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Магистерская программа «Анализ больших данных в бизнесе, экономике и обществе»

2018/2019
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс по выбору
Когда читается:
1-й курс, 2, 3 модуль

Course Syllabus

Abstract

The course is designed for first-year graduate (Master) students following the programs “Finance” and “Applied Economics and Mathematical Methods”. In particular, the course accentuates the problem of endogeneity and the ways to address it in the analysis of cross- sectional and panel data. The course is of applied nature: The material is presented, whenever possible, in a non-technical way, examples of empirical studies published in leading international economics and finance journals are discussed, and the lectures are supplemented by exercises in the computer lab.

Learning Objectives

• To familiarize the students with advanced methods of econometric research in economics and finance.

Expected Learning Outcomes

• Know key methods of econometric research, understand the causes and consequences of endogeneity, know the main methods for addressing this problem
• Understand endogeneity as a key issue affecting causal inference; be able to critically examine existing research from this angle
• Be able to apply the methods learnt when conducting own empirical analysis
• Be familiar with and be able to use key capabilities of the statistical package “Stata”, including its programming options (the so-called do-files)
• Understand the limits of interpreting regression results in most settings (the ceteris paribus clause)

Course Contents

• Overview of the classical linear regression model
L1.1. The classical linear regression model. OLS estimation. L1.2. Inference in the CLRM. L1.3. OLS asymptotics. L1.4. Specification and data issues. Reading: Wooldridge (2016), chapters 3-7; Hansen (2017), chapter 4, 7; Lecture notes.
• Introduction to econometric package Stata
Computer lab 1. Introduction to econometric package Stata (6 hours). CL1.1. Basic capabilities of Stata. Basic commands. Do and log files. CL1.2. The grammar of Stata. CL1.3. Creating and changing variables in Stata. Reading: Stata manual (2015); Lecture notes. Computer lab 2. CLRM in Stata (2 hours). CL2.1. Key commands of regression analysis. Hypothesis testing and model diagnostics. Reading: Stata manual (2015); Lecture notes.
• Endogeneity. Instrumental variables methods
L2.1. Mains sources of endogeneity: omitted variables, reversed causality, measurement error. L2.2. The IV method. Tests for instrument validity. The problem of weak instruments. Limitations of the IV methods. Reading: Wooldridge (2016), chapter 9; Hansen (2017), chapter 11. Computer lab 3. Instrumental variables (IV) methods (2 hours). CL3.1. Commands of the IV methods. Diagnostic tests. Reading: Stata manual (2015); Lecture notes.
• Analysis of panel (longitudinal) data
L3.1. Examples of panel data. L3.2. Fixed and random effects models. L3.3. Model diagnostics (the Hausman test, etc.). L3.4. Two-way fixed effects models. L3.5. Endogenous explanatory variables. L3.6. The Hausman-Taylor model. L3.7. Dynamic panel data models. Reading: Wooldridge (2016), chapters 13-14. Computer lab 4. Analysis of panel (longitudinal) data (6 hours). CL4.1. Fixed- and random-effects models in Stata. CL4.2. Model diagnostic (the Hausman test, etc.). CL4.3. The Hausman-Taylor model. CL4.4. Dynamic panel data models. Reading: Stata manual (2015); Lecture notes.
• Estimation of treatment effects. The difference-in-difference estimator
L4.1. Statistical setup. Selection on observables and selection on unobservables. Characterizing selection bias. L4.2. The difference estimators and the DiD. L4.3. Testing the key assumption of the DiD. Reading: Cerulli (2015), chapter 1, 3.4; Roberts and Whited (2013), chapter 4 (стр. 520- 531). Computer lab5. The difference-in-difference estimator (2 hours). CL5.1. Applying the DiD estimator using Stata. Reading: Cerulli (2015), chapter 3.6; Stata manual (2015); Lecture notes.
• Propensity score matching and regression discontinuity models
5.1. Matching models. Treatment effects and necessary identifying assumptions. Propensity score matching. 5.2. Regression discontinuity (RD) models. Sharp and fuzzy regression discontinuity designs. Identification of treatment effects in the sharp RD. Reading: Cerulli (2015), chapter 2.3 and 4.3; Roberts and Whited (2013), chapters 5 (pp. 531-549) and 6 (pp. 549-557). Computer lab 6. Overview of the matching and regression discontinuity models (2 hours). CL6.1. Estimation of matching models in Stata CL6.2. Estimation of regression discontinuity models in Stata Reading: Cerulli (2015), chapters 2.7 and 4.4.2; Stata manual (2015); Lecture notes.

Assessment Elements

• Home assignments
• Mid-term test
• Final exam

Interim Assessment

• Interim assessment (3 module)
0.5 * Final exam + 0.3 * Home assignments + 0.2 * Mid-term test

Recommended Core Bibliography

• Cerulli, G. (2015). Econometric Evaluation of Socio-Economic Programs : Theory and Applications. Heidelberg: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=991264
• Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2006). Introductory econometrics : a modern approach / Jeffrey M. Wooldridge. Mason, Ohio [u.a.]: Thomson/South-Western. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.250894459