AY2015-2016 Semester 1 Module Review

I am very grateful that I got over 30 MCs with results that isn't too far from my expectations. Actually, 30 MCs wasn't as bad as I thought especially after I submitted my thesis. Since I only have 15 MCs to do a review for, I shall try to be even more detailed this time round.

EC4103 Singapore Economy: Practice and Policy

I have much grievances over this module. It is nothing but a burden. Ask me to name anything I learnt from it and I won't even be able to think of a single thing. Probably how to follow instructions and carry out the commands in Stata? Right, so this module was introduced this semester as many employers have given feedback that NUS EC graduates can only regurgitate theory but aren't so well-versed when it comes to knowledge of the Singapore economy. Although this may be true to a certain extent, this module is definitely not gonna help much in that aspect. I honestly think the way the module is taught needs to undergo some serious revisions.

How does this module work? There are 5 lecturers and each of them assesses 20% of the course. Depending on the lecturer, there could be 2 or 3 reports to hand up over that 2 or 3 weeks. If not, presentations had to be made. Occasionally, presentation slides had to be submitted as well. This cycle continued and I think there were about 8 written assignments in total over the entire semester. Every assignment had to be presented and so on top of the additional presentations, there were a total of about 10 presentations. Initially, the module started out slow and steady with the report having a page limit of two sides. Then it became worse with each lecturer, with the last lecturer allowing a page limit of 5 to 7 pages. There were 5 themes in total. Aamir Hashmi took the first one and it was, in summary, on the macroeconomics of Singapore i.e. the characteristics of its economy etc. Danny Quah was next and took the part on growth if I didn't remember wrongly. Then came Ivan Png who taught Singapore productivity. Up next was Jessica Pan who taught income inequality. Last was Chia Ngee Choon who took the aspect of Singapore budget policies. Or was it taxation? I can't really remember but it was something along those lines. 

The bottomline is this: lectures are pretty much useless if you're talking about scoring for this module. Actually, even for the purposes of learning, the lectures still remain useless. The thing is the assignments had absolutely nothing to do with what was taught in lectures. The only thing that you need to know is how to surf the net and look for relevant information. So it wasn't surprising that the turnout for the lectures was just getting more and more pathetic by the week. I did not attend lectures at all but still managed to get away with a decent grade. The assignments by the first two lecturers started off as generic essay-based questions so you could say that it's pretty much a test of general knowledge. Then they became Stata-based with the next two lecturers. And the last lecturer's was simply basic calculations using Excel. Needless to say, the assignments of these three lecturers required students to obtain the correct results as well as to analyze the results. For Ivan Png's, all we had to do was simply to follow the step-by-step instructions in the assignment using Stata and then analyze the regression results. Jessica Pan's part was a lot harder in comparison as hers was not so straightforward so a lot of people had difficulties. But the tutor (Kelvin Seah) was always ready to help and you can actually check with him if the graphs and results you got were correct. And this was important as correctness is a major determinant of your score for Jessica Pan's segment as is the case for Ivan Png's as well as Chia Ngee Choon's parts though the other two lecturers weren't so flexible with the validation of answers.

This module consumed a lot of my time every week but really, I got nothing out of it. It came to a point that the workload was so heavy that I found the completion of assignments and preparation for presentations becoming increasingly burdensome. Among all the 5 themes, I picked up pretty much zero stuff. Before I took this module, I know so much about Singapore. After completing this module, I still know as much about Singapore. That's how helpful the module had been. By the way, this module is fully groupwork. After being assigned a tutorial group, you will be assigned to your groups. You don't get to choose your group members. So if you and your friends are in the same tutorial group but do not have different starting letters for your names, then you don't have to think of being in the same project group. Cos how they assigned the groups was a no-brainer. They simply went by alphabetical order and grouped say, ABCD together in one group, EFCG in another and so on. Anyway, this is pretty crucial. Your grade is highly determined by peer review as much as I thought it didn't play a big role. After submission of each report, your group members are supposed to rate you and not to worry, it's confidential. I gave high ratings for my group members but it ended up that two (out of three) of them got a grade lower than me.

Result: A-

I was surprised by this grade as it exceeded my prediction of a B+ or worse. I am really thankful to my group members for giving me good reviews (as they said). Of course, that comes with a price as I was doing a lot of the work. Then again, I thought we allocated the work quite well as everyone was doing what they're good at. This is not to say that I think A- was deserving of the work we had put in. Seriously, considering how much effort this module requires, I think I should have gotten an A in return. But this module is the sort that you put in 10 times more effort than what you get in return. Hopefully there will be some changes to this module for the next batch as I don't think the majority would like to experience what the Year 4s had just gone through.

EC4301 Microeconomic Analysis III

This is supposed to be a level 4000 module. It is also supposed to be a core module initially. Something wrong must have happened midway which caused it to become so unworthy of a level 4000 module in terms of both workload and content. Anyone who has gone through both EC4101 and EC4102 will definitely tell you that EC4101 is nothing compared to EC4102. And true enough, EC4301 is pretty much peanuts compared to EC4302. This module was taken by Chen Yi Chun this semester. And it's not that he can't teach difficult stuff but that he wants to tone down the difficulty due to past bad experiences. He's a pretty good lecturer and I think his lessons are surprisingly not mundane even though the content may be. The module started off hard as we were taught all the abstract stuff in consumer theory. Actually, abstract may be too strong a word for some. It won't actually be if you have been exposed to MA modules. Then when everyone thought they were done for for this module, the lecturer's announcement that midterms and finals weren't gonna test all these proofs came as a huge relief.

Topics
1.   Preference and Utility
2.   Utility-Maximizing Problem and Its Solution
3.   Utility-Maximizing Problem and Expenditure-Minimizing Problem
4.   Law of Demand, Exchange Economies, Pareto Efficiency
5.   Core and Competitive Equilibrium
6.   Welfare Theorems and Existence of Competitive Equilibrium
7.   Choice under Risk
8.   Externality
9.   Asymmetric Information
10. Modelling Strategic Behaviours I
11. Modelling Strategic Behaviours II
12. Incomplete Information and Efficient Mechanism

Weightage

Homework: 20%

Midterms: 30%

Finals: 50%

The homework was given out on a weekly basis and they were to be completed in groups of four or maximum, five. It wasn't graded based on correctness but rather, completeness. So I should expect that no one lost any marks from this component. There was also only one tutorial presentation per group. So the workload for this module was real light.

Midterms was tested up to Topic 5. Topic 6 was covered but not tested and its lecture was conducted during the recess week. Although this topic wasn't compulsory, it turned out to be useful for midterms. If you had attended the lecture or studied the notes on your own, it would help in scoring for your midterms. So take it with a pinch of salt when the lecturer says it's not tested. I disliked the topics after midterms especially Topics 8 and 9. They were way too simplistic for a level 4000 module and it wasn't value-adding in any way especially Asymmetric Information. Up till now, I still haven't been exposed to proper modelling of asymmetric information. All I understand are the implications of asymmetric information. Topics 10 and 11 are on game theory. Topic 12 was centered around mechanism design but its basis came from game theory. Mechanism design was a real interesting topic but too bad, it had to be so brief in this module. I scored 95 for my midterms. Average was 85? I can't recall exactly. The highest was 98 out of 100. I scored 95 probably cos I was pretty good at the first half of this module. Edgeworth Box was covered for Topics 4 and 5 but of course it wasn't as lame as what was taught in EC3101. Then again, I attribute the 95 I got mainly to the lecturer. I actually made many mistakes but he was amazingly lenient with his marking. As long as you showed some understanding of the concepts, he will give you credit. So keep this in mind if you're taking the module under Chen Yi Chun: do not leave anything blank. Actually, midterms was rather insignificant as it was upon 100 and when you convert the marks to 30%, it becomes negligible. The lecturer said it himself that people who scored well for midterms usually didn't score well eventually whereas those who didn't score well for midterms are the ones who fared well eventually. As much as there may be some truth to that statement, I personally think that midterms are such that you should get at least slightly above average if you are looking to get an A- or above. Nonetheless, I don't dispute the fact that finals is the determining factor and my grade is an affirmation of that as well. By the way, both midterms and finals were open-book though it wouldn't have helped much.

It was claimed that finals is cumulative but trust him not. On the whole, finals was easy. I got screwed up thanks to the steep bell curve and mistakes I shouldn't have made at all. As the second part had three chapters on game theory, it was no surprise that there was one entire question dedicated to game theory in the finals. Even though having taken EC3312 may be a plus, it wasn't as much help as I thought. Basically, game theory in this module was a lot more simplistic than in EC3312. Anyway, as with EC2101 and EC3101, the questions in the finals are such that it carries an overly high weightage. For instance, I could be given 10 marks for just a three-liner solution so it was pretty hard to gauge what was required of me especially when certain questions were quite open-ended.

Result: A-

I don't know what to make of this grade. After midterms, I was aiming for an A. After finals, I was realistically expecting a B+. I guess knowing your concepts well is the key to scoring for this module.

EC4303 Econometrics III

First and foremost, this module isn't as difficult as most may imagine it to be. There was more breadth than depth. Honestly, I question the use of econometrics exams at times. Here's the thing. You don't actually have to know how to prove theorems. All you need to understand are the concepts and properties of the theorems as well as how to go about applying them. But unfortunately, and I don't blame the lecturer for this, econometrics exams usually involve quite senseless questions. Anyway, this module was taken by Tatsushi Oka and he takes the definition of politeness to the next level. The topics covered for this module were quite impromptu in the sense that the later topics were chosen only because some of us were doing on these topics for our term paper.

Topics
1.   Ordinary Least Squares Estimation
2.   Linear Probability Model / Maximum Likelihood Estimation
3.   Nonparametric Regression
4.   Quantile Regression
5.   ARMA model
6.   GARCH model
7.   LASSO
8.   Panel Data Models
9.   Program Evaluation I: DID Estimation
10. Program Evaluation II: IV Regression
11. Program Evaluation III: Regression Discontinuity
12. Classification and Regression Tree

Weightage

Problem Sets: 20%
Term Paper and Presentation: 20%
Book Presentation: 10%
Participation: 10%
Finals: 40%

There were 3 problem sets and all of them were easy. They were graded based on effort and correctness. Most people scored 100 or at worst, 99 so this component wasn't gonna make any distinction among the students.

The term paper was to be done in groups of 3. You get to choose your groups. Basically, you had to find an existing paper, replicate the results and extend the paper. There were 2 presentations for this component. The first one was a 5-minutes presentation and it only involved presenting about the existing paper as in the type of dataset etc., the results your group had replicated and lastly, an outline of the extensions. The second one was a 10-minutes presentation and you had to present on the extensions your group had come up with. The report was only limited to 3 pages excluding appendices and all so it was pretty slack. The draft was due sometime during the middle of the semester whereas the final one including probably the last one and a quarter page on extensions was due sometime in Week 12 or 13.

Book presentation. This is probably the one and only useless component of this module. First, we were assigned a textbook, Statistical Learning from a Regression Perspective by Richard Berk and this textbook is considered pretty advanced for undergraduates. All the equations inside weren't what you normally see in an econometrics textbook. Second, this textbook was taught with students making presentations in their groups so every week, there would be one presentation until all 8 presentations were done. The chapter that you present on was decided by drawing lots. So some got the earlier chapters which were easier while those who got the later ones might need to spend a bit of time understanding the earlier ones before they could do their presentation slides. The presentation was up to 25 minutes and all 3 group members would present a section. I think during this weekly presentation, everyone was just shutting off their ears and that included me. The thing is some presentations were so poor that you could tell they didn't really understand the content themselves which I perfectly understand why. I really question the use of this component as the presentations not only consumed time but also, the book chosen is a bit too advanced for self-studying as its content is very different from what you usually see in econometrics.

Don't worry about the component on participation. You can get it just by attending all the lessons.

I actually only realized finals is 40% as I typed the weightage. But finals is the determining factor to your grade for this module. Oka was very generous with the 60% CA component as he said that everyone would probably get full credit for it. Finals was open book and there were some questions that we could directly refer to assignment solutions or lecture notes to get the marks. Then again, I thought the differentiating factor came in in the last 2 questions which added up to a total of 16 marks. I probably screwed up more than half of that 16 marks but judging from my grade, I believe most people did as well.

Result: A
This grade came as a big surprise as I was expecting at most an A-. Even though this module was more of breadth and it was a lot less rigorous than I thought, I think it's pretty much a must-take if you're looking to go into specialized economics jobs. Anyway, knowing more econometrics models is always a good thing for someone studying economics. This is not to say that you should take it if you're afraid of screwing up your CAP. I think I just managed to scrape through an A as it was really competitive with the cohort size of 20.

AY2014-2015 Semester 2 Module Review

I finally have some time to sit down and start writing this. The reason being I've been given a lot of work from Dr. Song whom I've been working as a research assistant for for the past one year or so. And on top of that, I've been working out a general direction to my thesis with my supervisor. You're probably wondering why I'm starting so early. That's just cos' I'm intending to overload to 30 MCs next semester. So it helps to spread out the workload a bit. Now you think that all sounds good but the fact is I'm only in the preliminary stage of finalizing my scope.

Now, back on topic.

Basically, I did only average this semester. And until now, I'm still not sure where I have gone wrong for two out of my four core modules. So this time round, I'm gonna take a different approach to my module review instead of my usual practice of following alphabetical order and starting with EC.

I'll first talk about the most unimportant module and that is SSA2211, a module which I can literally count with my fingers the number of hours I've spent on it in the entire semester. Then I'll move on to talk about the two modules that I've screwed up and which I do not know the exact reasons for why I did. So the disclaimer is that what I will say might turn out to be quite subjective. Finally, of course comes the two remaining modules that I know why I've screwed up or done well.

To sum up, the order of my module review will be the following: SSA2211, ST3242, ST3247, ST3239, EC4332.

SSA2211 The Evolution of a Global-City State

I would say that if you are someone who likes to think of already-defined history from a different perspective, then this module is definitely one for you. Norm has it that Singapore's history begins with Raffles' arrival or ok, maybe Sang Nila Utama. But this module takes you back to the 14th century and tries to argue why Singapore's history should be defined from that period onwards. Or at least I felt that was for the most part of the module. Then subsequently, the module unfolded according to the timeline on Singapore, moving onto Singapore's interaction with the Malay world and then onto the modern history and so on.

Personally, I find that as much as I was not the least interested in this module, I must say that it's quite an avenue to invoke your thinking to some extent. It makes you think of things in a way that you wouldn't have thought of. It's not your straightforward SS module or what you might have expected it to be. So if you're really keen on this module, chances are you'll enjoy it.

Workload wise, it wasn't too heavy or maybe because I didn't put effort into it.

Weightage
Tutorial attendance and participation: 20%
CA1: 20%
CA2: 20%
Finals: 40%

As typical of all arts modules, the first component wouldn't be easy to secure if you hadn't participated in tutorials. I, for one, chose to take this module because I saw that we'll only have four tutorials in total and that one of them was e-learning. Moreover, I missed one of them as well though I had a valid MC. So it's like my goodness, the last tutorial was the second and also the last time I was meeting the class. And of course, I did not participate at all so I'm pretty sure I lost at least half of this component. On another note, I am making an objective viewpoint by saying the tutorials are useless. Really, I think that the debate on the IVLE forum made so much more intellectual sense than the tutorials. Or maybe it was because my tutorial group was a bit passive to begin with and my tutor didn't seem to have much experience which explains why there weren't any insightful points raised during class. I'm not saying I read what people wrote on the forum because the truth is I didn't. In fact, I only knew the forum is active because I subscribed to it. But I did scan through the first few lines of the post when I received the email from IVLE. This was sufficient enough to give me an idea of what the post was roughly about so I can safely say it's a lot better to engage in the online discussions rather than the tutorials.

The second component comprises of an MCQ component and two structured-questions component if I may put it in my own words. The former made up 20% while the latter, 80%. And the 20% is free. I do mean free. Because you get to have a total of two tries for that component and after the first try on IVLE, they'll actually point out your mistakes and tell you the right answers. Oh yes, I didn't type that wrong. It was stupid but I guess in some way meaningful because that 20% was there to help you for the 80%. You basically get a clearer direction as to how you should answer the two questions for the 80% with those answers you got from the MCQ.

I finished this task just before the deadline which was the Sunday of the week of CNY. So you had to complete this by 2359 before it turns Monday. And after a delighted week of slacking, I was rushing this damn thing and finished it under 3 hours when it was recommended that we start two weeks ago when the assessment was released. Right but that came with a price as I only got a mere 72 out of 100. Most people were in the 70s range by the way so that essentially put me in the B range already.

Then came the second component which was due on the Monday of Week 12 if I didn't remember wrongly. Oh did I mention that this module advocates short and sweet instead of long and "detailed"? This was an essay component where you had to choose to stand in the shoes of one of the following: a Peranakan trader or merchant I can't remember, someone from a... Ok forget it. I have the full question paper so for those who are interested, you can get it from me. So you had to make references and stuff and was limited to 500 words I think. Oh but heck, I once again rushed this on the night before. As to the grade I got, I never knew as I was too lazy to go back on the Week 13 Friday to collect this back. But I do know from my tutor that I did ok. So I guess it was average.

And finally, finals. I read the notes for 2 hours or less and went to sleep in preparation for my first exam of the semester but I didn't feel like my exams have started cos I knew I was sitting for a module that I'm gonna have to use my S/U for. You'll be given a timeline of events in the exam that briefly touches on what has happened over the many centuries. Well, it'll be useless if you hadn't studied cos you wouldn't be able to make much sense of it like what I experienced. So I was just staring at the questions and deciding which one I could recall the most information for.

Result: B- (S)
Disappointed. I expected a B. But B or B-, still an S eventually. All I have to say for anyone who wants to take this module is that it's no use doing rote learning. Besides familiarizing yourself with the facts, it is also equally, if not more, important to begin questioning yourself and thinking more deeply into the issues. And one tip is that there're always some questions at the start of each tutorial. I mean they're printed on the tutorial itself. So print them out and start answering them. They'll serve as a very good preparation for finals.

ST3242 Introduction to Survival Analysis

This module was taken by Anthony Kuk. And he has improved greatly from when I took ST3131. But that still didn't stop me from not attending lectures. Tutorials are combined with lectures. So instead of two 2-hour lectures each week, it went down to one 2-hour lecture plus one 1-hour lecture plus one 1-hour tutorial. That also means the tutorial was personally conducted by the lecturer. Lectures weren't webcast but I think his notes were detailed enough.

I really kinda like this module a lot as much as I'm disappointed with the final grade. If I were to draw a similarity to a foundational module, I would say this module is most like ST2132. ST2131 only came in in one of the last few chapters and in the form of moment-generating functions and iterated expectations so it was a very small part. But the thinking was most similar to ST2132. It involved MLE and all. There was a lot on MLE but it wasn't just MLE alone. When it got to the semiparametric Cox model, it was more of computation of rank likelihood. Basically, I guess you could say that this module focuses a lot on how you would model survival data. Maybe model is too strong a word for technical dudes. I guess you can say it touches mostly on regression, or along that line. The thinking is very similar. I mean when we do regression, we think of whether the assumptions of OLS, to take the simplest case, are satisfied and what the solutions are if they aren't. A similar thing is done in this module. For example, in applying Cox's model, we assume proportional hazards. But what do we do if the assumption is violated? Also, you sort of see the realism in this module where we separate out different groups of individuals into stratas and assign a unique characteristic to them before carrying out analysis in R. And talking about R, this module uses a lot of R such that you begin questioning the presence of certain topics with regards to examination purposes. Then again, that's what makes the module more realistic. You take the data to a software, look at the coefficients and begin thinking bout' its implications for your model. 

Weightage

Assignments: 20%

Midterms: 20%

Finals: 60%

There were two assignments, one before recess week and one before reading week. The assignments were very easy to score. I got full marks for both of them along with some 95% of the cohort so that tells you a lot. 

Midterms was really easy too. I thought it was a test of time and accuracy rather than knowledge as everything was just straight from the notes. It also helps that it's open-book. I got 59/60 with 1 mark lost because I did not simplify something. But I think there was a total of 8 or 10 people who got 59 or 60. The median was around 43, very much to my surprise. I think a lot of it was due to carelessness.

The finals was also open-book but it was obviously a lot more challenging compared to the assignments and midterms. It required thinking and good understanding of concepts. There were altogether 5 questions and each was worth 20 marks. Only one of them was a blatant giveaway. 

Result: B+

I was extremely disappointed with this grade. I thought I did fairly well for the finals but apparently, I did not. It was weird because I could answer questions that others couldn't but I made mistakes in the last question which was probably the one that cost me my grade. Plus, maybe the proof that I gave in the first question wasn't foolproof enough. Well, so this is one of the modules I had no idea why I scored this way. Up till now, if you ask me, I still can't give an answer. I'm actually quite curious myself because I tend to reflect on why I did badly for a particular module and up until before this semester, I could always give myself the answer but this time round, I really can't. 

I guess maybe there were a whole lot of others who found the paper manageable or even easy. I can't say that this paper was hard but I definitely wouldn't say it was easy either. Before the results came out, I was realistically expecting an A- for this module but turns out it was even worse than what I thought.

But what I suggest is not to take the concepts at face value. Rather, try and look deeper and start appreciating them and their applications etc. All in all, I did not regret taking this module as it was interesting and for anyone intending to specialize in Biostatistics, taking ST3242 is sort of a must.

ST3247 Simulation

Once again, this is another module that I am truly disappointed with the final grade. It was taken by Vik Gopal. He's one hell of a good lecturer cos' he's always so patient and explains things very well. Probably the only issues with him are that he might be slower in replying emails and to me, it was as if he was rushing through some of the topics which I thought he might as well use the freed-up Week 13 so as to go slower on the earlier topics. Then again, it's probably because I'm very slow as it was only the weaker ones who weren't able to follow him. That said, I did not attend both lectures and tutorials for this module. Lectures were webcast so it's okay. If you do not attend lectures, watching the webcasts is a must as there may be quite a bit of important information passed down during lectures; things like how you should answer a certain question. And you could find yourself staring blankly at the notes without the explanations given during lectures.

I regretted not attending the tutorials. The reason being I was really so slack the past semester that I had trouble keeping up with the tutorials. So it became a snowball such that when the class could already be at say, tutorial 5 during Week 8 and I was only at tutorial 3 at most. It was really bad. To digress a bit, I think I owe my laziness to the lack of breaks from the start of last Semester 1 until the end of Semester 2. As I had to do an internship right after the end of finals of Semester 1 and only ended it before the start of Semester 2, I really lacked a break in between. To make things worse, Semester 1 was already pretty physically taxing on me as I often had to stay up late in order to finish up the work on my RA side. And I have neither the mental capability nor the discipline to pick up a pen and start doing work this semester. But really, the tutorials were useful in my opinion as there were some questions in which you really needed an explanation from the lecturer. Oh, I did attend the very first tutorial and Vik provides extra insights into the more challenging questions which makes you understand better. 

And again, if I were to draw a similarity between this module and a foundational module, it would most definitely be ST2131. If you're not good at ST2131 and do not want to screw up your CAP, then avoid this module. I was not good at ST2131 but I decided that this module was gonna be useful. It first started out with how one can simulate a random variable from discrete and continuous distributions. This part of the module was hard for me because I wasn't good at my foundations from ST2131 so most of the time, I find the methods that Vik's teaching very out of the blue. It's like I would never think of such hoo-ha methods. Then came monte carlo and after Week 7, it was my favourite and most challenging part of the module, discrete event simulations. So everything you learnt bout' simulating from distributions comes into play. You're given a real-life situation. For example, the classic one used in this module is that there's only a single server at say, a bank. So you had to find out the arrival time, waiting time and departure time of each customer. To take it to a more complex level, the customers would leave after a certain waiting time according to a distribution you have to simulate from as well. Nevertheless, all these are already very simplistic scenarios.

Weightage

Assignments: 20%

Midterms: 30%

Finals: 50%

There were 4 assignments in total. You had to submit two portions; one was the handwritten portion to be submitted to the office and another was the coding portion to be submitted on IVLE. The first two assignments had both portions. The third assignment had only the R coding portion while the last had only the handwritten portion. I got full marks for all the assignments but of course, that didn't help at all in pulling up my disastrous midterms. Nonetheless, try not to lose marks in the assignments even though the median is always not near the full mark. I mean the first two assignments were kinda hard but the last two were easy. Then again, all the assignments took up a lot of my time except for the last one on validation techniques.

Midterms was upon 40. And I can say I did not do well because I did not study hard enough. I do mean it. Because I went home to try the questions again and I easily scored above 30 instead of the mere 25 that I actually got. The median was 22. At that time, I slacked too much especially due to the festive CNY season. So I was rushing through all the tutorials before the midterms. I thought I understood them and I neglected non-homogeneous poisson process which so very well came out in the midterms. It was easy but I lost the marks due to complete ignorance of that concept. 

I was hoping finals will be hard with emphasis placed on discrete event simulations as I had practised quite a number of challenging questions from the textbook and was confident that this would be the topic which can pull my grade up. How dismayed was I when I saw that not only was the DES question easy, it was taken straight from the past-year paper as well. In fact, the paper was so easy that 1/3 of the cohort left before the one and a half hour mark.

Anyway, it's kinda strange; closed-book, no cheatsheets, no formula sheet. I mean there were really quite a lot of algorithms to remember coupled with its details. And that's just the part on algorithms. There were still some other formulas to remember for the later topics. Right, talking bout' algorithms, the emphasis of this module is on pseudo-code so you do not need to be well-versed in R in order to obtain a good grade. That said, if you wanna take it to reality, of course you gotta show a certain level of competency in R so as to make use of whatever you learnt in this module. Nonetheless, there's sufficient guidance from Vik to ensure that you can code whatever you were taught.

Result: B

I was expecting a B+ and I've got no other reason for why I got this grade except that the bell curve was too steep thanks to the finals. I can say this with certainty because Vik sent the review of the finals to us after he finished marking and I got everything that he pointed out as a common mistake right. I mean I really spent time to understand the concepts towards the end of the semester. But there was this question on alias method that I totally missed out a formula which cost me 4 marks and I believe that was the contributing factor to my grade on top of the mediocre performance for midterms. 

Even so, I do not regret having taken this module. I really enjoyed the part on DES and the flow was great because you could make sense of what was required given a real-life problem. You had to know how to simulate from distributions, apply it to discrete event problems then perform checks as to whether what you had assumed for the problems were right plus whether the results you got made sense in reality.

ST3239 Survey Methodology

This module was taken by Zhou Wang. You may have some difficulty understanding what he's saying due to his accent. For the record, I did not attend any lectures except for the first. Lectures weren't webcast. You might lose out a bit provided you can actually follow him during lectures. Anyway, he uploads the notes shown on the visualizer in lectures.

Different sampling methods such as simple random sampling, stratified random sampling and cluster sampling are among the topics that were covered. The only topics that may sound more foreign are ratio, regression and difference estimation as well as estimation of population size.

I can say without hesitation that ST3239 is definitely one module that I regretted taking. I gave bulk of my bid points to it in the hope that I would score well due to its similarity to ST2132. But not only did I not score well, it was not the least useful to me unlike ST3247. And I didn't find it interesting like ST3242. If there is one word that I need to use to summarize the content from this module, it would be "proofs". Lots and lots of proofs were provided. But it wasn't the focus of this module I guess. You know the thing is that most people do not follow theory when they do sampling. I don't know if it's because they're unaware of the implications of not doing so or whether the implications of not doing so are too blown up thanks to advocators of sampling methods. I'm saying this because I recently had to code block randomization in Stata as a task given by my RA Prof. And I tried to argue with him that doing the way he told me to would result in biased estimates. But the answer he gave was that as long as we perform a t-test and the p-value is high, it wouldn't matter much for a large sample. In fact, before I took this module, I will not think that I would ever have to use the knowledge I'll be getting out of this module in years to come. I mean I'm not gonna go into something that requires empirical work, not even for my thesis. And even if I do, it seems like EC people are kinda too, if I may put it bluntly, ignorant to even know about the existence of such theoretically-validated methods. Even so, it might not come to me as a surprise if anyone who has taken this module realizes that it's sorta unrealistic. But to be fair, for anyone who would like to work in fields related to empirical research, it's probably good to take this module.

Weightage
Tutorial attendance: 10%
Assignment: 30%
Finals: 60%

I've to say the workload of this module is really light. You just have to attend a total of 5 tutorials in the span of the entire semester to get full credit for the first component. As for the second component, it was actually meant to be a midterms but for some reason or another, it was decided that the midterms be changed to an assignment. The assignment was extremely easy which resulted in the median being 94 out of 100. I got 96 which was only at most a B+ grade. The finals was, as I mentioned in my previous post, extremely easy. Basically, this module was mostly just applying formulas from the notes. Nothing fanciful at all. In fact, everyone found it to be one of the easiest level 3000 electives.

Result: B+
I attribute the grade I got to the 20 marks I lost on the question in the finals which actually only required clear-cut copying from the notes. So if you had copied the proof one-for-one onto your cheatsheet, you'll be able to get full credit for that question. Besides, the lecturer did mention that the paper was found to be easy for most people but of course, it wasn't as well done as the assignment due to carelessness but it still doesn't change the fact that the bell curve will be really very steep. Nonetheless, the lecturer also said that there wasn't sufficient understanding of the notes so this may serve as a tip for anyone who wishes to take this module under Zhou Wang.

EC4332 Money and Banking II

I think the title "Money and Banking" isn't reflective of the content covered for this module if Martin Bodenstein is the one taking it. Instead, he geared the module towards monetary policy such that there wasn't much of banking at all. Nonetheless, he has very good understanding of the material he's teaching. I guess this is because of his work experience. You can look up his CV for what I mean. By the way, he's all good and well face-to-face but not when it comes to emails. He replies to an email once in a blue moon so most of the time, he doesn't. Anyway, if you find yourself interested in what I'm gonna say below, take EC4331 Monetary Economics and Policy next semester. According to him, it's because the EC department wants to move EC4332 back to the more qualitative line of content such that it'll be more of a build-up from EC3332 so they decided to create a separate module that focuses on monetary policy. And, for anyone who wants to specialize in monetary economics, this module is a must-take. I find myself reading papers on monetary policy with ease now as compared to when I first started back in Feb whereby there was nothing then that I could understand.

The New Keynesian Model is one of the most widely used models in analyses of monetary policies. And even if the papers you're reading happen to not be using NKM, chances are that once you study NKM, you'll find yourself equipped with the ability to understand most of the other models as well because the current trend is that most models are built on microfoundations just like the NKM. You can get a little bit of the NKM from EC4102 under Aamir Hashmi. But of course, it's not as full-blown as this. Speaking of EC4102, it'll be highly advantageous to have taken EC4102 before you take EC4331. Although EC4102 is a co-requisite for EC4331, I do think the relevance to EC4331 comes in only at the last bit of EC4102. This means it'll be better to have taken EC4102 before you take EC4331.

This module starts out difficult due to the sheer amount of so-called algebra such that you begin seeing stars in the third homework or something. But in the lecturer's own words, all the algebra you do in this module are trivial. What's more important is that you get the intuition of why a particular shock will change the economy in the way it did. In fact, that is also the most difficult part of the module for me: intuition. Until now, if you ask me, I may not be able to give a very satisfactory answer to some of the qualitative questions covered in this module. The lecturer probably recognized that and set the finals such that it focused a lot on qualitative understanding rather than quantitative understanding like in the midterms. And halfway through the semester, I started spending much less time on this module as there was really nothing much you can study. Well, but I guess the homework helps to keep up consistency.

Weightage
Tutorial attendance and participation: 10%
Homework: 20%
Midterms: 35%
Finals 35%

I do not know why the first component even exists in the first place cos' even if there were people who went up to present during tutorials, their names weren't taken down. But the lecturer did mention that everyone will probably get full credit for this component.

Each person was allocated 4 homework in total. How it works is that there were altogether 10 homework. And everyone would be assigned 2 among the first five and the remaining 2 among the last five. Of course, this once again comes with some sort of bias. I got allocated the more computationally intensive ones which seemingly, a lot of people had difficulty with. The last 5 homework were all fairly easy whereas the earlier ones were very time-consuming; I can spend up to 4 or 5 hours just to get them done. I got full marks for only one assignment whereas the others were all 9.5 out of 10. I guess it was already somewhat disadvantageous as some got full marks for all. Nonetheless, I don't think it matters much as long as you didn't get like 6 or something.

Midterms was quantitative in nature. There were 3 questions in total. It wasn't a test of difficulty but time. The first question was algebraically intensive and that took away 55 minutes out of the two hours we had but even past the one-hour mark, there were still a lot of people struggling to finish the first question. I got 95 out of 120. The median was 82.

And lastly, the killer finals. I must say, and I said this to the lecturer himself, this is without doubt the hardest EC paper I have sat for by far and I hope it'll be the hardest one already. Apparently, he said that he deliberately phrased the questions in a way such that if you do not know your concepts well, you wouldn't know what he's asking for. Basically, the finals was a test of conceptual understanding. There were two questions and I could do the first one with confidence. But I lost it halfway through the second one because there came up some weird variance which I've never seen before in the entire semester. And by the time I figured out what the variance meant, I could only do the 5 marks proving and not the 10 marks one. Most people around me either gave up or were panicking as well. It doesn't help that this is not the kind of paper whereby you could just move on to the next part if you did not know how to do the previous because it was a snowball which summed up to some 30 marks out of 120.

Result: A+
I was very pleasantly surprised by the result. Never in my wildest dreams did I expect to get even an A-, much less an A+. I've no idea how and why I got this grade. Seriously, I don't think I deserve the grade at all because in absolute terms, I actually didn't do well for the paper especially for the second question. I guess I got the grade only because I did well relative to others. In fact, the lecturer did mention in his email to us after grading the finals that our performance for the finals was a little worse than what he expected. This reminds me, many people have said that level 4000 modules have no bell curve. But I'm pretty damn sure that this module has and this on top of the small cohort size of 34 students just goes to show how intense the competition had been. With so many factors in play, it really got me wondering how I got this grade but I never figured out, not until I met the lecturer sometime later...

So he told me it's because I demonstrated very good understanding of my concepts in the finals. And there was apparently a very huge gap between me and the second best scorer; I do believe that the second best scorer was probably the one who got 102 for the midterms and 102 was the highest mark for the midterms. In fact, the 95 I got was only around the 75th percentile benchmark which essentially put me in the A- range already. I guess for anyone who wants to take EC4331, be prepared for a challenging finals though I highly doubt the finals will be as hard as this semester's from what I was told. However, if you know your concepts well, chances are you'll be able to do well for this module as the lecturer also mentioned that most people can follow the notes and keep blindly applying the method of undetermined coefficients, but then how many of them actually understand what they're doing? And before I forget, do not attempt to write long-winded solutions for the qualitative questions. I did so for one of the homework and I was deducted half a mark for that. It's preferable that the answers be short and straight to the point. Probably that was one of the contributing factors to my grade for finals.


And that's it for my module review this semester. By the way, I did not give a list of topics covered for each module this time round. But I do have the full set of lecture notes, assignments etc. for all my 4 core modules. However, I'm no longer gonna upload my stuff and I've my own reasons for doing so. So if you would like to get access to my course materials, drop a comment along with your email address and I'll try to respond asap.

AY2013-2014 Semester 2 Module Review

Well okay, besides having been occupied for the past month, I did very badly this semester so I needed quite a bit of time to get back up to write this post. Nevertheless, I shall try my best to remain objective so as to benefit the readers of this blog.

CS1010E Programming Methodology

This module was taken by Prof Joxan Jaffar. Apparently, he taught fairly well at the start of the semester, only to deteriorate drastically towards the end. The language taught is C. 

Weightage

Sit-in labs: 50%

Midterms: 10%

Finals: 40% 

This module was basically open-book for every single graded component though I should think that wouldn't help much for a computing module. The main topics covered were control flow, arrays, pointers and structures just like for any other introductory computing module. Sit-in labs were held every odd week (starting from Week 5 onwards) and take-home labs were assigned every other week. If you can do the take-home labs, you might probably consider skipping the lab sessions during the even weeks.

The format of the midterms was as follows: it was a multiple-choice paper comprising of 20 questions; each question has a string of code and you're supposed to interpret it and then choose the option that has the correct output. I actually found it pretty meaningless a paper by the way. But the questions were tricky; sometimes it might just take one semi-colon to make a difference in the solution so exercise extra caution and do not overlook any details. I only got 13/20 which is pretty much below the median I guess. But that is just because I totally neglected one or two topics and there were actually close to 6 questions on those topics so I should think it wouldn't be hard to get above 15. You'll know your midterm results very soon since they'll upload the solutions on the same day itself.

Sit-in labs got progressively harder; there were basically 5 sit-in lab sessions where each subsequent lab was worth 1 more percent than the previous one starting with 8% and ending with 12%. I got A for the first two (but really, anyone can get A for the first one) and B for the remaining ones. That essentially put me in the 50th percentile already. 

Finals was just, bad; pointers, structures everywhere. There were two sections in the finals. Section A was like the midterms just that it was no longer MCQ so that you had to write the output in the blank space provided. There were 15 questions on that. Section B was then testing students on their ability to write codes so as to solve the problems presented in the questions. Personally, I thought some of the questions were quite different from those in the sit-in labs but if you're good at computing, it shouldn't make much of a difference. I basically screwed up both sections and I gave up halfway to the extent that I scribbled some nonsense in Section A. That was how badly I wanted to end this and it doesn't really help that it was my first paper. 

Result: B-

Yes, first B- though it was kinda expected. My advice is to try and get As for all the labs or at least, 4 out of 5. That can secure you at least a B+, I hope. I actually didn't do much practice for this module except  for the take-home labs. In fact, I was quite passive in the sense that I believed practice would only take you so far for a computing module. I am just glad to get over this module which was pretty much useless as C is just obsolete already. If given a choice, I would have loved to take IT1006 but no, CS1010E is a core module for Stats majors.

EC3312 Game Theory and Applications to Economics

This module was taught by Prof Sun Yeneng who was seemingly from the Maths department in FOS. His lectures were so-so as most of the things in the notes were taken from the textbook. I gave up attending them after the first one. Textbook is essential: A Primer in Game Theory by Gibbons.

So there were 4 themes altogether: (1) Static Games of Complete Information, (2) Dynamic Games of Complete Information, (3) Static Games of Incomplete Information and (4) Dynamic Games of Incomplete Information.

Weightage

Tutorial attendance and participation: 5%

Assignments: 10%

Midterms: 35%

Finals: 50%

Basically, attend all tutorials and present once to secure the 5% component, There were 2 assignments, each worth 5%. Assignments were very easy so most people should have no problem securing full marks for them.

The bell curve for midterms was steep; the median was 27/35 and it doesn't help that there are a few PhD students taking this module. So I got only 29 but really, the midterms was actually very easy. The reason why I didn't get above 29 was because there was this particular concept that I didn't clear up before taking the midterms as I started studying only one or two days before the test itself. But the concept itself isn't hard so actually, it shouldn't be a feat to get above 30. Midterms are returned during the next tutorial.

I screwed up the finals especially those questions on Bayesian Nash Equilibrium. Although I could have presented my answers in a more detailed fashion, I don't think it would have helped much as I was pretty weak in that concept itself. The thing about game theory is to really appreciate the big idea. When I was studying for this module, there were many times I had to question myself why this or that has to be done. I just couldn't see the importance in certain things. I mean in reality, most firms aren't going to say, "Ok, so let's formulate our strategies and then work out the Nash equilibrium." It just defeats the purpose of game theory. I took this module actually to train my logic and learn to understand from the perspective of a firm. Unfortunately, I thought there was way too much focus on theoretical concepts so that I didn't actually reap much from this module.

Result: B+

B+ was not shocking given that there were probably only a little over 50 people taking this module and that this module didn't really come as intuitive to me. My advice is that divert more attention to the topics covered after midterms as those will be the main focus of the finals. Well honestly (and I'm not saying this cos' I didn't do well), this module is close to useless unless you want to go on to higher-level modules or do your thesis on something related to game theory. I mean the emphasis on the theories of Nash Equilibrium was just way too much such that I don't see the realism in this module.

EC3333 Financial Economics I

This module was taught by Prof Lu Jingfeng whose English may be a bit hard to comprehend (I seldom comment on such things but if I have to say it, that just means something). But then again, if you don't go for lectures... 

And so this was one module I really disliked and for the first time, I actually regretted taking a module. I thought nothing could get worse than micro in terms of the level of interest but this was just many times worse than micro. Aside from the fact that everything was being thrown at you without any avenues to enhance your understanding (I tried to look for a book that derives all the theories and formulas but to no avail, neither was asking the Prof a solution as well), this module was way too technical for my liking. So it's just formulas and graphs and formulas again.

Topics covered are Optimal Risky Portfolios, CAPM, APT, Bonds and Options. I personally think the textbook was bad (no derivations) but still necessary for this module since the notes are mainly slides created out of the content in the textbook: Investments and Portfolio Management by Bodie, Kane and Marcus, 9th Edition. Note: Investments by Bodie, Kane and Marcus, 9th Edition is almost identical and can be used as a substitute.

Weightage
Tutorial Attendance and Participation: 15%
Assignments: 15%
Midterms: 30%
Finals: 40%

The first component is supposedly easy to get, just attend all tutorials and participate three or four times in class. Similarly, the second component is supposedly easy to get as well but unfortunately, I did not get full marks for the last two assignments. Basically, how it works is that each student gets allocated 3 assignments at random so some may get the ones in the earlier weeks and some may get a mixture of those in the earlier and later weeks. Well, the impression it gives is that it might be somewhat unfair since those who got allocated the tutorials covered in the earlier weeks (especially before the midterms) have it easier. But really, I think the assignments are all easy and given enough understanding, one should be able to score full marks for all of them.

The midterms was disastrous for me. I got a mere 21/30 when the average was probably around 25.5 to 26. Reason: This exact same question from the practice midterms came out and I screwed it up. Yes I did not practise the midterms but aside from that, I think even if I did not, I should have been more careful and have a deeper understanding of the concepts involved. Then there was another question in which I got the correct answer at first but having been too paranoid, I decided to act smart and write something else so I got that wrong too. In fact, there was only one 'differentiator' question which oh, I happened to answer it wrongly as well. That, I can only blame myself for not understanding the earlier chapters thoroughly. In fact, I think the midterms is really doable and it wouldn't have been hard to get at least 26 or something along that line. Anyway, midterm scores will be uploaded onto IVLE once grading has been done.

Finals. Finals was apparently easy since I got a B and I thought I did pretty well. So it's either I underestimated the cohort (doesn't help that there are a few DDP students taking this module) or I overestimated myself. I think it's a mixture of both. Proofs can be easily found in the textbook and yes, contrary to what I thought, many people actually read them. Other than that, it's all just simple calculations and manipulation for one question.

Result: B

ST2132 Mathematical Statistics

Well, the first thing I must say is that as intimidating as Prof Lim Chinghway looks, he's one heck of a good lecturer. He's patient, willing to help, explains concepts really clearly and would even repeat if the class did not catch it. Possibly almost everything you would want to see in your lecturer.

The fact is that this module is very easy compared to ST2131. Like I said, Prof Lim toned it down a lot so that it transformed from a killer module to a CAP-puller for most people I guess.

The big topics covered included Simple Random Sampling, Parameter Estimation (MOM and MLE), Fisher Information, Efficiency, Sufficiency, Hypothesis Testing, Generalized Likelihood Ratio Test and the Comparison of Two Samples. Textbook isn't necessary at all since the notes are sufficient and tutorial questions are from there as well but you could still get it if you want: Mathematical Statistics and Data Analysis by John Rice, 3rd Edition.

Weightage
Tutorial Attendance: 5%
Tutorial Participation: 5%
Assignments: 20%
Midterms: 20% (graded on completeness and also, correctness from Week 2 or 3 onwards cos' they found a grader)
Finals: 50%
Bonus: 5% (for participation either in class or on IVLE Forum)

The first 3 components are free marks so there's no need to talk about them (because you are actually allowed to make changes to your tutorial answers when your tutorial mates are presenting their answers).

I screwed up midterms as usual, got only 24/40. Average was 22 or something. The questions were easy but one of them was pretty unprecedented. But on overall, I must say the Semester 2 paper was harder than Semester 1 paper (and much less people take ST2132 in Semester 2). Even so, it was still doable. Highest was 38/40 by the way. So the S.D. is pretty high. The midterm score was uploaded onto gradebook before the scripts were returned to students during tutorial.

Finals was easy as well. Know your concepts well and trust me on this, copy down all the probability mass or density functions onto your cheatsheet. You'll need it during the exam. With that, you can easily get a decent grade provided you know what you are doing.

Result: A-
Probably got saved by the finals.

ST3131 Regression Analysis

Personally, this is one of the hardest Stats modules I have taken. The level of understanding involved in this module on top of the derivations is not trivial. Oh plus the bell curve is very steep with lots of Maths majors screwing it up. From what I heard, this module used to be on the same level as ST1131. However, since Prof Anthony Kuk took over, it became quite difficult a module.

Coupled with procrastination, I was always lagging behind lectures (I don't go for lectures partly cos it's 8am and partly cos going for lectures isn't gonna make much of a difference). I never attended tutorials as well since the tutor is atrocious as everyone has made him out to be.

Topics covered ranged from 1-factor ANOVA, 2-factor ANOVA to simple, multiple, subset regression, residuals, outliers and the combination of ANOVA with regression. There was no compulsory textbook for this module. All were just references but the main one was Introduction to Regression Analysis by Montgomery, Peck and Vining, 5th Edition.

Weightage
Assignments: 20% (2 assignments worth 10% each)
Midterms: 20%
Finals: 60%

Most people got full marks for assignments which mainly made use of R (I did not though). R isn't tested in finals by the way. Midterms was just plugging in formula. Well, the thing about midterms was that you didn't have to understand what you are doing, you just have to know what to use. Average was around 30/40. The midterm score was uploaded onto gradebook before the scripts were returned to students during lecture.

Finals was the hard one for me or probably for many people. So nothing in the notes appeared and they were all out-of-context questions. This just boils down to how well you understand the concepts and yes, because I didn't, I screwed the finals up badly. But the finals was really typical of an open-book exam where you had to think on the spot and manipulate some stuff. Time wasn't a constraint though.

Result: B
No doubt finals is the determining factor since I got full marks for midterms but at the same time, no doubt that this module is important. My advice is be consistent, clarify doubts immediately, don't ever snowball anything.

End of Year 2 Semester 2

So Wednesday marked the end of my second year. I've been pretty much slacking for this semester. I wasn't working that hard but at the same time, it's not as bad as Year 1 Semester 2. But somehow or other, I feel that this semester has passed really very quickly. It seems like I have not settled down yet and it's already the examination week. 

And I guess the readers of this blog will be quite interested in how I'm doing so far for my modules this semester so I shall just give a brief summary of how I've fared. Basically, I'm more or less prepared for my first B- or C+ for CS1010E; I just totally screwed up the finals and it doesn't help that I'm only slightly above average for my lab component which is 50%. So there is a high chance that I will be using my second S/U this semester and that I wouldn't be able to continue with my second major. I admit that I have not much talent for computing and it happens that finals focused on my weakest topics: pointers and structures (I think it should be the same for most beginners). I think it doesn't take practice but experience and more importantly, wits to do well for this module. As for the midterms for the other modules, I scored just a few marks above average for EC3312 and ST2132 whereas for EC3333, I was a few marks below the average. The reason was because my concepts for the earlier topics for EC3333 weren't exactly strong and that was the reason why it cost me most of my marks for the midterms on top of a giveaway question that was identical to one in the previous midterm exam that was uploaded by the Prof but having procrastinated, I did not do it so I deserve to get below average. Plus the bell curve for EC3333 is very steep and I really do mean very steep. So I can only depend on finals now to pull me up hopefully to an B+ as the finals focused quite a bit on derivation-based proofs and understanding of theorems. But at the same time, I am not hoping for too much, probably expecting a B or maybe a B- now that I think of it. Yeah, that's me; the more I think about something, the more I would lower my expectations for it. As the saying goes, the greater the expectations, the greater the disappointment. I think that's very true for me as I would brace myself for the very worst before getting my results every semester.

ST3131, on the other hand, was one module I did well for the midterms (yes, I should think full marks is considered pretty okay) but then again, the midterms was really easy; it was simply plugging in the right formulas. Yes, it's that straightforward. I feel that ST3131 is a module that is hard to understand but easy to apply once you get it. But how wrong was I as the Prof decided not to be so kind for the finals and set some questions that are not in the scope of the coverage of the lectures which was expected since it's an open book exam. 

To sum up, I am not confident of even securing a 4.0 this semester. Yes, I am not someone who's confident about himself or herself so I am expecting to obtain quite a bad SAP this time round. To drift off a bit from academics, I am now trying to secure the position of a RA with a Prof after being rejected for two internships and having received no replies from other organizations. I just hope I'll at least have something to occupy me for this summer (maybe to act as a fallback for my predicted lousy results). 

Reflections on AY2013-2014 Semester 1

The workload for this semester was very light having taken only EC modules. In fact, I didn't do much practice but I was pretty consistent in my revision. So yes, that's all you have to do to score for higher-level modules and this leaves you with an additional 3-5 hours of free time everyday.

I think taking a break from my second major and focusing on EC3101 and EC3102 for this semester was the right decision. In fact, before the start of the semester, I kept pondering over whether I should clear both EC3101 and EC3102 together or just EC3102 first. Apparently, the lecturer for EC3101 will be a different one in Semester 2 and that might have increased my chances of getting a higher grade should he decide to focus less on Asymmetric Information. However, I am glad to have cleared both the core modules all at once as my level of interest in micro makes clearing EC3101 a burden.

Meanwhile, I have taken a look at the content for EC4101 and it's good to know that it will be a lot more rigorous in mathematics as compared to EC3101. Fortunately for me (and unfortunately for some), the core electives which are solely micro-based are usually the ones that are more theoretical in nature and less useful in reality (probably with the exception of Labour Economics). Also, since mathematics and statistics are indispensable tools to economic analysis, it is best to avoid qualitative modules as much as possible.

Having said that, my choice of electives for this semester is apparently quite challenging to most people. But really, EC3341 isn't hard as what many people have claimed. However, I was honestly pretty put off by the wordy textbook this module uses which made me regret a little to have taken this module. Even so, I think this module lays a good foundation to higher-level modules in the field of international economics. For those interested in this field, EC3341 is a must-take. On the other hand, I do not advise those who aren't mathematically-inclined AND wish to avoid the risk of screwing up their CAP as much as possible to take EC3314. But knowledge-wise, this module is truly one of the better ones to take.

Reflections on AY2012-2013 Semester 2

I think "I deserve it" basically sums up this semester. It was unforgivable for me to have slacked off during the second half of the semester when the content was getting increasingly more challenging and that I did not have an intelligence level that was on par with those who can do well without studying much. Yes, so there's actually nothing else I can reflect on; this is probably the biggest downfall for this semester: laziness.

On a side note, the reason why I could do fairly ok for EC2102 was because knowing I had done really badly for the midterms, there was an urgent need to pull up the finals. Maybe that partially led to neglect for other modules as well on top of the lack of interest.