoring and truncation, Selection models

 

Nonparametric Methods in Econometrics
Density estimation, Nonparametric regression, Some technical issues (edge effects, mixed regressors, etc),  Partially linear regression, General conditional moment restriction models, Specification testing, Nonparametric regression with endogenous regressors (Time permitting)

 

Causal Modeling

The analysis of data to test causal theories, the use of factor analysis to test models of measurement, and the comparison of alternative models is discussed.

 

Research Methods for Operations and Information Management

Linear Programming, Integer Programming, a litte bit of Non-linear Programming

 

Seminar in Operations Management

Dynamic Programming, Applications of Markov Decision Processes

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