Introduction to Time Series

COURSE OBJECTIVE
This course introduces students to time series analysis and dynamic econometric models.

COURSE CONTENT
This course covers both theoretical and practical aspects of time series econometrics including the analysis of stationary and non-stationary stochastic processes in economics and finance.

The students are introduced to autoregressive moving average (ARMA) models, autoregressive distributed lag (ADL) models, and error correction models (ECM). Furthermore, the course provides both theoretical and practical insight into parameter estimation in time-series and the use of these models for forecasting, testing for Granger causality, and performing policy analysis using impulse response functions.

Finally, the students become familiar with the fundamental problem of spurious regression in time-series analysis. We find a solution to this problem by taking a journey into the theory and practice behind unit-root tests, cointegration tests and error-correction representation theorems.

TEACHING METHODS
Lectures and practical classes. During practical classes time will be made for discussing exercises.

TYPE OF ASSESSMENT
Final exam and group assignment – Individual assessment.

ENTRY REQUIREMENTS
None.

RECOMMENDED BACKGROUND KNOWLEDGE
This course builds on the foundations laid either in the sequence of courses in Quantitative Methods (in the Economics programme) or in that of `Statistics` and `Business Mathematics` (in the Business Administration programme). It assumes some familiarity with probabily and statistics. This material corresponds more or less to Part I (Chapters1-3) in Stock & Watson, and students are recommended to refresh their memory on this prior to the first lecture.

TARGET AUDIENCE
This course in the minor Applied Econometrics is targeted at both econometrics and non-econometrics students that have knowledge of basic mathematics, probability and statistics.