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Winter 2019

Introduction to Econometrics (Syllabus)

This is the first course in econometrics for undergraduate students of engineering. We will quickly review required probability and statistics. Then we dive into the world of empirical analysis. We follow Woodridge’s infamous textbook. We will thoroughly cover regression analysis in this course. We introduce the theory of regression concurrent with many important economic applications.

Lectures:  Lecture 1: Introduction  | Lecture 2: Probability  | Lecture 3: ‌Mathematical Statistics  | Lecture 4: Simple Linear Regression  | Lecture 5: ‌Multiple Regression: Estimation  | Lecture 6: Multiple Regression: Inference  | Lecture 7: Multiple Regression: Asymptotics  | Lecture 8: Multiple Regression: Further Issues  | Lecture 9: Dummy Variables  | Lecture 10: Heteroskedasticity  | Lecture 11: Identification Methods  | Introduction to Stata

Problem Sets:  Pset 1 (q6 data)  |  Pset 2 (q4 data)  |  Pset 3  |  Pset 4 (q6 data)  |  Pset 5 (q5 data)  |  Pset 6 (q5 data)  |  Norouz Project  |  Pset 7 (WAGE1) (WAGE2)  |  Pset 8 (WAGE2)  |  Pset 9 (Vote1)  |  Pset 10  |  Pset 11 (WAGE2)  |  Pset 12  |  Pset 13  | 

Introduction to Modern Macroeconomics I (Syllabus)

This is the first course in the macroeconomics sequence of the master program. Main goal of this course is to introduce dynamic general equilibrium models developed during last 50 years in order to better understand behavior of aggregate economies. Models in this course are based on strong microeconomic foundations, which is in contrast to traditional Keynesians approach. We first introduce centralized economy of Robinson Crusoe, living in an island in planet Mars. This basic model is the workhorse of most modern macroeconomic models. We will then use this base model to study economies in long term, which is known as the realm of economic growth.

Then we show how introduction of markets and prices can deduce the same pareto optimal allocation, the fact that is known as the fundamental welfare theorems. We add government and international trade to our baseline model. Then we introduce money and effects of monetary policy. In order to better understand how monetary policy works (or does not work) we briefly introduce financial intermediation and asset pricing. We also dedicate two sessions to labor markets and theories of equilibrium unemployment. Using the basic insights of our model we will explain the well-known Mundell-Fleming model of monetary policy in an open macro model and the policy trilemma.

Finally, using DSGE models, we show how introduction of stochastic term in our basic models of economy can help us better understand and predict the macroeconomy.

Lectures:  Lecture 1: Introduction |  Lecture 2: Centralized Economy |  Lecture 3: Economic Growth |  Lecture 4: The Decentralized Economy  |  Lecture 5: Government: Expenditures and Public Finances  |  Lecture 6: Fiscal Policy |  Lecture 7: Equilibrium Unemployment Theory |  Lecture 7.1: Equilibrium Models of Labor Market |  Lecture 7.2: Efficiency |  Lecture 7.3: Unemployment Fluctuations |  Lecture 7.4: Mismatch |  Lecture 8: Open Economy | 

Problem Sets:  Pset 1  |  Pset 2  |  Pset 3  |  Pset 4  |  Pset 5  |  Pset 6  |  Pset 7  |  Pset 8  |  Pset 9  |  Pset 10  |  Pset 11  |  Pset 12  |  Pset 13  | 

Exams   Midterm 1  |  Midterm 2  |  Final

“Economics is judged ultimately by how well it helps to understand the world and how well we can help improve it.” - Gary Becker

Introduction to Modern Macroeconomics II (Syllabus)

This is the second course in the macroeconomics part of the master program. In the theoretical part, this course will first review the fundamental welfare theorems, then explores a variety of macroeconomic models in which the welfare theorems do not necessarily hold, including overlapping generations models, equilibrium models with labor market search and matching frictions, economies with sticky prices and sticky wages, and environments in which money facilitates exchange. We will also explore the role of government policy within these models, including fiscal and monetary policy. We will also look at environments with non-convex adjustment costs, such as irreversible investment and fixed costs of changing prices.

Another pillar of this course is quantitative methods in dynamic recursive macroeconomics. The main mathematical tool for dynamic recursive analysis is the "Principle of Optimality" and "Dynamic Programming", which will be thoroughly introduced and used throughout this course. We will use Python as the main programming language.

Lecture Notes:  Equilibrium Models of Labor Market |  Efficiency |  Unemployment Fluctuations |  Mismatch | 

Problem Sets:  Pset 1: OLG and Rational Bubbles  |  Pset 2: Tirole (1985)  |  Pset 3: Pay-As-You-Go Social Secturity  |  Pset4: Principle of Optimality and the Search Theory of Labor Markets  |  Pset5: Neoclassical Growth Model  |  Pset6: Mc Call Search Model  |  Pset7:  | 

Exams   Midterm 1  |  Midterm 2  |  Final

Summer 2018

Social Experiments and Public Policy (slides)

Spring 2018

Applied Econometrics

The goal of this course is to prepare students for using micro-data both for micro and macro applications. I start with estimation methods such as OLS, MLE and GMM then I will cover a number of topics that are important in applied research: Logit, Probit, Hazard models, Cox model, RCTs, IV, DD, RDD, Quintile regression, Propensity Score Matching, linear panel modes and time permitting I will introduce bootstrap methods. Core methods of the course are divided into two categories: structural modeling and reduced form identification methods.


Lectures:  Lecture 1: Introduction  | Lecture 2: Microeconometric Data Structure  | Lecture 3: Estimation of Linear Models  | Lecture 4: Maximum Likelihood Estimation  | Lecture 5: GMM  | Lecture 6: Logit  | Lecture 7: Nested Logit and General Extreme Value  | Lecture 8: Probit  | Lecture 9: Random Utility Models with the Mixed Logit Model  | Lecture 10: Tobit, Selection Models and the Roy Model  | Lecture 11: Econometrics of Field Experiments  | Lecture 12: Machine Learning and Econometrics  | Lecture 13: Duration Analysis  | Lecture 14: Models for Panel Data  | Lecture 15: Cluster Robust Standard Errors  | Lecture 16: ‌Bootstrap Methods

Problem Sets:  Pset 1: Sampling, Linear Regression and Introduction to Stata  |  Pset 2: Monte-Carlo Simulation  |  Pset 3: Maximum Likelihood and GMM  |  Pset 4: Logit (q2 data) (q3 data)  |  Pset 5: Nested Logit and Probit (data)  |  Pset 6: Tobit and Selection Models (data)  |  Pset 7: Tab20 (data)  |  Pset 8: Econometrics of Field Experiments (data)  |  Pset 9: Duration Models (data)  |  Pset 10: Linear Panel Models (data)  |  Pset 11: Panel Models (data)  | 

Final Exam   Questions  |  Data  | 

The 7 Habits of Highly Effective People:  Book  |  Slides  |  Video  |  Wikipedia  |  Summary  |  Stephen R. Covey, The Author

Fall 2017

Topics in Macroeconomics and Labor Market Dynamics

This 5-week course will introduce basic frameworks to study issues at the intersection of Macroeconomics and Labor Economics. We will start the course by introducing frictionless neo-classical model of labor supply and labor demand. Then we present facts in support and against this basic model. We will spend the rest of the course in studding search and matching models in the labor market. Introducing the canonical model of Diamond, Mortensen and Pissarides and its extensions are the main objective of this short course.


Spring 2017

Quantitative Economics