IØ8304 - Forecasting methods in economics and finance


Examination arrangement

Examination arrangement: Assignment
Grade: Passed/Failed

Evaluation form Weighting Duration Examination aids Grade deviation

Course content

Decisions in business, economics, and finance depend crucially on future values of demands, economic growth, prices, and other variables of interest. Hence, the need of forecasting these variables in the best possible way. This involves micro- and macroeconomic data, accounting data and financial market data. The purpose of this course is to give the students tools to make forecast and an understanding of the modelling and forecasting of economic variables. The course is at advanced master / PhD level. It provides a support for thesis and paper writing involving economic forecasting. The course covers main topics in modern econometric analysis, including estimation techniques, model selection, hypothesis testing, forecasting, and simulations using state of the art models. The course offers models that are ideally suited for univariate and multivariate point forecasts, event (including extreme events) forecasts, and distributional/interval forecasts.

The course will first introduce data and descriptive analysis, variable transformations, basic regression analysis and principal component analysis. Intermediate and advanced topics in time-series econometrics will then be presented. We start by discussing the decomposition of a time series into season, cycle, trend, and volatility. Models discussed include ARIMA and ARFIMA models, GARCH models (univariate and multivariate), VAR models and VECM. State-of-the-art forecasting methods, such as Mixed Frequency Data Sampling (MIDAS), Regime (or Markov) Switching models, and Bayesian forecasting techniques, will be discussed. In the course we will also look at Quantile Regression, Limited Dependent Variable Models and State Space Models with Kalman filter (including unobservable component models). Examples of panel data techniques will also be discussed. The course will at the end also introduce statistical/machine learning techniques, such as elastic net, ridge regression and LASSO estimation. The pedagogic approach is fully interactive and illustrated with relevant data from economics and finance. The focus will be more on the empirical implementation of the techniques than on their theoretical underpinnings. The techniques will be illustrated with several empirical applications, and then implemented using Macrobond Reuter and Eviews.

Learning outcome

In this course, the candidate will acquire key knowledge in modern forecasting methods in economics and finance. The candidate will receive training to make a lecture of a selected topic and present this. The candidate will also receive training in writing and presenting a termpaper. This will be an important part of the general PhD training for the candidate. The candidate will also get acquaintance to usage of databases and statistical software.

Learning methods and activities

The course will consist of a mixture of traditional lectures and practical exercises. It is assumed that the student brings his/her own laptop at the lectures and gets familiar with the usage of available databases and software for empirical analysis of economic and financial data. Data will cover micro- and macroeconomic data, accounting data, and data on financial markets (stocks, interest rates, currencies, and commodities). The Macrobond Reuter database and Eviews/Excel will be used in the course, but also other databases and software (e.g. R, Python, Stata, Oxmetrics, Matlab). It is also expected that the student will be active in all of the classes with discussions.

Compulsory assignments

  • Computer exercises

Further on evaluation

In order to get the course approved, the students need to: (1) Make a lecture on a selected topic and present this, and (2) Make a termpaper and present this. The quality of the lecture/termpaper and the presentations of these will be evaluated by an external sensor and the lecturer.

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Course materials

Recommended books:

(1)Diebold F., 2017, Forecasting in economics, business, finance and Beyond (free download from:

(2) Elliott G. and Timmermann A., 2016, Economic Forecasting Hardcover, Princeton University Press

(3) Ghysels E. and Macellino M., 2018, Applied Economic Forecasting using Time Series Methods, Oxford University Press

(4) Gonzalez-Rivera G., 2013, Forecasting for Economics and Business, Routledge

(5) Hendry D. and Castle J., 2019 Forecasting: An Essential Introduction, Yale University Press

(6) Hyndman R.J. and Athanasopoulos, 2018, Forecasting Principles and Practice, Otexts

Credit reductions

Course code Reduction From To
TIØ4557 3.5 01.09.2015
More on the course



Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level


Term no.: 1
Teaching semester:  AUTUMN 2020

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Managerial Economics, Finance and Operations Research
  • Industrial Economics and Technology Management
  • Business Economics
  • Financial Economics
Contact information
Course coordinator:

Department with academic responsibility
Department of Industrial Economics and Technology Management



Examination arrangement: Assignment

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