Economics Welcome Guide

Surviving the first-year economics course sequence (Math Camp, Micro, Econometrics) that students across departments take together.

First-year economics classes

This primer is for students who have to take economics classes in their first year. One disclaimer up front: your experience may differ, so take everything here as one perspective rather than a rule.

Background

As a first-year PhD student in any of the fields that take economics classes, your background will usually fall into one of these categories, or a mixture:

Economics (some students, mostly in Policy; some in Management and Marketing)
Natural sciences like Mathematics, Statistics, Physics (rare, but some students)
Business background with some mathematics, such as Finance, Financial Engineering, Applied Economics (most students in Finance)
Business background with very little mathematics (some students in Accounting, Management, Marketing)

The general pattern is:

If your background is in economics or the natural sciences, you should be well prepared for what’s coming. You have some or extensive experience with variables, proofs, optimization problems, game theory, derivatives, integrals, real analysis, and related concepts.
If your background is in business, you may have seen some economics in the past but have little theoretical foundation. Maybe you have some empirical and econometrics background, but that’s about it – you may have never done proofs or game theory, done little optimization, and be rusty on derivatives and integrals.

How prepared will you feel?

A rough rule of thumb: the more economics and proof-based mathematics you’ve already done, the smoother the start. If you come from a quantitative economics or math/statistics background, you’ll likely be well prepared and able to follow along from fairly early on. If you come from a business background with little formal math – which describes a lot of incoming students – expect the first few months to feel hard, with problem sets and exams that are tough before things start to click. That difficulty reflects where you’re starting from, not your ability, and it’s normal for it to ease as the year goes on.

The tips here are aimed mainly at that second group – people arriving without much economics or proof-based math background.

Comprehensive exams, grades, and standing

Some people in the management department will have to sit one or more comprehensive exams. These are roughly 4-hour exams held in early June that cover everything from the past year in a specific field. For Policy students, this means macroeconomics and microeconomics, but their background is usually in economics, so they tend to be fine. The real challenge is being an unprepared student with a microeconomics comprehensive exam.

The first sitting is in early June; if you score below 50%, there’s a make-up exam in early August. A rough first attempt is more common than people expect, so treat the make-up as a normal part of the process rather than a disaster. Repeated failure can affect your standing in the program, so it’s worth taking seriously, but a single weak first sitting is very recoverable.

If you do NOT have a comprehensive exam (likely in Marketing, Accounting, Management, etc.), then even if you feel underprepared and struggle on exams, there are seemingly no hard rules that would have you dismissed. The good-standing requirement expects that you will have a minimum of a B- in all courses with an expected average of A– but in practice the class averages are low and the curve is generous, so even a rough raw score on a midterm often still lands you at A- once the curve is applied. Even if you fail the course, depending on your specification, it may not matter (although you may need to retake Econ) as you only need to pass 2 Econ courses to meet minor requirements.

This touches on one of the most important lessons of the first year. You were likely either naturally talented or very studious before this, so you’re probably not used to getting terrible grades – but it will happen. Nothing quite prepares you for a grade far below anything you’re used to on an exam you studied hard for and lost sleep over, but at some point it will happen. You are not stupid, you are not broken, and you are not your grade. Resilience is the most important quality you’ll build in the first year, and it outweighs any technical knowledge.

General survival tips

Incoming students often ask how to prepare for the first year by reading books, watching lectures, and so on. It’s generally better not to, unless you have three months or more with absolutely nothing else to do. This is your last real break for a while, so rest, travel, and enjoy time with family; you need to be fully rested; and the odds that pre-reading actually helps your grades are low. When math camp starts, the goal is just to be settled in Toronto with a calm head.
Since you can only study so many hours, make sure “life” obstacles don’t undermine your outcomes: food, sleep, physical health, the quality of your relationships, and alcohol or other dependencies.
Stress is a silent killer, and it’s very common among PhD students given high expectations, isolation, and uncertainty. This isn’t professional advice, but a few things that tend to help: good sleep, avoiding bad food, getting outside, avoiding dependencies, seeing people, breathing, seeing a psychologist, and listening to music.
Generally, don’t take on work outside the PhD during the program, especially in the first year (side gigs, part-time work, external studies). If you have time off, spend it resting and doing things that make you happy.
The difficulty and nature of the material differs between lectures, assignments, and exams. Lectures tend to be highly theoretical and cover more than what’s tested. Assignments are usually harder, since you’re given more time. It won’t be obvious early on what’s relevant versus not, but it gets clearer as you work through problems and mock exams.

Toolbox

Before the program starts, make sure your tools match your level of study: a good laptop with well-organized folders, a note-taking method that works for you, and the relevant accessories and filing systems.
Some students work from a binder and loose-leaf sheets, and some take no notes at all, but a decent note-taking tablet that syncs with a file-sharing system is a worthwhile investment. A tablet paired with cloud storage makes notes much easier to organize and review. It won’t determine whether you can prove some convexity result, but it does free up mental space.

Exam-taking skills

In the first year you’ll have several paper-based, closed-book exams. Learning how to prepare for and sit them is essential.
When you prepare, simulate exam conditions as closely as possible: save mock exams for the end of your preparation, time yourself, write on paper, and set aside anything that doesn’t directly improve your performance.
Managing stress and nutrition before an exam is essential. In most cases, better sleep and less stress the day before will help more than extra studying.
One effective approach is divide and conquer: start by reading all the questions, then solve the easiest one to build confidence, and continue from there. Your first read isn’t your full understanding – some questions take several readings before they click, so allow yourself to read, integrate, and think. If a question won’t budge after several readings, move on (better one bird in hand than two in the bush). It’s tempting to write something for every question, but that’s not actually useful, since most professors give no credit for filler.
Also develop a way to manage stress during the exam. What will you do when panic hits?

How the economics classes are structured

Each class is split into two sequences, so you effectively have four sequences in each of Micro/Macro/Metrics across the year (e.g. in Micro, Consumer Choice runs September to late October, General Equilibrium November to December, and so on). It’s subtle but important: you really have four courses per field in a year, not two.
Microeconomics, Macroeconomics, and Econometrics each run two 2-hour lectures per week. Micro and Macro also add a weekly 2-hour tutorial where the TA works through the assignments.
Attending the lectures is a must. Even if you don’t understand anything, it keeps you in step with the pace of the class. Attending the TA session can be either very valuable or not, depending on the session.
Economics students spend more attention on Micro and Macro, since Metrics has no comprehensive exam, and often skew their effort that way. If you have a Micro comp, it’s worth doing the same. Otherwise it matters less, but Micro is still probably the higher priority, since cohorts often do worse in Metrics.
Economics professors are generally very nice and approachable, and your questions are welcome. That said, some are so fluent in the material that they don’t always see where a beginner is stuck, and a few are very direct – they might tell you you’re off-track in front of the class. It isn’t personal or a verdict on you; it’s a style difference, and everyone has been on the receiving end of it.
When classmates ask sharp questions or comment confidently, keep in mind they’re often the ones who came in with the most background – they’re not the room’s baseline. In reality, most people are lost a good chunk of the time, even when it doesn’t look that way.

The economist mindset

It’s fair to say that most economists draw heavily on mathematics for the majority of their activities, so they approach problems in a rigorous, structured way. If you come from a business background, you may be used to looser explanations and informal notation – that’s not a personal failing, it’s just that many business curricula teach an applied, approximate version of the math, because formalism isn’t what industry rewards. Economics will ask for more rigor than you’re likely used to.

This year you’ll learn about proofs, properties, objects you didn’t know existed, intuitions formalized in mathematics, and more. If there’s one central piece of advice to keep in mind: by focusing on the form of the answer, the substance comes more easily. That means defining your variables and objects, defining the properties studied, using proper notation, writing succinctly, and forcing a canonical answer to each problem.

This takes intentional practice – the serious, rigorous kind, where you read questions several times, really think, write multiple approaches (all with good notation), and work with clean structure. Only once you’ve practiced that way can you perform well under exam stress and time pressure. The strongest students write mathematics with almost machine-like clarity and structure – and that’s a learnable habit, not innate talent. It’s well worth building.

Math Camp

Structure: Two sequences – (1) real analysis and (2) statistics. Four 2-hour sessions per week (unless changed) for around three weeks.
Description: You’ll join a room of people you’ve just met and cover unfamiliar mathematical material at a fast pace. It’s intense and packs a lot into a short window, but the point is to get everyone to a shared baseline before the real courses begin – not to weed anyone out.
Lecture difficulty: Most people have very little background in real analysis, so that part can feel confusing at first. Statistics is more standard and quite manageable if you’ve seen any before; if not, expect it to take some getting used to as well.
Exam difficulty: Very close to what’s covered in class. Pass/fail, and pretty much everyone passes.
Tips: Odds are you won’t learn anything very deep about real analysis or statistics in that short a time, so it’s usually enough to learn the material by heart and reproduce it on the exam. The assignments and lecture notes are solid, so do them. There’s a lecture section, a discussion section, and a problems section.

Microeconomics

The first-year micro sequence spans four sub-sequences across the year.

Consumer Choice

Structure: Consumer Choice, Comparative Statics, Expected Utility.
Description: Usually taught in a board-heavy, fast-paced, proof-driven style. Each class works through a series of proofs tied to the concept of the day, and the volume of material is large.
Lecture difficulty: Among the hardest of the year. Expect to follow only part of it in real time – and know that most of the room feels the same way.
Problem sets: Very challenging. Many problems are hard to even start at first, and you may have to present your attempts to peers during the TA session.
Exam difficulty: Demanding, and class averages tend to be low (often around 50%) – which is expected here and reflected in the curve. Grading is often more generous than you’d fear.
Tips: Come to class even when you’re confused – it’s good protection against giving up, and only a portion of lecture material shows up on the exam. Don’t expect to fully solve every problem set; if you present, just do your best. The material is often presented in a much harder way than the underlying concepts actually are, so reach for other sources (AI assistants, colleagues, etc.); the lecture notes are solid if dense, and you can feed them to an AI assistant to talk through. Skip the textbook and extra readings unless you really want them. Don’t guess at solutions – write down everything you know about the problem, then work through it slowly. And don’t try to prove every result in the notes; there isn’t time, and by the comprehensive exam you’ll have a sense of what’s essential.

General Equilibrium

Structure: General Equilibrium, Collaborative Game Theory.
Description: Usually a bit easier to follow and a bit slower-paced, though exams can still feature some real curveballs.
Lecture difficulty: Surprisingly moderate.
Problem sets: Can feel quite different from the lectures. If you’ve seen this material before it’s manageable; if not, it can be hard to know where to start – in which case it’s fine to wait for the solutions and learn the steps.
Exam difficulty: Usually representative of the problem sets, with few curveballs in the final – though the comprehensive exams can be tougher.
Tips: Don’t try to guess solutions to the first problem sets; they involve structured steps you can’t improvise, so wait for the solutions or the TA’s walkthrough. Don’t fixate on understanding everything in the lectures – focus on understanding and solving the problems. The slides are solid; use them.

Game Theory

Structure: Basics of Game Theory, Refinements.
Description: Starts from the basics but moves quickly, and ramps up in difficulty fast.
Lecture difficulty: Average to difficult.
Problem sets: Range from easy in the first weeks to quite difficult later. It takes time to build intuition for many of these problems.
Exam difficulty: Questions range from easy to hard.
Tips: Don’t relax in the early weeks assuming it’ll stay easy. Problems tend to be either procedural (defined steps) or intuitive (no fixed recipe) – make sure you can reliably do the procedural ones, and give yourself room to struggle with the intuitive ones. For intuitive problems, invest time understanding the structure of the game before solving. If you hit very long, multi-step proofs and you’re behind, it’s fine to prioritize other things. As the number of variables grows, so does the room for confusion – and stress makes it worse – so carefully reading and setting up the problem matters even more.

Contracts and Mechanism Design

Structure: Various game-theoretic models.
Description: Builds on the game-theory foundations and applies them in a model-based framework. One of the most interesting sequences, but with a high skill ceiling, and the connection to the earlier sequences isn’t always obvious at first.
A note on variability: Experiences with this sequence differ quite a bit. Some students have found it much more approachable in years it was taught differently, so don’t take any single account (including this one) as definitive. The split between the third and fourth sequences also shifts year to year – material can move from one to the other – so the difficulty of a given sequence depends partly on how the content is divided that year.
Lecture difficulty: Variable – anywhere from moderate to difficult depending on how the sequence is taught and where the material falls.
Problem sets: Range from difficult to very difficult depending on the specific model and assumptions.
Exam difficulty: Ranges from moderate to very hard.
Tips: Don’t expect to understand everything – learn the canonical solution to the simplest version of each model rather than memorizing every proof. With more variables in play, spend more time reading and understanding the structure of the question before answering. A single added variable or tweak can make a question much harder, and exams may ask you to solve models you haven’t seen, add assumptions, or flip the setup. The best preparation is to know the simple versions cold, then read each exam question several times before diving in.

Econometrics

Like Micro and Macro, this course runs two 2-hour sessions per week, but with no tutorial. The full course (Econometrics I + II) is divided into four sequences; this discussion splits it in two.

Econometrics I (Theoretical Econometrics)

Structure: Identification, Instrumental Variables, Sufficient Statistics, Hypothesis Testing, Power, Size, etc.
Description: If you’re not comfortable with mathematical symbols, high-level matrix algebra, and statistics, this class will be very difficult. Where many econometrics courses touch lightly on proofs and focus on applying the models, this sequence is oriented almost entirely toward proofs and conditions.
Lecture difficulty: The first half is hard to follow. Watch out for lectures taught without slides, since the pace tends to jump. The second half is usually a bit easier once you have some footing, but still demanding.
Problem sets: Expect a couple in the first half, ranging from doable to quite hard. The second half tends to involve some coding.
Exam difficulty: There may be no exam in the first half; where exams do occur, averages tend to be low. Later exams are more manageable when they resemble past mock exams.
Tips: Econometrics usually has more graded problem sets than Micro, so put your effort into scoring well on those rather than banking on the exam, and don’t hesitate to use any resource available. The suggested books and papers are optional – consult them as needed. Do ALL the practice exams and problem sets; practice is how the patterns and understanding come, and re-reading past lectures is a much less efficient use of time. Current exams often resemble past ones, so anchor your studying in practice rather than pure theory (this differs from Micro, where intuition arguably plays a bigger role). And even if you don’t understand much, still go to class.

Econometrics II (“Applied” Econometrics)

Structure: Non-parametric methods, Curse of Dimensionality, Panel Data biases, etc.
Description: Despite the “Applied” label, most lectures still feel theoretical (proofs of convergence, asymptotic normality, etc.). It is still more applied than the earlier sequences.
Lecture difficulty: Perhaps the most manageable of the economics classes. The later material has some hard ideas, but the pace is usually reasonable and the math not too overwhelming.
Problem sets: Manageable. Expect some ungraded coding problem sets (loosely relevant to your grade) and a term paper with fairly open instructions.
Exam difficulty: One exam tends to be manageable and close to the problem sets and past exams; another can be more of a curveball.
Tips: Avoid pouring time into things that don’t go directly toward your grade – unfortunately that sometimes includes the most practical material (the coding problem sets), though this can change over time. Practice pays off here too; concepts recur, so if you’re comfortable with the canonical forms on pen and paper, you should be in good shape. Aim to get the best grades you can on problem sets and term papers, where you have time – exams have a large noise component and can catch anyone out.