Эконометрика (продвинутый уровень)

2024/2025

Статус: Маго-лего

Кто читает: Отдел сопровождения учебного процесса в магистратуре

Когда читается: 2, 3 модуль

Онлайн-часы: 20

Охват аудитории: для своего кампуса

Преподаватели: Гришунин Сергей Вадимович

Язык: русский

Кредиты: 6

Полная версия программы учебной дисциплины

Аннотация

The course will be a core one for the Banking Institute Master program “Financial Analyst”. The course is intended for studying during the first and the second semester of the Master level education. The course is a prerequisite for some both core and specialized courses of the curriculum. Because of study of material of the course, a student should master and be able to prove the basic facts of strict development of classical econometrics. She/he should also know main ideas of univariate and multivariable time-series analysis including Box-Jenkins approach, ARIMA (p, d, q) models, non-stationary time-series, unit root tests, co-integration, VAR and VECM.

Цель освоения дисциплины

The purpose of the course is to give students new and extended skills in both econometric tools and their application to contemporary economic problems. The main studying purpose of such topics is to clear understanding of econometric ideas, assumptions under which econometric approaches can be applied. The student should have skills of application of the indicated tools and methods to researches in problems of Micro-, Macroeconomics and Finance. The student should have knowledge and skills of “Econometrics” (Bachelor level) and a number of mathematical and statistical courses such that “Linear algebra”, “Statistics”, “Probability theory”.

Планируемые результаты обучения

• to estimate linear regression model • to estimate and interpret logarithmic and semi-logarithmic models • to choose correct functional form of the model • to detect and correct for multicollinearity • to detect and treat autocorrelatied disturbances
• to estimate binary choice models • to estimate models with censored and truncated dependent variables • to estimate basic panel data models, • to choose appropriate model specification: fixed effects, random effects • to estimate dynamic panel data models with GMM