2013/2014  BA-HA_E181  Applied Time Series Analysis and Forecasting

English Title

Applied Time Series Analysis and Forecasting

Course information
Language	English
Exam ECTS	7.5 ECTS
Type	Elective
Level	Bachelor
Duration	One Semester
Course period	Autumn Wedensday 12.30-15.10, week36-41,43-48
Time Table	Please see course schedule at e-Campus
Max. participants	30
Study board	Study Board for BSc in Economics and Business Administration
Course coordinator
Natalia Khorunzhina - Department of Economics (ECON)
Administration: Ida Lyngby - il.eco@cbs.dk
Main academic disciplines
Economics, macro economics and managerial economics
Last updated on 05-08-2013

Learning objectives

Upon the end of the course, the students will be able to: Understand various important concepts of forecasting in the areas of economics and business, Understand different approaches for modeling trend, seasonality and persistence, Use the analytical tools that econometricians employ to analyze data Tailor-make models for their applications and use them to produce forecasts in economics and business, Use packaged computer programs for the forecasting purpose, and Complete basic programming tasks using programming language R.

Course prerequisites

Forecasting of time series requires the use of econometrics and econometric software. The course assumes familiarity and completed bachelor courses in economics and basic econometrics. However, the necessary econometrics will be reviewed.

Examination

Individual home assignment: Examination form Home assignment - written product Individual or group exam Individual Size of written product Max. 15 pages Assignment type Written assignment Duration Written product to be submitted on specified date and time. Grading scale 7-step scale Examiner(s) One internal examiner Exam period Winter Term and December/January Make-up exam/re-exam Same examination form as the ordinary exam

Course content and structure

Accurate forecasting of future events and their outcomes is a crucial input into a successful business or economic planning process. Forecasting is used to answer important questions, such as: How much profit will the business make? How much demand will there be for a product or service? How much will it cost to produce the product or offer the service? How much money will the company need to borrow? When and how will borrowed funds be repaid? Businesses must understand and use forecasting in order to answer these important questions. This course provides an introduction to the application of various forecasting techniques. This course aims to introduce quantitative methods and techniques for time series modeling, analysis, and forecasting.  Emphasis will also be put on the applications in economic and business related areas.  Computing is an integral part of this course, therefore all students are expected to do data analysis, modeling and forecasting with computer programming software. The focus of the course is to use past data to predict the future.  The key concept is that there is an underlying process that gives rise to the data.  Using statistical properties of that process, we can develop forecasts. Forecasting methodology will be presented in a lecture format in the first part of each class meeting. The application, however, is the centerpiece of the presentation. In the second part of the class meeting, students will work on in-class applications.  We will start with simple models that are widely used in business and progress to modern methods that are used by professional forecasters and economists. We will be studying basic components of time-series data, such as trend, seasonal, and cyclical components, as well as basic model specification techniques, such as moving average and auto regressions, used in the forecasting of time-series.  All forecasting methods will be illustrated with detailed real world applications designed to mimic typical forecasting situations. The course uses applications not simply to illustrate the methods but also drive home an important lesson, the limitations of forecasting, by presenting truly realistic examples in which not everything works perfectly! Examples of the applications include, but not limited to, forecasting retail sales, hotel occupancy, fishery output, consumer loan requests, predicting expansion of fast food chain stores, modeling and forecasting macroeconomic activity indices such as Gross Domestic Product, unemployment and inflation, modeling development of a small open economy, forecasting New York Stock Exchange index and currency exchange rates and many other applications. The course’s development of personal competences:  During the course, students will develop theoretical, empirical and programming skills necessary for handling the time series analysis, models and forecasts.

Teaching methods

Lectures and exercises in computer labs.

Student workload

SAT 225 hours

Expected literature

Required: Diebold, F. X. Elements of Forecasting, 4th Ed. (Mason, OH: Cengage Publishing, 2007). Optional: Bowerman, Bruce L., Richard T. O'Connell and Anne B. Koehler, Forecasting, time series, and regression: an applied approach (4th edition), Duxbury Press (2005).