Course description Econometrics 15 Higher Education Credits (ECTS Credits) Course code: ST223G Spring 2015

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Department of Statistics 2015-10-02/PGA Course description Econometrics 15 Higher Education Credits (ECTS Credits) Course code: ST223G Spring 2015 Content The course consists of four parts.

1 Department of Statistics /PGA Course description Econometrics 15 Higher Education Credits (ECTS Credits) Course code: ST223G Spring 2015 Content The course consists of four parts. I) Econometrics I, Examination 6.0 credit points II) Econometrics I, Compulsory Assignment 1.5 credit points III) Econometrics II, Examination 6.0 credit points IV) Econometrics II, Compulsory Assignment 1.5 credit points The first two parts of the course deal with simple and multiple regressions, the consequences and the actions we take when the model assumptions are not correct. They also deal with models for binary dependent variables such as logit and probit models. The last two parts of the course deal with analysis of time series, especially exponential smoothing, ARIMA, VAR and other volatility models. They also deal with models dynamic econometric models, error correction, cointegration and analysis of panel data.

2 Course literature Gujarati, D. N. and Porter, D. C. (GP) Basic Econometrics, 5th edition. McGraw-Hill, New York Montgomery, DC, Jennings, CL, and Kulahci, M. (MJK) Introduction to Time Series Analysis and Forecasting, 1:st edition. Wiley, New Jersey Exercises in Time Series Analysis (ETSA). Used only on parts III and IV. Will be posted on the course website. Learning Outcomes After completing the course the student should be able to: Carry out the analysis of different econometric problems using regression models Carry out the analysis of econometric time series data by using time series models Teaching Teaching consists of lectures (L1-L25), exercises (E1-E12) and computer exercises (D1-D8) according to the course schedule. NOTE! The first lecture (Lecture-1) is mandatory.

3 Disposition Part I and II Lectures: Lecture Content Chapter L1 Course information, introduction GP:1 L2 Linear regression model assumptions GP:2-3 L3 Linear regression cont. GP:3-4 L4 Linear regression cont. GP:5-6 L5 Multiple linear regression GP:7 L6 Multiple linear regression GP:8 L7 Dummy variables GP:9 L8 Multicollinearity GP:10 L9 Heteroscedasticity GP:11 L10 Heteroscedasticity GP:11 L11 Autocorrelation GP:12 L12 Model specification, outliers GP:13 L13 Non-linear regression, logistic regression GP:14 L14 Repetition Group Exercises: Exercises (GP) E1: 2.7, 2.13, 3.1, 3.6, 3.7, 3.14, 3.16 E2: 5.1, 5.5, 5.8, 5.14, 5.15 E3: 6.1, 6.2, 6.7, 6.13, 6.14, 7.10 E4: 7.14, 7.20, 8.2, 8.6, 8.7 E5: 8.19, 8.20, 9.1, 9.5, 10.10, E6: 10.19, 11.1, 11.2, 11.6, 12.2, 12.3, E7: 13.2, 13.3, 13.11, 13.19, 15.7, Computer exercises: Exercises (GP) D1: 5.9, 5.19, 6.20, , 8.26 D2: 9.22, 9.26, , D3: Work with Assignment-I D4: Work with Assignment-I

4 Part III and IV Lectures: Lecture Content Chapter L15 Panel data analysis GP: 16 L16 Panel data analysis GP: 16 L17 Dynamic econometric model GP: L18 Basic concept of time series GP:.21 L19 Time series econometrics GP:21 L20 Exponential smoothing MJK: 4 L21 Moving average MJK:5 L22 Autoregressive processes MJK:5 L23 Autoregressive moving average MJK:5 L24 ARIMA, GARCH, ARCH MJK:7 L25 Repetition Group Exercises: E (GP) E (ETSA) (MJK) E (ETSA) E (ETSA) E (ETSA) Computer Exercises: D5 Dynamic models (GP) D (MJK) D (MJK) D8 Work with Assignment-IV Course Schedule See the webpage of the course.

5 Examination I. Econometrics I, exam The written exam deals with regression analysis according to the course content for the part I and II. II. Econometrics I, compulsory assignment To be announced later III. Econometrics II exam The written examination deals with panel data and time-series analysis according to the course content for the part III and IV. IV. Econometrics II, compulsory assignment To be announced later The assignments are done as a group project (a group consists of 1-3 students) and presented in writing. We encourage discussions among students between groups during the work on the assignment, but each group must finally be able to answer for their own handed-in written report. Deadline for handing in assignment II: Friday 27 November, Deadline for handing in assignment IV: Monday 11 January, Grading Criteria The compulsory assignments are graded as Pass (G) or Fail (U). If a compulsory assignment is graded as Fail (U), the student will have only one chance to re-submit the assignment and this has to be done within a week. The written exams are graded as A, B, C, D, E, Fx and F. The passing grades are A, B, C, D and E, where A is the highest and E is the lowest. Failing grades are F and Fx, where F is lower than Fx. When obtaining a failing grade F or Fx in the written examination, we will not give extra exercises or extra assignments to obtaining a passing grade.

6 Grading criteria for written examination part I and III The maximum total credit is thus 100 points for each examination. Grades are given on a seven-point rating scale: A points B points C points D points E points Fx points F points To pass the course a minimum grade of E is required. Final grade for the complete course Econometrics, GN, 15 credits points To pass the entire course, a minimum grade of E for parts I and III, and a pass grade for parts II and IV are required. The final grade of the entire course is given according to the following table: Grades of Part I and III (independent of the order) A + A, A + B A + C, B + B, B + C A + D, A + E, B + D, B + E, C + C, C + D C + E, D + D, D + E E + E Final grade A B C D E Teachers and Communication Course coordinator and examiner Per Gösta Andersson, room B per.gosta.andersson@stat.su.se Teaching Assistant Gelaye Hailemichael, room B gelaye.hailemichael@stat.su.se

Detailed course syllabus

Detailed course syllabus 1. Linear regression model. Ordinary least squares method. This introductory class covers basic definitions of econometrics, econometric model, and economic data. Classification