Econometrics (Advanced Level)

Master 2020/2021

Category 'Best Course for Career Development'

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Compulsory course (Applied Economics and Mathematical Methods)

Area of studies: Economics

Delivered by: Department of Economics

Where: St.Petersburg School of Economics and Management

When: 1 year, 2, 3 module

Mode of studies: offline

Instructors: Полякова Евгения Юрьевна, Alexander Muravyev

Master’s programme: Applied Economics and Mathematical Methods

Language: English

ECTS credits: 6

Contact hours: 46

Full Syllabus Ask Question

Abstract

The course is designed for first-year graduate (Master) students following the programs “Finance” and “Applied Economics and Mathematical Methods”. Its main goal is to familiarize the students with advanced methods of econometric research in economics and finance. 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

A review of the classical linear regression model
Key panel data techniques
An overview of the matching models and regression discontinuity designs
Computer exercises using the statistical software package “Stata” are an integral part of the course, which ensures that the students get hands-on experience of analyzing real world data

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
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.
Endogeneity. Instrumental variables methods
CL2.1. Key commands of regression analysis. Hypothesis testing and model diagnostics. 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.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).
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).

Assessment Elements

Four home assignments
Mid-term test
Final exam
The duration of the final exam is two academic hours (80 mins). on-line

Interim Assessment

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

Bibliography

Recommended Core Bibliography

Giovanni Cerulli. (2015). Econometric Evaluation of Socio-Economic Programs. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.adstae.978.3.662.46405.2

Recommended Additional Bibliography

Atanasov, V., & Black, B. (2016). Shock-Based Causal Inference in Corporate Finance and Accounting Research. Critical Finance Review, (2), 207. https://doi.org/10.1561/104.00000036
Bruce E. Hansen. (2013). Econometrics. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C0DB9E1E
Roberts, M. R., & Whited, T. M. (2013). Endogeneity in Empirical Corporate Finance1. Handbook of the Economics of Finance, 493. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.h.eee.finchp.2.a.493.572

Course Syllabus