AØKA08007U  Econometrics C

Volume 2013/2014
Education
BSc in Economics - compulsory
MSc in Economics
Content
Econometrics C is the final course in the compulsory BSc. course sequence in statistics and econometrics. The course Econometrics B focuses on linear regression and instrumental variables estimation of the linear regression model for cross‐sectional data. The current course goes into more details with the estimation principles and presents the generalized method of moments and the likelihood analysis. Econometrics C also discusses dependent observations and gives a detailed account of the econometric analysis of time series data. As an integral part of the course, students are introduced to statistical tools for analysing time series and panel data.
Learning Outcome
Give an account for the motivation and intuition for different principles for estimation and inference ‐ specifically the method of ordinary least squares (OLS), method of moments (MM), and maximum likelihood (ML) ‐ and discuss relative advantages and drawbacks. ∙ Give an account for the sufficient conditions for consistent estimation and valid inference and apply the estimation principles to derive MM and ML estimators in statistical models. ∙ Give an account for the important differences between (independent) cross‐sectional data, analyzed in detail in Econometrics B, and time series data. Precisely describe the conditions under which the results from the linear regression analysis for cross‐sectional data can be used also on time series data. ∙ Explain the consequences of unit roots in economic data and interpret statistical models for stationary and non‐stationary time series. Construct and interpret statistical tests for unit roots in economic time series. ∙ Interpret statistical models based on cointegration and error correction and perform empirical analyses based on these ideas. ∙ Give an account for autoregressive conditional heteroscedasticity (ARCH) in financial time series, and perform empirical ARCH analyses. ∙ Give an account for models involving both a cross‐sectional dimension and a time dimension (panel data) and perform empirical analyses based on these models. ∙ Choose the relevant tools for a given problem and apply the tools to new problems and new data sets. More specifically to identify the characteristic properties of a given data set, suggest and construct relevant statistical models, analyze to what extend the statistical model is congruent with the 38 data, estimate and interpret the parameters of the model, formulate economic questions as hypotheses on the parameters of the model, and test these hypotheses. ∙ Use statistic and econometric software. Give statistically sound and economically relevant interpretations of statistical results. ∙ Use statistic and econometric terms in a correct way and be able to present econometric results in a clear and concise way.

Syllabus:

  • Textbook: Marno Verbeek: A Guide to Modern Econometrics, 4th Ed., Wiley.
  • Chapter 1-3 (cursory reading) p. 1-93 (93*)
  • Section 4.1-4.5 (cursory reading) 94-112 (18*)
  • Section 4.6-4.11: p. 112-136 (25)
  • Chapter 5-6 p. 137-205 (69)
  • Section 7.1.1-7.1.6 p. 206-217 (12)
  • Section 7.3 p. 231-238 (8)
  • Chapter 8 p. 278-337 (59)
  • Section 9.1-9.3 p. 338-350 (13)
  • Section 9.4-9.7 (cursory reading) p. 350-371 (22*)
  • Section 10.1-10.2.4 p. 372-387 (15)
  • Section 10.3 p. 394-396 (3)

 
Lecture notes:

  • [1] Introduction to Time Series (13)
  • [2] Linear Regression with Time Series Data (22)
  • [3] Introduction to Vector and Matrix Differentiation (cursory reading) (6*)
  • [4] Dynamic Models for Stationary Time Series (28)
  • [5] Non-Stationary Time Series and Unit Root Testing (21)
  • [6] Cointegration and Common Trends (31)
  • [7] Modeling Volatility in Financial Time Series: An introduction to ARCH (16)
  • [8] Generalized Method of Moments Estimation (31)


Location:
Check the location of the lecturing and classes at the  locationplan for the spring semester

Econometrics A and B
4 hours of lecturing and 2 hours of excercises per week for 14 weeks

Various software packages will be introduced and applied
Credit
7,5 ECTS
Type of assessment
Oral examination, 25 minutes under invigilation
Oral examination with preparation
Exam registration requirements
As a part of the course, three written assignments should be completed and accepted.
Aid
Written aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
100 % external censorship
Exam period
Will be published before the start of the semester
Criteria for exam assesment
The Student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
  • Category
  • Hours
  • Lectures
  • 56
  • Class Exercises
  • 28
  • Preparation
  • 121
  • Exam
  • 1
  • Total
  • 206