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Магистратура 2020/2021

Эконометрика (продвинутый уровень)

Статус: Курс обязательный (Финансовая экономика)
Направление: 38.04.01. Экономика
Когда читается: 1-й курс, 1-4 модуль
Формат изучения: без онлайн-курса
Прогр. обучения: Финансовая экономика
Язык: английский
Кредиты: 8
Контактные часы: 90

Course Syllabus


The main objectives of the first part of Econometrics are to introduce students to basic econometric techniques and to prepare them to do their own applied work. Students are encouraged to think of the course as a preparation toward their thesis research project. The course is taught in English. The purpose of the course is not only to develop new skills in econometric tools and their application to contemporary economic problems, especially in financial economics, but also to study theoretically econometric methods and to review some sections of econometrics on a solid theoretical background. In the first module of the semester, we cover fundamental topics in time series analysis, such as ARMA models, non-stationary time-series, Brownian motion and unit root tests, cointegration, VAR and VECM. During the second module students study binary choice models (logit, probit, tobit, Heckman) and basic concepts of panel data analysis (pooled regression, fixed and random effects, dynamic panel models, binary choice panel data). All topics are accompanied with real data examples in R, Stata, EViews, and JMulTi. The course is taught in English. Course Pre-requisites: Calculus, Probability Theory and Statistics at an intermediate level. Completion of Mathematics for Economics and Finance course is required. Successful completion of Econometrics will allow students to take the Financial Econometrics class.
Learning Objectives

Learning Objectives

  • During the course students will be introduced to modern approaches in analysing economic and financial data
  • Upon completion of the course students should be:familiar with the basic tools available to economists for testing theories, estimating the parameters of economic relationships in financial markets and forecasting financial and macroeconomic variables;
  • able to read, interpret and replicate the results of published papers in economics and finance using standard computer packages and real-world data
Expected Learning Outcomes

Expected Learning Outcomes

  • Explain conditional expectations and their relationship to the population regression function
  • Explain main notions of econometrics
  • Be able to relate simple and partial correlation coefficients via the regression anatomy formula
  • Calculate confidence interval
  • Derive robust standard errors
  • Apply econometric techniques to real economic situations
  • Test for heteroskedasticity
  • Address endogeneity problems
  • Outline the conditions under which nonlinear estimators are consistently estimated
  • Explain specifics of working with time series data
  • Test time series for various deviations from stationarity and transform trend- and unit-root stationary processes into stationary ones
  • Derive asymptotic distribution of estimators when the standard regularity conditions do not hold
  • Construct linear models for time series data and apply the Box-Jenkins procedure.
  • Model the dynamics of several variables simultaneously, and analyze structural and reduced-form relations between different time series
  • Use advantage of the panel data, correctly use these models
  • Use discrete choice model, correctly use these models
  • Outline the problem related to estimation of Discrete choice models
  • Use models from Optional topics
Course Contents

Course Contents

  • Introduction to Econometrics
    The FAQS of economics research. Causal Relationships. Experiments and Quasi-experiments. Identification and Statistical Inference. The Selection Problem. Cross Section and Longitudinal Data
  • The Simple Regression Model.
    Derivation of OLS estimates. Mechanics and Properties. Units of measurement and functional form. Unbiasedness and efficiency
  • Multi-variate Regression Analysis
    Motivation: multiple sources of variation. Mechanics and interpretation of OLS. The “partialling out" interpretation and linear projections. Unbiasedness and efficiency: the Gauss-Markov Theorem
  • Inference in the Multi-variate Regression Model
    Sampling distributions of the OLS estimators. Testing Hypothesis. Confidence Intervals.
  • Asymptotic Properties of OLS
    Consistency, asymptotic normality and asymptotic efficiency. The LM test. Sources of endogeneity: omitted variables, measurement error, simultaneity
  • Further Issues in OLS estimation
    Data scaling and beta scores. Quadratic and interaction terms. Prediction. Dummy Variables. Proxy variables. Missing data and outliers.
  • Heteroscedasticity
    Consequences for OLS. Heteroscedasticity-robust inference. BreuschPagan and White tests. WLS and FGLS.
  • Instrumental Variables and 2SLS
    Instruments as a solution to endogeneity. Reduced form equations. Exclusion restrictions. Rank condition. Two-stage least squares and GMM. Consistency and other asymptotic properties. Potential pitfalls. Local Average Treatment Effects.
  • Maximum Likelihood
    ML Estimators. Likelihood ratio, Wald and LM tests. GLS and 2SLS as ML estimators
  • Review of main characteristics of time series
    Time series basics. Main characteristics of time series. Autocorrelation and partial autocorrelation. ARMA models: estimation and forecasting
  • Nonstationary time series. Spurious regressions
    Stationarity and nonstationarity. Random walks. Difference-stationarity and trend-stationarity. Spurious regressions.
  • Volatility modelling
    Volatility clustering, (G)ARCH, extensions to GARCH.
  • Unit roots and tests for stationarity. Structural breaks. ARIMA models. Forecasting
    Brownian motion. Testing for stationarity. (Augmented) Dickey-Fuller tests. Other tests of nonstationarity. Parameter instability and structural changes. Testing for structural change. Structural changes and unit roots. ARIMA models. Long memory processes. Forecasting.
  • Vector autoregressive models
    Vector autoregressions. Granger causality. Cointegration. Johanssen test on cointegration. Vector error correction models
  • Static and dynamic panel data
    Notion of panel data, pooled regression, fixed and random effects, “within” estimator, random effect estimator, “between” estimator, specification tests. Dynamic panel data. Arellano-Bond estimator
  • Discrete choice models
    Binary, multiple, and ordered discrete models. Properties of binary data, problems with linear regression, logit, probit, goodness of fit. Censored and truncated observations, Tobit models. Sample selection problem, Heckman models. Estimating treatment effects.
  • Discrete choice models in panel data
    Binary models with fixed and random effects, Tobit, discrete dynamic models with panel data, incomplete panel.
  • Optional topics
    Could be chosen from the set of topics: non-parametric and semiparametric methods; simulation-based estimation; shrinkage methods
Assessment Elements

Assessment Elements

  • non-blocking Homework Assignments
  • non-blocking Midterm test (Part I)
    Для студентов она дистанте экзамен проводится в письменной форме с использованием синхронного прокторинга. Экзамен проводится на платформе https://hse.student.examus.net). К экзамену необходимо подключиться за 10 минут до начала. Проверку настроек компьютера необходимо провести заранее, чтобы в случае возникших проблем у вас было время для обращения в службу техподдержки и устранения неполадок. Компьютер студента должен удовлетворять требованиям: 1. Стационарный компьютер или ноутбук (мобильные устройства не поддерживаются); 2. Операционная система Windows (версии 7, 8, 8.1, 10) или Mac OS X Yosemite 10.10 и выше; 3. Интернет-браузер Google Chrome последней на момент сдачи экзамена версии (для проверки и обновления версии браузера используйте ссылку chrome://help/); 4. Наличие исправной и включенной веб-камеры (включая встроенные в ноутбуки); 5. Наличие исправного и включенного микрофона (включая встроенные в ноутбуки); 6. Наличие постоянного интернет-соединения со скоростью передачи данных от пользователя не ниже 1 Мбит/сек; 7. Ваш компьютер должен успешно проходить проверку. Проверка доступна только после авторизации. Для доступа к экзамену требуется документ удостоверяющий личность. Его в развернутом виде необходимо будет сфотографировать на камеру после входа на платформу «Экзамус». Также вы должны медленно и плавно продемонстрировать на камеру рабочее место и помещение, в котором Вы пишете экзамен, а также чистые листы для написания экзамена (с двух сторон). Это необходимо для получения чёткого изображения. Во время экзамена запрещается пользоваться любыми материалами (в бумажном / электронном виде), использовать телефон или любые другие устройства (любые функции), открывать на экране посторонние вкладки. В случае выявления факта неприемлемого поведения на экзамене (например, списывание) результат экзамена будет аннулирован, а к студенту будут применены предусмотренные нормативными документами меры дисциплинарного характера вплоть до исключения из НИУ ВШЭ. Если возникают ситуации, когда студент внезапно отключается по любым причинам (камера отключилась, компьютер выключился и др.) или отходит от своего рабочего места на какое-то время, или студент показал неожиданно высокий результат, или будут обнаружены подозрительные действия во время экзамена, будет просмотрена видеозапись выполнения экзамена этим студентом и при необходимости студент будет приглашен на онлайн-собеседование с преподавателем. Об этом студент будет проинформирован заранее в индивидуальном порядке. Во время выполнения задания, не завершайте Интернет-соединения и не отключайте камеры и микрофона. Во время экзамена ведется аудио- и видео-запись. Процедура пересдачи проводится в соответствии с нормативными документами НИУ ВШЭ.
  • blocking Written exam (part I)
  • blocking written final exam (Part II)
  • non-blocking Problem sets I
  • non-blocking midterm test (part II)
  • non-blocking Problem sets II
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.16 * Homework Assignments + 0.29 * Midterm test (Part I) + 0.55 * Written exam (part I)
  • Interim assessment (4 module)
    0.5 * Interim assessment (2 module) + 0.1 * midterm test (part II) + 0.05 * Problem sets I + 0.05 * Problem sets II + 0.3 * written final exam (Part II)


Recommended Core Bibliography

  • Analysis of financial time series, Tsay, R. S., 2005
  • 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
  • Hamilton, J. D. . (DE-588)122825950, (DE-576)271889950. (1994). Time series analysis / James D. Hamilton. Princeton, NJ: Princeton Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.038453134
  • Verbeek, M. (2004). A Guide to Modern Econometrics (Vol. 2nd ed). Southern Gate, Chichester, West Sussex, England: John Wiley and Sons, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=108185
  • Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2006). Introductory econometrics : a modern approach / Jeffrey M. Wooldridge. Mason, Ohio [u.a.]: Thomson/South-Western. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.250894459

Recommended Additional 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
  • Enders, W. (2015). Applied Econometric Time Series (Vol. Fourth edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639192
  • Greene, W. H. (2015). Econometric analysis. Slovenia, Europe: Prentice-Hall International. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.1BF5A5CA
  • Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=145686
  • Morgan, S. L., & Winship, C. (2007). Counterfactuals and Causal Inference : Methods and Principles for Social Research. New York: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=206937
  • Peter Kennedy. (2003). A Guide to Econometrics, 5th Edition. The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.mtp.titles.026261183x
  • Ruud, P. A. (2000). An Introduction to Classical Econometric Theory. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780195111644
  • Tsay, R. S. (2002). Analysis of Financial Time Series : Financial Econometrics. New York: John Wiley & Sons, Inc. [US]. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=87319
  • Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2010). Econometric analysis of cross section and panel data / Jeffrey M. Wooldridge. Cambridge, Mass. [u.a.]: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.263114414
  • Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, Mass: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=78079