Econometrics

2021/2022

Academic Year

RUS

Instruction in Russian

10

ECTS credits

Delivered by:

Department of Applied Economics

Type:

Compulsory course

When:

3 year, 1-4 module

Instructors

Demidova, Olga

Kramkov, Viacheslav

Maksimov, Andrey G.

Telezhkina, Marina

Teplykh, Grigorii

Программа дисциплины

Аннотация

A solid background in mathematics and elementary statistical theory is a must. Mathematics: a basic understanding of multivariate differential calculus. Statistics: a clear understanding of what is meant by the population quantities and the sampling distribution of an estimator, and of the principles of statistical inference and hypothesis testing. Courses in Economics are useful but not required. No previous knowledge of econometrics is required.

Цель освоения дисциплины

• To develop an understanding of the use of regression analysis for quantifying economic relationships and testing economic theories
• To equip for reading and evaluation of empirical papers in professional journals.
• Special attention will be given to assumptions required for analysis, how plausible these assumptions are in particular real-world applications and what kind of bias appears when these assumptions are not satisfied. We will try to see the intuition behind each method and, in some cases, obtain precise mathematical statements.

Планируемые результаты обучения

• Students are aware of the concept of the simultaneous equations model. Students can perform exogeneity tests.
• Students are aware of the consequences of incorrect model specifications. Students know how to deal with the violation of SLR model assumptions.
• Students are aware of the consequences of non-stationarity and know how to deal with it.
• Students are aware of the signs and consequences of disturbance term’s autocorrelation in LR model. Students can perform autocorrelation tests.
• Students can estimate models with a binary response variable. Students are aware of the properties of these models.
• Students can perform ML estimations. Students are aware of the ML estimators' properties.
• Students can test various hypotheses on the basis of MLR estimations. Students know what multicollinearity is, what consequences it has and how it can be prevented.
• Students can transform response and explanatory variables while estimating regressions. Students know how to interpret estimates in a model with transformed variables.
• Students can use instrumental variables in a regression estimation. Students know about IV properties.
• Students can work with panel data. Students can estimate simple panel regressions. Students can perform tests for choosing between panel models.
• Students can work with time-series data Students can estimate simple time series models and interpret the results.
• Students know about the reasons, consequences and detection of heteroscedasticity. Students can perform heteroscedasticity tests.
• Students know Gauss-Markov theorem applications. Students know and can check the properties of OLS estimators. Students can test various hypotheses on the basis of SLR estimations.
• Students know how to transform categorical variables into a set of dummies avoiding the dummy trap. Students know how to interpret dummy coefficient estimates. Students know how to perform the test for a structural break.
• Students understand why econometrics is useful. Students can distinguish between different data types.

Содержание учебной дисциплины

• Introduction to Econometrics
• Simple Linear Regression Model(SLR)with Non-stochastic Explanatory Variables. OLS estimation
• Multiple Linear Regression Model (MLR): two explanatory variables and k explana- tory variables
• Variables Transformations in Regression Analysis
• Dummy Variables
• Linear Regression Model Specification
• Heteroscedasticity
• Stochastic Explanatory Variables. Measurement Errors. Instrumental Variables
• Simultaneous Equations Models
• Maximum Likelihood Estimation
• Binary Choice Models, Limited Dependent Variable Models
• Modelling with Time Series Data. Dynamic Processes Models
• Autocorrelated disturbance term
• Time Series Econometrics: Nonstationary Time Series
• Panel Data Models
• Causal analysis and other topics

Элементы контроля

• Test 1 (September)
• Mini presentation (Fall)
• Test 2 (October)
• Test 3 (November)
• Final presentation (Fall)
• Test 4 (December)
• Handout for the final presentation (Fall)
• Test 1 (January)
• Test 2 (February)
• Test 3 (March)
• Test 4 (May)
• Mini presentation (Spring)
• Final presentation (Spring)
• Handout for the final presentation (Spring)

Промежуточная аттестация

• 2021/2022 учебный год 2 модуль
  0.2 * Test 1 (September) + 0.2 * Test 2 (October) + 0.2 * Test 4 (December) + 0.05 * Handout for the final presentation (Fall) + 0.2 * Test 3 (November) + 0.1 * Final presentation (Fall) + 0.05 * Mini presentation (Fall)
• 2021/2022 учебный год 4 модуль
  0.15 * Test 1 (January) + 0.15 * Test 2 (February) + 0.15 * Test 3 (March) + 0.25 * Final presentation (Spring) + 0.05 * Handout for the final presentation (Spring) + 0.15 * Test 4 (May) + 0.1 * Mini presentation (Spring)

Список литературы

Рекомендуемая основная литература

• 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
• Dougherty, C. (2016). Introduction to Econometrics. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780199676828

Рекомендуемая дополнительная литература

• Jeffrey M. Wooldridge. (2019). Introductory Econometrics: A Modern Approach, Edition 7. Cengage Learning.

Презентации

• Syllabus