GRA 6647 Applied Econometrics and Time Series

Responsible for the course
Hilde C Bjørnland

Department of Economics

According to study plan

ECTS Credits

Language of instruction

This course is a graduate level introduction to Econometrics.

Learning outcome
Econometrics uses statistical methods for estimating economic relationship, testing economic theories, and using estimated models to analyze the effect of policy intervention for the public and the private sector. The goal of the course is to give students an intuitive yet formal understanding of the basic techniques used in applied econometrics, so that eventually they can master and produce sophisticated applied econometric analysis. The focus of the course is on time series econometrics with applications in macroeconomics and international finance. We will cover univariate and multivariate models of stationary and nonstationary time series, including structural VARs. A review of the main estimation methods, such as maximal likelihood and instrumental variables, will also be covered.

Introductory Course in Econometrics

Compulsory literature
Hamilton, James D. 1994. Time series analysis. Princeton, N.J.: Princeton University Press
Patterson, Kerry. 2000. An introduction to applied econometrics : a time series approach. London : Macmillan. 2, 3, 6, 7, 8, 14

Hilde C. Bjørnland. Articles supplementing the book will be distributed

Recommended literature
Favero, Carlo A. 2001. Applied macroeconometrics. Oxford: Oxford University Press. Chapter 1,2,3 and 6
Greene, William H. 2008. Econometric analysis. 6th ed. Upper Saddle River, N.J. : Prentice Hall

Course outline
I. Introduction - Review of undergraduate material
  • Means, standard deviations, covariances and correlations
  • Least squares, Maximum Likelihood, diagnostic tests (t and F-distribution)

II. Stationary univariate time series
  • White noise, moving average, autoregression, ARMA models
  • Analysis of business cycles in the frequency domain, spurious cycles
  • Lagging and leading indicators of the business cycle, the role of financial vaiables

III. Non-stationary univariate time series
  • Deterministic and stochastic trends, unit root tests, structural change
  • Trend/cycle decompositions
  • Forecasting, (asset prices)

IV. Classical Multiple Linear Regression Model
  • Least squares - specification, computation, diagnostic tests
  • Hypothesis testing
  • Spurious regression and economic relationships

V. Vector autoregression (VAR) methodology
  • Granger causality, cointegration, economic examples (i.e. purchasing power parity)
  • Structural VARs – identification, impulse responses, forecast error variance decomposition
  • Monetary policy in structural VAR systems

VI. Methods of Estimation
  • Instrumental variables (IV) estimation
  • Maximum likelihood estimation
  • Generalized method of moments (GMM) estimation

VII. Setting up an econometric project
  • Data handling, specification, modeling, policy analysis

Computer-based tools
The course uses modern statistical software such as EVIEWS. Knowledge of EXCEL is required.

Course structure
36 hours of lectures.

Specific Information regarding student evaluation will be provided in class. Please note that while attendance is not compulsory in all courses, it is the student's responsibility to obtain any information provided in class that is not included on the course homepage/Blackboard or text book.

Term paper and a three hour written exam. Groups of up to three students on the termpaper. Both parts of the evaluation must be passed to obtain a coursegrade. The termpaper counts for 40% of the grade and the exam counts for 60% of the final grade.

Evaluation code(s)
GRA66471 counts for 100% of the final grade in GRA 6647

Aids at the examination
A bilingual dictionary and BI-approved exam calculator. Exam aids at written examiniations are explained under exam information in our web-based Student handbook. Please note use of calculator and dictionary.

Makeup exam
Re-takes are only possible at the next time a course will be held. When the course evaluation has a separate exam code for each part of the evaluation it is possible to retake parts of the evaluation. Otherwise, the whole course must be re-evaluated when a student wants to retake an exam. Retake examinations entail an extra examination fee

Honor Code
Academic honesty and trust are important to all of us as individuals, and represent values that are encouraged and promoted by the honor code system. This is a most significant university tradition. Students are responsible for familiarizing themselves with the ideals of the honor code system, to which the faculty are also deeply committed.

Any violation of the honor code will be dealt with in accordance with BI’s procedures for cheating. These issues are a serious matter to everyone associated with the programs at BI and are at the heart of the honor code and academy integrity. If you have any questions about your responsibilities under the honor code, please ask..