Teaching

The complete list of classes which I have taught is given in my CV but the main courses in my teaching portfolio can be divided into graduate and undergraduate Econometrics, Health Economics and Mathematical Economics.

I have been working on a textbook, titled Constructive Econometrics I. The book is a self-contained one-semester introduction to Econometrics theory and application written for graduate students in Economics and related fields, consisting of 10 chapters.

First, it covers fundamentals of probability theory and statistics deriving most of the stated results and enhancing understanding of the subject with various analytical and computational problems and their solutions. Then the textbook integrates these fundamentals with Econometrics theory and application in the context of linear regression analysis.

Secondly, this book presents both closed-form and simulation-based methods of model estimation. Simulation-based methods, Bayesian and Classical, deal with intractable distributional forms in mostly nonlinear models, which arise in applications when making assumptions consistent with data, departing from basic models and their convenient closed-form solutions. In addition to presenting the philosophical differences between alternative estimation methods, the textbook is rather pragmatic and computational modern showing how these methods may complement each other when solving empirical problems.

Finally, the book puts particular emphasis on building programming skills that help students work independently of statistical packages, typically designed to treat only a narrow range of models and estimation methods. All examples and exercises in this book have code in Matlab, which comes with detailed line-by-line explanations. Like their knowledge of probability theory, statistics and simulation-based methods, programming skills also serve as a basis for more advanced courses in Econometrics and student’s own research.

Outline of the content: Constructive Econometrics I

Chapter 1. Introduction to Probability Theory

1. Introduction
2. Classical, Frequentist and Bayesian
3. Combinatorial Problems
4. Random Events and Probability
5. Axiomatic Construction of Probability Theory
6. Problem Solving

Chapter 2. Random Variables and Distributions

1. Introduction
2. Measurable Functions
3. Discrete and Continuous Variables
4. Moments of Special Distributions
5. Problem Solving

Chapter 3. Multivariate Distributions

1. Bivariate Normal Distribution
2. Marginal and Conditional Distributions
3. Transformation and Convolution
4. Special Multivariate Distributions
5. Problem Solving

Chapter 4. Transformation of Random Variables

1. Introduction
2. Characteristic Functions
3. Moment-Generating Functions
4. Cumulant-Generating Functions
5. Problem Solving

Chapter 5. Convergence of Random Variables

1. Introduction
2. The Bernoulli Scheme
3. Modes of Convergence
4. Laws of Large Numbers
5. Central Limit Theorems
6. Problem Solving

Chapter 6. Classical Inference

1. Introduction
2. Interval Estimation
3. Method of Moments Estimation
4. Maximum Likelihood Estimation
5. The Wald, Likelihood Ratio and Lagrange Multiplier Tests
6. Problem Solving

Chapter 7. Linear Regression

1. Introduction
2. Ordinary Least Squares
3. Efficiency, Consistency and Asymptotic Normality
4. Maximum Likelihood Estimation of the Normal Linear Model
5. Hypothesis Testing in the Normal Linear Model
6. Problem Solving

Chapter 8. Bayesian Inference

1. Introduction
2. Conjugate Prior Analysis and Noninformative Priors
3. Bayesian Estimation and Hypothesis Testing
4. Bayes Factors: Model Comparison and Averaging
5. Connections Between Bayesian and Frequentist Methods
6. Problem Solving

Chapter 9. Simulation Methods

1. Introduction
2. Importance Sampling
3. Method of Simulated Moments
4. Simulated Maximum Likelihood
5. Markov Chain Monte Carlo
6. Problem Solving

Chapter 10. Linear Regression Extensions

1. Introduction
2. Normal Hierarchical Linear Models
3. Semiparametric Regression Models with Smoothness Prior
4. Bayesian Variable Selection via Gibbs Sampling
5. Nonparametric Regression via Variable Selection
6. Problem Solving