Econ6003, Semester 1, 2023
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Econ6003, Semester 1, 2023
Final Exam Info & Preparation Advice
Note 1. (Important) This is NOT an open book exam like In-Class Assignment. Final
Exam is conducted by University Exams Office via a separate Canvas Page. The following is a Replica of instructions you see when you enter that Canvas Site.
Your exam will consist of:
|
Question type |
Points |
Recommended time spent |
Problem 1 |
Multiple Answers/Fill-in-blanks testing basic understanding |
24 |
40 min |
Problem 2 |
Answers to be supplied as MCQ/FIB format, but all refer to the solution of an optimization problem . |
24 |
45 min |
Problem 3 |
Extended Response — Requires solving an optimization problem, and upload of hand- written answers. |
12 |
35 min |
The following is a replica of what you will see when you click on the Exam Quiz.
Instructions You will need to install the ProctorU Extension. Please download it here for Chrome browser or here for Firefox. All exam questions will appear only in this quiz. Do not click submit until you have finished answering all questions. Exam is divided into Problem 1, Problem 2 and Problem 3 (and sub-parts). You can score a maximum of 60 points. Points for each part are shown. Problem 3 requires you to handwrite your answer on white paper in blue / black pen. You must scan / photograph your work only after you have submitted the quiz as the use of your mobile phone during the quiz is prohibited. You must upload your document to the Canvas assignment within 30 minutes after the quiz submission. You must not handle your mobile device at any point prior to submitting this quiz. Use of a mobile device during the quiz will be flagged as an integrity breach by ProctorU. |
Note 2. Exam is worth 45% of your final grade if you have attempted Homework 2,
otherwise it is worth 55%.
Note 3 Problem 1 tests your understanding of all the material. Problem 2 concerns a
dynamic programming problem. Problem 3 concerns constrained optimization.
Study Resources Mathematics for Economists by Simon and Blume. Relevant portions from this text were indicated during the term. Other supplied Chapters from different texts, Lecture notes (These are available from the Lectures page on the regular Canvas site. Weekly Class slides and Problem Sets. Check out the Wikipedia pages for specific terms as well the Wikipedia math pages for the most part are excellent. In fact I quite often use them as a quick reference during the course of my own research. Lecture 13 on Dynamic Programming: Restrict attention to the introductory material covered in the actual lecture, and Problem Set 7. |
Preparation Method. Surely, each of you will have your own preparation style for
exams. However, I strongly advise you to make sure you work through every exercise in the problem sets as on your own (even after you have seen the solutions!).
Problem 1 . (24 pts) This consists of 5 questions. Each question requires a brief answer, does not require any long computations/derivations.
Problem 2 & Problem 3 (24 pts and 12pts respectively.). These are essentially two opti- mization problems in principle but you will still need to draw on other topics to provide all the answers correctly. It would be fair to say that however, in comparison to In-Class assignment, these are conceptually less demanding and more mechanical.
Examinable Material. In principle everything covered over the semester. However, I
will not be examining you directly on pre-midterm material. For example, “linear indepen- dence” was covered before the midterm but you still need to check for linear independence
of gradients when checking NDCQ in optimization problems.
Post midterm here is a set of keywords for you to focus on:
Linear Algebra all of it . Convex sets, Separating Hyperplane Theorems, Farkas Lemma, Derivative as the “best linear approximation” , Mean Value Theorem, Differentiability in Rm, difference between the gradient and derivative in Rm, Quadratic functions and definite- ness of matrices. Envelope Theorem, First and Second Order conditions for unconstrained maximization. NDCQ (non-degenerate constraint qualification). Constrained optimiza- tion with equality constraints (Theorem of Lagrange), and with mixture of equality & inequality constraints (Kuhn-Tucker Theorem (necessity only)),
For Dynamic programming in particular, make sure that you understand setting up of the Bellman Equation, the Envelope Condition and the Euler Equation.
2023-06-07