Department of Applied Economics

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Regular version of the site

Events

Apr 25 – Apr 26

7th Conference "Applied Econometrics"

All events

Publications

Book

Academic Star Wars: Excellence Initiatives in Global Perspective
In press

Yudkevich Maria, Altbach P. G., Salmi J.

Cambridge: MIT Press, 2023.

Article

The Impact of Carbon Tax and Research Subsidies on Economic Growth in Japan

Besstremyannaya G., Dasher R., Golovan S.

HSE Economic Journal. 2025. Vol. 29. No. 1. P. 72-102.

Book chapter

Science or industry: Improving the quality of the Russian higher education system

Panova A., Slepyh V.

In bk.: Vocation, Technology & Education. Vol. 1. Iss. 4. Shenzhen Polytechnic University, 2024.

Working paper

Living Standards in the USSR during the Interwar Period

Voskoboynikov I.

Economics/EC. WP BRP. Высшая школа экономики, 2023. No. 264.

All publications

109028, Moscow
Pokrovsky blvd. 11,
Room S-527
Phone: (495) 772-95-99 ext.27502, 27503, 27498

Administration

Department Head – Svetlana B. Avdasheva

Deputy Department Head – Liudmila S. Zasimova

Manager – Maxim Shevelev

Faculty of Economic Sciences

Department of Applied Economics

Delivered by:: Department of Applied Economics

Type:: Mago-Lego

When:: 3, 4 module

Instructor

Aleksandrova, Ekaterina

Full Syllabus Ask Question

Abstract

This course is a gentle introduction to modern applied statistics and econometrics. The course is based on the following principle: first, idea and formal description of mathematical concepts are given, second, these concepts are applied to real-world problems. The course has three main chapters: probability theory, statistics, and econometrics. Programming in R will be a red thread through all topics. Usage of R helps to apply statistical techniques to real data. The probability theory’s part is devoted to the most fundamental aspects of statistical analysis. Moreover, during this part we will also cover R programming, therefore, the first part of the course will form foundations for further topics. The statistics’ part explains principles of the basic applied statistical analysis and serves as a bridge between probability theory and the most applied part of the course, econometrics. Econometrics is a collection of mathematical tools which helps to forecast variables, find new dependences and test theories.

Learning Objectives

The goal of this course is to improve students’ skills in the linear regression analysis, to learn how to estimate the model with the binary dependent variable, to learn how to estimate FE and RE panel models, learn how to estimate difference-in-differences model, to make students familiar with the basic tools for testing theories, to make students able to read, interpret and replicate the results of published papers using standard computer packages and real-world data

Expected Learning Outcomes

Be able to use theoretical notions, concepts and interpret the models with Panel Data.
to be able to detect heteroscedasticity problem
to learn how to estimate the model with the binary variable
Explain the difference between fixed-effect, random-effects, and first-difference models; the parallel trends assumption
Be able to address endogeneity problems
Know properties of maximum likelihood estimates.
be able to identify cases when it is possible to use IV regression models
be able to estimate the IV regression model
be able to define and use the maximum likelihood estimation approach
be able to apply difference-in-differences model
be able to interpret the difference-in-differences model
be able to identify a strong and a weak instrument
to be able to test for heteroskedasticity
to be able to measure different and same constructs in multiple dependent variable case
to be able to analyze and estimate Panel Data models on real data

Course Contents

Heteroscedasticity
Endogeneity. Instrumental variables method. 2SLS
Binary dependent variables. Logit and probit models
Maximum Likelihood Estimation
Multiple dependent variables
Panel Data Models
Difference-in-Differences

Assessment Elements

Test 1
Test 2
Exam

Interim Assessment

2022/2023 4th module
0.3 * Test 2 + 0.4 * Exam + 0.3 * Test 1

Bibliography

Recommended Core Bibliography

Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9780521848053
Introductory econometrics : a modern approach, Wooldridge, J. M., 2009
Verbeek, M. (2017). A Guide to Modern Econometrics (Vol. 5th edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639496
Zhang, Y. (2007). Fundamentals of Biostatistics (6th ed.). Bernard Rosner. The American Statistician, 183. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.bes.amstat.v61y2007mmayp183.183

Recommended Additional Bibliography

Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics : An Empiricist’s Companion. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=329761
Stanton A. Glantz. (2012). Primer of Biostatistics, Seventh Edition. McGraw-Hill Education / Medical.

Authors

Шевелев Максим Борисович
ALEKSANDROVA EKATERINA ALEKSANDROVNA

Statistical Analysis II