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Course Syllabus

2022/2023

Data Analysis and Econometrics: Applications to Environmental Economics

Type: Mago-Lego
Delivered by: School of World Economy
When: 2-4 module
Online hours: 24
Open to: students of one campus
Instructors: Sedat Alatas, Elena Kotyrlo, Natalia Makeeva, Elena V. Semerikova
Language: English
ECTS credits: 9
Contact hours: 52

Course Syllabus

Full SyllabusAsk Question

Abstract

The purpose of the course is not only to give students new skills in both econometric tools and their application to contemporary economic problems, especially in environment problems analysis, but also to study theoretically econometric methods and to review some sections of econometrics on a solid theoretical background. The structure of the course also includes derivation of basic properties of estimation methods but excludes proofs of the most analytically sophisticated results. The main studying purpose of such topics is to clear understanding of econometric ideas, assumptions under which econometric approaches can be applied. At the same time, the students should get skills in reading and understanding of the most advance econometric articles.
Learning Objectives

Learning Objectives

  • The course aims to provide students with: • knowledge on the fundamentals of econometrics and its application • knowledge and proficiency on the use of statistical package R for econometric analysis • practice in conducting data analysis and application of econometric tools in research and analytics • a range of quantitative methods of policy evaluation • examples of their application • problems that arise when evaluating programs and policies • directions of further development in the area of policy evaluation • practical exercises that can help in research and analytic activity
Expected Learning Outcomes

Expected Learning Outcomes

  • Basic knowledge that will help you answer the following questions: • What is policy? • What would be the economic basis for your testimony? • Is there an objective theoretical justification for either policy? • How would you go about calculating the expected gains and losses from these proposed policies?
  • Have an ability to use computer software to perform statistical analysis on data (specifically, STATA).
  • Able to fit a logistic and linear regression model on a given dataset
  • Knows the main determinants of exchange rates, understands the role of expectations
  • Course gives opportunities to students to study how to apply econometrics and statistical software to model economic and financial processes, justify causal relations, find their main determinants and make forecasts.
Course Contents

Course Contents

  • Topic 1.1: OLS
  • Topic 1.2: Model Specification
  • Topic 1.3: Multicollinearity, Heteroskedasticity and Autocorrelation
  • Topic 1.4: Endogeneity and Instrumental Variables
  • Topic 2.1: Maximum Likelihood and Models with Limited Dependent Variables
  • Topic 2.2.: Time series econometrics. Univariate time series
  • Topic 2.3: Time series econometrics. Multivariate time series
  • Topic 2.4: Panel Data Analysis
  • Topic 3.1. Introduction. Randomization.
  • Topic 3.2. Propensity score matching
  • Topic 3.3. Difference-in-difference
  • Topic 3.4. Non-market valuation
  • Topic 3.5. Regression discontinuity.
  • Topic 3.6. Measuring distributional program effects
Assessment Elements

Assessment Elements

  • non-blocking Test on part 1
  • non-blocking Project Assignment
  • non-blocking Test on part 2
  • non-blocking Presentation on Part 3
  • non-blocking Test on Part 3
  • non-blocking Activity score
Interim Assessment

Interim Assessment

  • 2022/2023 3rd module
    0.3 * Test on part 1 + 0.3 * Test on part 2 + 0.4 * Project Assignment
  • 2022/2023 4th module
    0.3 * Presentation on Part 3 + 0.4 * Test on Part 3 + 0.3 * Activity score
Bibliography

Bibliography

Recommended Core Bibliography

  • Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics : Methods and Applications. New York, NY: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=138992
  • Econometric analysis of panel data, Baltagi, B. H., 2005
  • Elhorst, J. P. (DE-588)171025091, (DE-576)131852809. (2014). Spatial econometrics : from cross-sectional data to spatial panels / J. Paul Elhorst. Heidelberg [u.a.]: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.396630170
  • Jeffrey M. Wooldridge. (2019). Introductory Econometrics: A Modern Approach, Edition 7. Cengage Learning.

Recommended Additional Bibliography

  • A guide to modern econometrics, Verbeek, M., 2008
  • Thornton, M., & Hashimzade, N. (2013). Handbook of Research Methods and Applications in Empirical Macroeconomics. Cheltenham: Edward Elgar Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=609451

Authors

  • MAKAROVA EKATERINA ALEKSANDROVNA