Time Series Analysis

Description

Time Series Analysis is a part of the English-language Master’s program in Economic Analysis.
The course will introduce the students to the applied analysis of univariate and multivariate time series data that represent sequential information on, for example, stock, bonds or commodities prices, and other macro- or microdata of interest. Topics covered in the course include unit root, stationarity, spurious regressions, vector autoregression models, cointegration and error-correction models, as well as multivariate volatility models. Applied analysis in the course will be undertaken using a popular R computing language. 

 

Will be interesting for: 

  • analysts of companies and NGO who work with large data sets
  • faculty members and scientists
  • final year undergraduate students    

 

After course completion, you will able to: 

 

  • understand the structure and techniques to analyze and forecast time series data
  • conduct applied analysis and empirical research using time series data

 

Prerequisites: 

Knowledge of descriptive statistics, linear regression analysis and basics of R programming

 

Faculty:
Oleg Nivievskyi is an Assistant Professor at Kyiv School of Economics with Ph.D. in Agricultural Economics and Applied Statistics from University of Goettingen (Germany, 2010). Oleg has founded the Center for Food and Land Use Research at KSE and has more than 18 years of international experience in applied research in agri-food product and factor markets and value chains, as well as in agri-food and regulatory policy impact

 

Language: English

 

Education format: 

The course will be held from January 09 to March 03, 2022. The detailed schedule of classes will be announced later. The course will be conducted in an online format during the day (with a possibility to come to KSE)

 

Price: 10 000 UAH

 

Course outline:
1. Basic Time Series Concepts
2. Univariate Stationary Processes (MA, AR,  ARMA)
3. Vector Auto Regressive Models (VAR)
4. Nonstationarity and Cointegration (CI)
5. Error Correction Models (ECM)
6. Vector Error Correction Models (VECM)
7. Auto Regressive Conditional Heteroskedastic Models ((G)ARCH)

 

Download the detailed course syllabus here