Master
2022/2023
Statistical Analysis II
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type:
Elective course (Population and Development)
Area of studies:
Public Administration
Delivered by:
Department of Applied Economics
Where:
Faculty of Social Sciences
When:
1 year, 3, 4 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Ekaterina Aleksandrova
Master’s programme:
Population and Development
Language:
English
ECTS credits:
6
Contact hours:
48
Course Syllabus
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
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.