ETC2410: Introductory Econometrics

10% mid sem test - mcq on laptops in a cramped room so quite easy to cheat. questions were fairish in difficulty. no cheat sheet
10% group assignment 1 - hard
10% group assignment 2 - hard
10% tut participation - 5% for submitting tutorial questions which are marked so you as long as you put or look like you put some effort in, you can get the 5%. and the other 5% is for coming in.

to be honest, im not deserving of my mark because i still have no idea to this day what i've learned. all the assignments were super hard, the content in this unit was super hard and is a huge step up from bus stats. the math is quite complex and i really dont understand anything in this unit. the theory is abstract (to me anyways) and to finance students, good luck lmao! i had Le as my tutor and he was amazing. if you can, try to get him. he is very good at explaining concepts (dare i say better than farshid) and is very responsive to emails (will reply at 1am). his notes are also very good.
if you have a weak background in math you will struggle in this unit but if you have a strong background, this should be a walk in the park for you. dont do this unit unless you have to

farshid vahid-araghi. quite difficult to follow but not enough to warrant avoiding the unit because hes teaching. i heard someone called jun is one of the best lecturers for this unit so maybe worth waiting for him?

Yes 2 previous exams with answers

3 out of 5

Yes, with screen capture

no

1x 1 hr 30 tutorial - most of the time doesnt even reach the 1hr 30 mark.
1x 2 hr lecture

sem 1 2019

D

1x 10% mid-semester test, 2x 10% group assignments, 1x 10% tutorial participation, 1x 60% final exam.

Really satisfactory unit. I haven’t been to or seen most of his lectures so I couldn’t comment on the lecturer’s teaching quality, but some say that he can be very hard to follow especially during the last few weeks of content. Most finance/economics/econometrics/actuarial majors would start this unit straight after ETC1000 or their first semester, and it has quite a steep learning curve compared to the first-year equivalent of this unit.

You basically look at linear regression once more (the same kind you do in ETC1000), but you look at more on the mathematics behind it. Farshid starts off with describing the different types of data and why econometrics is needed in the social sciences and business/finance, and then runs through a “rehash” on basic concepts of statistics and matrix algebra, so you know what knowledge you should expect before jumping into the unit itself. Personally, I thought watching YouTube videos and reading the appendix of the textbook gave me quite an edge so I’d recommend doing it as well. The rest of the content focuses on regression, in particular the Ordinary Least Squares (OLS) estimator. You’d learn about their assumptions to achieve the estimator’s unbiasedness, efficiency and ability to do inferencing (Gauss Markov/Classical linear model is introduced here). There is quite a bit of proofs that you ought to follow so it can be confusing. You’ll then run through coefficients and their interpretation (like ETC1000), goodness-to-fit, models involving log and quadratic terms, and model selection criteria (choosing a model with the least BS), which gets very interesting once you get the hang of it. After this comes the harder parts of the unit. Farshid starts talking about violations of the OLS assumptions that make the OLS less “adequate” in inferencing from data, including heteroskedasticity and serial correlation, their diagnosis and amendments to make a model reliable for inference. This is where he starts talking about time-series data as well, and unfortunately, the lecture slides can be quite confusing here (since he doesn’t dwell into time series data and its differences with cross-sectional data, which is what we’ve seen earlier). He then goes through some dynamic modelling (using past data to predict future data), some problems that may arise from this and finally the asymptotic properties of having large sample sizes (CLT, and weak law of large numbers are introduced here).

This unit can be quite mathematically demanding for the common student, though it is quite simple relative to other ETC units that you might come across, so it’s essential for you to brush-up on your mathematics (basic summation and matrix algebra should be sufficient, you can learn this in two weeks). You need to hand-in your tutorial questions uploaded during the week to get your participation marks, but these won’t be marked. The mid-semester test is 20 MCQs which can be quite tricky, so study up before this! There are also 2 10% group assignments that take up quite some time, but the assignment marks are fairly easy to obtain if you get the right tutor, and the final exam (which was done yesterday) is tending to be less mathematical and more about inference and testing, which is fairly straightforward compared to past exams.

The tutorials are scored so it’s definitely worth it to attend, and you do quite a bit of work on EViews, a data analysis tool that you have to use in your assignments, and you are allowed to bring a cheat sheet (2-sided A4 with hand-written notes) into the exam so you're not required to memorise the wide variety of formulas used in this unit. I really liked this unit overall.

Professor Farshid Vahid-Araghi, nice guy and very approachable, was quite thorough with the content from week 1-7, but can be quite hard to follow sometimes.

5 past exams with solutions, though the nature of the unit has changed a bit the exams are still quite relevant.

4.5 out of 5

Yes, with screen capture

Jeffrey Wooldridge, Introductory Econometrics: A Modern Approach (5ed or 6ed are used, but 5ed is recommended by the lecturer as 6ed has some errors within it). Great textbook, also relevant for ETC3410: Applied econometrics, but the lecture slides and study material are sufficient for this unit.

1x 2 hour lecture, 1x 1.5 hour tutorial

2018 Semester 2

97 HD

- This consisted of 20 MCQ's to be done in 1hr on a Thursday night. For me, despite feeling that I'd done well on this, I got caught out by a lot of careless errors leading to a much lower mark than expected. Therefore, the key here is to read every question carefully and ensure that you revise as much as possible for this.

- as long as you complete Part A of the tutorial exercises uploaded onto Moodle each week, then you'll receive 1% of your grade each week. These marks run from weeks 2 to 12, meaning that you can miss 1 tute and still receive full marks

- These are basically the tutorials where you go over the tutorial exercises provided on Moodle each week. Part A of the tute exercises has to be done at home and is not covered in the tutes as it counts towards your participation marks for each week. Part B is explained in the tutes, and normally delves into deeper concepts that are either mathematical proofs or explanations carried out in EViews which is the statistical software used in this unit only. Therefore, the tutes are a must to attend unlike the lectures where listening to the recordings as sufficient, as to obtain your participation marks you must attend the computer lab each week.

- this is where the majority of the course content is covered. Natalia delivers all of the lectures except for the first half of the first lecture which Farshid conducted. Originally I found it a bit difficult to understand her and what was going on, but eventually this made more sense as everything was able to clarified with extra revision.

- Groups were randomly allocated based on the computer lab that you were enrolled into. For each assignment you had to write up a report incorporating statistical information such as graphs, hypothesis testing and other factors taught throughout the course. Assignment 1 had a far more formal structure compared to assignment 2, where you were given free reign to write whatever you wanted and no word limit as long as you kept everything statistically relevant to the goal of the assignments. Assignment 1 was more content related, whilst assignment 2 was application based in the finance world by looking at asset pricing techniques and seasonality in relation to stocks. Both assignments were marked leniently, as most groups that I know did well on both assignments.

- This consisted of some MCQs and short answer questions. The short answer questions included some derivations, which were explained in-depth in the lectures themselves. Some of the phrasing in the exam itself was a bit difficult to comprehend, however I found that with careful reading and proper revision before the exam date, as well as attempting past exams that I was able to do quite well in it, contributing to my good result at the end.

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This was quite a step-up from ETC1000, so for anyone doing this subject, in particular as it is compulsory for any commerce specialist degree, finance major or econometrics major, then a good background in statistics will help you to do well in this unit. For me, I had 2 things that kept me going throughout the unit outside of lecture notes to do the best in the unit that I could.

1. Setup a group study chat on FB messenger. Originally someone posted on Moodle asking for people's facebook names, and the group was only at 4 people. However, that eventually reached 100 people, which was great as you had a network of people willing to answer questions for everything econometrics related, whether it was tedious or not. This is something that I will be using in all of my econometrics related units in the future hopefully.

2. Ben Lambert's youtube videos saved me quite often in the course. Whenever I zoned out due to my lecture slot being a Wednesday 8am, I referred to the videos here to ensure that I thoroughly knew what the course was speaking about. Also, each of his videos are short and succinct which is great as they summarise parts of the course away from a 2hr lecture. Be mindful though that not all the proofs are given in these videos, so be sure to watch the lecture recordings to memorise those proofs and write them on your cheat sheet to bring into the exam.

Also, a double-sided A4 handwritten cheat sheet was allowed into the exam. This saved me quite a bit, as I was able to write-out all the necessary derivations (except for one, which I missed ) that were covered on the exam. The cheat-sheet also means that less memorisation is required, as you can refer to the cheat-sheet to help you as you go in the exam.

Overall, despite struggling with some of the explanations at the start of this unit, I loved the unit even though it took away a lot of my time to study for other subjects. It introduced the OLS framework and is a great background unit to do before continuing with further econometrics units in the future, if that is your preferred pathway.

Professor Farshid Vahid - also the Chief Examiner of the unit. Really nice and approachable guy, who clarified content whenever necessary and helped out as much as he could
Dr Natalia Bailey - only lecturer for the unit in terms of content. Knew her stuff well but explanations weren't always succinct, so it helped to listen to the lecture recordings to clarify as much information as possible.

The past 4 exams from 2016 & 2017 were provided, which helped to understand the content, but not the structure of the final exam due to the course changing quite significantly this year

4 out of 5

Yes, with screen capture

Introductory Econometrics: A modern approach (5th edition) - there is also the 6th edition, but due to a typesetting error it's not recommended to purchase. Therefore, this is the only prescribed textbook for the unit, but I didn't find it necessary as I only used it a couple of times. Also, the pdf to the textbook is freely available here, so even if you feel like you need the textbook the PDF is always around.

Semester 1, 2018

80 HD

10% Assignment 1, 10% Assignment 2, 10% Weekly Homework Submission, 10% Mid-Sem Test, 60% Exam
Up to 5% bonus final marks for helping your peers in the moodle forum!

Assignment 1/2: I hated them so much. They were group assignments where you work with the same group for both assignments. My problem with them was that the task was very vague in that you are given data about a topic (we did race and gender inequality in pay and the housing market) and you had to write a report where you outline information about the data, justification and process of developing a suitable regression model, and inference and conclusion. My tutor encouraged us to work with other groups. I was constantly asking other groups for help because I had no idea how to progress. However, as long as you put some effort it, it seemed to be fairly easy to get some decent marks as average mark for Assignment 1 and 2 was 82 and 75 respectively.

Tutorial/Comp Lab: Every week, you're given tutorial work based on last week's content to do to show your tutor in next week's tutorial. As long as you have done it or attempted it, you get the mark. Each week's work gets you 1 mark so it's basically an easy 10 marks. As there are 11 weeks, you can miss 1 and still get full marks. Each week there is assigned tutorial work to complete. Tutor will guide you through the questions as you follow along using the program 'EViews'. Occasionally, tutors will comment on some of the content and discuss things that students often get wrong. This really helps with the assignments and exam. Stuff my tutor has mentioned has allowed me to score marks I wouldn't have known otherwise.

Mid-Sem Test: 25 MCQ in week 5. Sometimes they'll have subtle wording which can easily throw you off the right answer. Average mark was around 16/25

Content: The first few weeks were awful for me. I found it to be very confusing and difficult to wrap my head around content. Unit begins by teaching regression and introduces you to the 'OLS Estimator'. There are some maths involved which makes it even more confusing. 2nd half of the content involves time series data and forecasting, still using OLS. I feel that I only really got a good understanding of the first few weeks of content at like week 10! Definitely found content easier to understand near the end of the unit which felt rewarding, however, it does take a lot of effort.

Lectures: Because of inconveniences, I mostly skipped the 1st lecture and watched it online but attended the 2nd lecture. Not the most entertaining but was very helpful in developing my understanding of the content. If you can't attend, just watch them online.

Exam: 4 sections worth 15 marks each (60 total, worth 60% of final mark). 1st section is MCQ and each section focuses on a particular topic. e.g.
Section 2 tends to focus on cross-sectional data, section 3 and 4 focus on time series, forecasting, etc., basically the 2nd half of the unit. The past exams give a strong indicator of the format and types of questions asked so definitely do them.

Overall, fairly difficult subject which is unfortunately a core for all commerce specialist students and is quite a big step up from ETC1000. In the end I did find it mildly interesting, but for the average person who won't, I recommend avoiding it if you can.

Farshid Vahid and Natalia Bailey. Farshid lectures for the first 5 weeks then Natalia takes over for the remaining 7 weeks

Yes, they provided the previous 3 semester's actual exam, all of which included a rough marking guide

3 out of 5

Yes, with screen capture

"Introductory Econometrics: A Modern Approach 5th edition." Apparently the 6th edition introduced errors so they recommend the 5th. It is prescribed where they say you're disadvantaging yourself by not having it. Personally, I rarely used it. It did help clarify a few things but overall, not that necessary as long as you utilise your other resources well.

2x1 Hour Lectures, 1.5 Hour Comp Lab

Sem 2 - 2017

90 HD