Empirical Macroeconomics (Y1/E1, 5op)

NB: This an advanced course primarily intended for major subject students of economics. It is possible for students of other faculties to apply for a study right for advanced studies, but the applicant must have a well-grounded reason for her/his application (for instance, post-graduate studies at another faculty of the University). Before applying, the student should have completed the intermediate studies in economics. Further information can be found here. Also, it is expected that the student has acquired the basic skills listed under ‘Prerequisites’ below.

Lecturer: Professor Markku Lanne, consultation hour Wednesdays 13 – 14 (office: B307).

Teaching assistant: Helinä Laakkonen (office: A319).

Overview: The goal of the course is to provide an introduction to business cycle analysis and the methods of modern empirical macroeconomics. The topics include the measurement of the business cycle, and dating and predicting business cycle turning points. Some econometric models suitable for capturing the behavior of macroeconomic time series are introduced. Issues relating to the construction of macroeconomic data are also covered. In particular, many macroeconomic variables are subject to revisions that must be taken into account in empirical work, and the properties of revisions as well as methods to deal with them are discussed.

Prerequisites: Basic knowledge of macroeconomics, econometrics and time series analysis (for example, Advanced Econometrics or the course in the analysis of stationary time series offered by the Department of Mathematics and Statistics, or equivalent) is assumed. Eviews and R are the software packages that will be used to demonstrate the methods in class, but any available computer program may be used for the assignments and the term paper.

Textbooks: The lectures will not directly follow any textbook, but the relevant parts of the following may be useful as supplementary reading. In addition, a number of relevant journal articles are listed below.

  • DeJong & Dave (2007): Structural Macroeconometrics. Princeton University Press.
  • Hamilton (1994): Time Series Analysis. Princeton University Press.
  • Harvey (1993): Time Series Models. Second Edition. Harvester-Wheatsheaf.
  • Soerensen & Whitta-Jacobsen (2005): Introducing Advanced Macroeconomics: Growth & Business Cycles. McGraw-Hill.

Lectures: 24 hours, 23 March to 4 May, 2009, Mondays and Wednesdays 10 – 12, ECONOMICUM SH 1. The lecture on Monday 27 April. is devoted to assignments (see below). In addition, there will be one excercise session on Wednesday 8 April (8 – 10, ECONOMICUM SH 1) and Thursday 16 April (8 – 10, ECONOMICUM SH 1). The lectures on Wednesday 22 April. and Wednesday 29 April are devoted to a problem-based group assignment. Presence at these lectures in compulsory.

On Wednesday 1 April, 10 – 12, instead of a lecture, there will be a session on the use of the R and Eviews software packages (at the computer lab of the Department of Economics).

Lecture slides as well as links to supplementary material such as programs and data used in class will be posted here in advance. Skimming through the slides before the lecture is highly advisable, and you should have the printouts available in class.

Slides Data and Supplemental Material Programs
Slides 1 (added 19.3.) Figures and Tables for Introduction sim_ar1.prg
Notes on Eviews and R (added 27.3.) Quarterly U.S. CPI Inflation 1970:1 – 2006:4
Slides 2 (added 2.4.) U.S. Macroeconomic Data Set with Description fixed_cycle.r
spectrum.r spectrum_estimation.r
Slides 3 (added 13.4.) filters.r
crosscorr.r
mvspectrum.r
Slides 4 (added 3.5.)

Assignments: There will be three homework assignments during the course, each consisting of a number of exercises. The assignments and the data sets will be posted here.

Typically the exercises will involve small-scale empirical analyses employing methods presented in class, but there may be some analytical problems as well. In class, the practical implementation of econometric methods will be illustrated with Eviews and R, but you are free to use any software package for the assignments. For credit, the solutions of the assignment (a hardcopy or e-mail attachment) should be returned to Helinä Laakkonen by 10 a.m. on the work day preceding each excercise session, and you must also be prepared to present your solution at the exercise session. Each assignment is graded on the scale from 0 to 5 (see Grading below). The grading criteria are the following:

0 1 2 3 4 5
Less than 50% of the exercises done At least 50% of the exercises done showing good effort At least 50% of the exercises done and some solutions are very good At least 75% of the exercises done showing good effort or at least 50% of the exercises done and some solutions are excellent At least 90% of the exercises done showing good effort and the solutions are mostly very good At least 90% of the exercises done showing good effort and the solutions are mostly excellent

Problem-based Group Assignment: 15% of the grade is based on a problem-based assignment solved in small groups. The lectures on Wednesday 22 April and Wednesday 29 April are devoted to this assignment. Attendance at these lectures is compulsory. Further information will be posted here in due course.

Timetable of Lectures and Other Sessions

Date
Monday 23 March, 10-12 Lecture 1 (ECONOMICUM SH 1)
Wednesday 25 March, 10-12 Lecture 2 (ECONOMICUM SH 1)
Monday 30 March, 10-12 Lecture 3 (ECONOMICUM SH 1)
Wednesday 1 April, 10-12 Session on Eviews and R (Computer lab)
Monday 6 April, 10-12 Lecture 4 (ECONOMICUM SH 1)
Wednesday 8 April, 8-10 Excercise session 1 (ECONOMICUM SH 1, Solutions must be returned by 10 a.m. on Tuesday 7 April)
Wednesday 8 April, 10-12 Lecture 5 (ECONOMICUM SH 1)
Wednesday 15 April, 10-12 Lecture 6 (ECONOMICUM SH 1)
Thursday 16 April, 8-10 Exercise session 2 (ECONOMICUM SH 1, Solutions must be returned by 10 a.m. on Wednesday 15 April)
Monday 20 April, 10-12 Lecture 7 (ECONOMICUM SH 1)
Wednesday 22 April, 10-12 Lecture 8 (ECONOMICUM SH 1, problem-based group assignment)
Monday 27 April, 10-12 Exercise session 3 (ECONOMICUM SH 1, Solutions must be returned by 10 a.m. on Friday 24 April)
Wednesday 29 April, 10-12 Lecture 9 (ECONOMICUM SH 1, problem-based group assignment)
Monday 4 May, 10-12 Lecture 10 (ECONOMICUM SH 1)

Term Paper: 35% of the grade is based on a term paper (see below) that is an emprical research report written on the given topic, employing the methods covered in the course. The term paper should be returned to Helinä Laakkonen by Monday 18 May, 2009 (3 p.m).

Grading: The final grade of the course is computed as the weighted average of the grades of the final exam (35%), the term paper (35%), the homework assignments (15%), and the problem-based group assignment (15%). All four parts are separately graded on a scale from 0 to 5, and in order to pass the course you must get at least 1 in each.

Exams: Final exam 7 May, 2009, retake exam 25 May .2009.

BSCW: All learning material will be made available through the BSCW (Basic Support for Cooperative Work) tool. The BSCW folder of the course can be found at https://kampela.it.helsinki.fi/bscw/bscw.cgi/0/2997506. For the access right to this folder, send an e-mail message containing your own e-mail address (of the form firstname.surname@helsinki.fi if you are a student at the University of Helsinki) to Helinä Laakkonen. Instructions on the BSCW are available at http://ok.helsinki.fi/index.php?page=277&language=1 (in Finnish) and at http://bscw.fit.fraunhofer.de/bscw_help-4.2/english/contents.html (in English).

Tentative Outline

Depending on time constraints, the list of topics is subject to change. The material marked with an asterisk (*) is required reading for the exam (only the parts covered in class).

1. Introduction

  • Soerensen & Whitta-Jacobsen, Chapter 14.*

2. ARIMA Processes in Time Domain

  • Hamilton, Chapters 3, 15 and 17.

3. Spectral Analysis

  • Hamilton, Chapter 6.
  • Harvey, Chapters 6 and 7.2.

4. Measuring Business Cycles

5. Locating and Predicting Turning Points

6. Stylized Facts

7. Real-Time Data and Data Revisions