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# Time Series and Panel Data Analysis

2019/2020
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс по выбору
Когда читается:
4-й курс, 1, 2 модуль

### Course Syllabus

#### Abstract

Time Series and Panel Data Analysis (intermediate level) is a two-module course designed for fourth year ICEF students. The course is divided into two parts. The first part covers time series theory and methods, while the second part goes over panel data analysis. Students will learn basic theoretical results and how to estimate time series and panel data in practice with the help of computational software. The course is taught in English. Course Pre-requisites: Statistics, Mathematics for Economists, Introduction to Econometrics, Introduction to Economics.

#### Learning Objectives

• introduce the students to the modern methods of time series and panel data analysis
• prepare students for individual work, in particular on their bachelor's theses

#### Expected Learning Outcomes

• Explain specifics of time series data
• construct linear models for time series data and apply the Box-Jenkins procedure
• test data for stationarity and transform non-stationary series into stationary ones.
• model the dynamics of several variables simultaneously, and analyze relations between different time series
• be able to estimate different time series models with the help of statistical software
• construct forecasts for macroeconomic and financial variables
• model dependence in conditional variance of times series data
• estimate the basic models of conditional heteroskedacticity using statistical software
• explain specifics of panel data: when it is used and what flexibility it adds to econometric models
• construct and estimate linear models with unobserved heterogeneous effects
• compute the pooled OLS, fixed effects, and random effects estimators
• compute Arellano-Bond estimator
• construct nonlinear models for panel data, in particular, binary choice models, and estimate those models in practice

#### Course Contents

• Time series: basic concepts
Definition of time series. Introduction of main characteristics of time series (stationarity, ergodicity, autocovariance function, correlogram). Lag operator.
• ARMA models
Autoregressive models. Moving-Average models. Wold decomposition. Moments, stationarity and invertibility conditions. Autoregressive Moving-Average models. Aggregation. ADL models.
• Nonstationary time series
Deviations from stationarity: unit roots, deterministic trends, structural breaks. Tests of stationarity
• Conditional heteroskedasticity
ARCH and GARCH models: introduction, properties, estimation
• Multivariate time series
VAR models: properties and characteristics. Granger causality.
• Estimation and forecasting
Estimation of ARMA and VAR models. Forecasting. Properties of forecasts. HAC variance estimation
• Panel data: Introduction
Introduction to panel data analysis. Advantages of panel data
• Linear Panel Data Models
Fixed effects and random effects. Between, within, and pooled estimators. Estimation and hypothesis testing
• Dynamic Panel Data Models
Dynamic panels. Arellano-Bond estimator.
• Nonlinear panel models
Binary response models with panel data. Logit and probit models of panel data.

• problem sets
• essay
• midterm
• final exam

#### Interim Assessment

• Interim assessment (2 module)
0.15 * essay + 0.5 * final exam + 0.2 * midterm + 0.15 * problem sets

#### Recommended Core Bibliography

• 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