The main body of the course will include the following empirical DSGE evaluation methods: calibration, maximum likelihood and Bayesian estimation procedures

1 C^OURSE

I^NSTRUCTOR T^ERM

L^ECTURE D^AYS L^ECTURE T^IMES L^ECTURE L^OCATIONS

Econ. 608: Macroeconometric Analysis Chetan Dave

Winter 2020 Tuesdays

12:30pm-3:20pm Cameron Library 1-30

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Office Hours TR, 3:30-5:30pm, email appointment is best

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Pre-requisites Restricted to graduate students.

Course Description The aim of this course is to introduce students to applied structural dynamic stochastic general equilibrium (DSGE) modeling. The course will employ various DSGE models with a focus on estimation and testing for inference. As a part of the course, students will be exposed to advanced solution techniques for stochastic difference equations and general modeling setup. The main body of the course will include the following empirical DSGE evaluation methods: calibration, maximum likelihood and Bayesian estimation procedures. Additional topics that may be covered will include the method of moments and indirect inference. Technical prerequisites for the course are an understanding of multivariate calculus and matrix algebra and knowledge of basic econometric techniques.

Textbooks

Teaching Methods

DeJong, D. N. and C. Dave, 2011. Structural Macroeconometrics, 2^nd edition, Princeton University Press, ISBN-13: 978-0691152875.

Notes: This textbook is required.

This is a lecture based course and class participation from students is strongly encouraged.

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Grading Criteria Each of the two take home exams in this course count for 40% of the final grade. Assignments will be handed out throughout the term (4 such assignments) and will count for a total of 20% of the final grade. Grades are based solely on exams and assignments; there will be no extra credit or additional work in exchange for grades. The numeric to letter grade mapping is as follows.

Numeric Grade Letter Grade

90-100 A+

85-89 A

80-84 A-

77-79 B+

74-76 B

70-73 B-

67-69 C+

64-66 C

60-63 C-

57-59 D+

54-56 D

50-53 D-

0-49 F

Attendance and Conduct

Additional Resources

University Policy and Notices

Students are expected to attend class regularly and be prepared to participate. This means that students will have completed the assigned readings and other materials ahead of time. Students are expected to treat everyone in the classroom with respect. Please remember to turn off or set your cellular phones to vibrate. You are responsible for any announcements made or information given during class; exams will be based on lecture material and required readings.

The Student Success Centre (www.studentsuccess.ualberta.ca) offers a variety of learning resources, including a variety of workshops in learning effective study and exam strategies. Sessions are available in person and online, for a modest fee.

Policy about course outlines can be found in the Evaluation Procedures and Grading System section of the University Calendar. The University of Alberta is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behavior (online at

3 www.governance.ualberta.ca) and avoid any behavior which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University (http://www.ualberta.ca/current- students/academic-resources/academic-integrity). Audio or video recording, digital or otherwise, of lectures, labs, seminars or any other teaching environment by students is allowed only with the prior written consent of the instructor or as part of an approved accommodation plan. Student or instructor content, digital or otherwise, created and/or used within the context of the course is to be used solely for personal study, and is not to be used or distributed for any other purpose without prior written consent from the content author(s).

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Week 1. Introduction, Notation

Week 2. Notation, Introduction to Coding Weeks 3-4. Example Model and Data, Coding

Weeks 5-6. Linearization and Linear Solutions, Take Home Exam 1 handed out SPRING BREAK

Week 7. Isolating Cycles in Data

Week 8. Summarizing Time Series Behavior, Calibration Week 9. Calibration

Weeks 10-11. Maximum Likelihood

Week 12-13. Bayesian Methods, Take Home Exam 2 handed out