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Hi folks, I'm in a bit of a dilemma about choosing the best possible math courses, given my constraints, in order to improve my profile for econ PhD admissions. Would really appreciate your advice on this. Background: Undergrad from a ~top university in India, MSc. from a ~top university in Europe. Unfortunately, no separate math classes during my MSc. During my undergrad, we had two 'mathematical methods for economists' (MME) courses which provide some basic training on most of the math topics one expects before a PhD: calculus, probability, linear algebra etc. However, the MME courses are not nearly as rigorous as standalone courses like Calc 1, 2, 3; linear algebra etc. Through a process of trial and error, I've learnt that I need to show separate credits for these math courses in my profile in order to be eligible for atleast decent econ PhD programs (aiming mostly for the U.S). I've been an RA for a little more than 3 years now, have a published co-authored paper (and working towards more), and strong references from the profs I work with. So the non-grade aspect of my profile is in decent shape. I also got a 166 in the GRE quant, which I took in December 2021. Current status: I began with real analysis from UIUC's NetMath program and scored decently well (got an A). I'm now taking calc 2 from Westcott Courses, which provides a credit from UMass Global (not my first choice but had no other viable option at the time). Dilemma: I'm torn between taking calc 3 vs. linear algebra after I'm done with calc 2. Ideally I'd like to take both but I only have until December till some deadlines close. I'm leaning more towards calc 3 because my research statement/SoP leans a bit more towards asking questions that might require a theoretical approach, in addition to empirical tools. I've also had a lot of exposure to intermediate/advanced econometrics and statistics in the course of my research work as an RA, and I developed a working understanding of concepts where I needed to. I'm wondering whether this could partially compensate for the lack of a separate linear algebra course in my profile. TL;DR: I have two possible bundles of math courses for signalling purposes in front of admissions committees: bundle A = {real analysis, calc 2, calc 3} and bundle B = {real analysis, calc 2, linear algebra}. I'm leaning towards A because I'm hoping my research experience might indicate a bit more familiarity with econometrics/statistics, and the calculus in calc 2+3 is what I need courses for. I only have a few months and a full-time job so doing both calc 3 and linear algebra is near impossible without getting bad grades. Which bundle do you think would make the most sense? Would really appreciate your advice on this.

Hello, I’ve just finished my first year of a BSc Economics degree at Warwick University. I’ve been dead set on a PhD in Economics in the US for a while, and I’m aiming for the top 5. However, I don’t feel like my undergraduate degree is helping me. Although I can take a limited number of modules from the maths department, I don’t feel like I’m getting enough maths to be competitive at the top programs. I have the option to switch to BSc MMORSE (essentially maths, stats and economics), which allows for far more mathematical/statistical/econometric modules to be taken while not sacrificing much economic theory, although it would take me two years longer. I’m struggling to come to a decision on whether to switch. I’ve performed well in my first year (estimated top 0.5-1%), but worry that even if hypothetically every other part of my application was exceptional, it would be undermined by my lack of maths. I realise how hard it is to get into a top 5 program especially with a non-top 10 undergrad, but I’m serious in my ambitions. Do you think the value placed by the top PhD programs on mathematical preparation justifies the obvious financial/social costs of switching? Thank you!

Hey guys, This is bit of a long post. I had a Bachelors and Masters in Business Administration from India (Not a lot of quant coursework). I got admit to a PhD Finance program at lower ranked (top 100) department in the US in 2016. I was there for 1 semester but due to a combination of factors (lack of good research opportunities, me not having clarity on what I wanted to study, and moving to be closer to my husband), I transferred to a Masters in Finance Program of a top 50 department and graduated with a lot of doctoral coursework (Probability and Stochastic Processes, Mathematics of Economics, Mathematics of Finance, audited Optimization, Theory of Finance all at the doctoral level) and a small research project under a tenured Prof. My research project did not turn out as well as I had expected it to (since I took it for just one semester) and I am not sure if I would be able to go to this Prof for an LoR. I have been working with a top financial analytics company in credit model validation and verification for almost two years now and I would love to get back to pursuing a PhD hopefully in 2021. I have matured more as a person, and have found the real reasons why I want to do research. I have never let go of learning - I have been taking up coursework in an Analytics masters program to get more hands on experience in research methodology and general data science which have been very useful. However, I am still not very confident of my quant skills and above all my ability to do Research. I was playing around with the idea of applying to PhD programs directly. However, I am concerned about: 1. LoRs from my professors - it's been two years and I am not sure if they would remember me. 2. Jumping right into a PhD after 3 years. I was wondering if I can gradually get my feet wet with a Masters. I would also like to expand the universe of schools to Europe, Australia, and Canada. I saw that some of these schools require a masters dissertation as a part of their application. Lately, I have been looking at schools that offer MPhil or MRes with both coursework and dissertation options which I frankly find attractive. I would love to beef up my quant and Econ along with getting some research experience. So far, I have come up with LSE, Bocconi, and Alliance Manchester. But some of these are so pricey. I was going through an older thread (here) where some people have said that there are departments with a track record of placing students in good PhD programs. I am honestly wondering how to go about finding a Masters degree that would be a good preparation for a PhD in Finance with a good mix of coursework and research. I am an international student and with already one Masters, I am now looking for a Masters program that is no longer than 1 year. So, my questions are: 1. Can you suggest Masters programs that are good preparation for PhD in Finance? I humbly request you to specify which university and which department. I know an MA Econ or Statistics is good, but I am trying to see if I can get specific recommendations apart from the ones that I have already shortlisted :) 2. I am 32 and while age is a just a number, numbers are everything where we are ;) and I want to get some honest feedback on whether it even makes sense to go for another Masters or just bite the bullet and apply for PhD programs directly? Thank you!!

Background: Apps are out, so I want to focus on preparing myself for the the first year of Econ programs, rather than stressing about which one I may or may not get into. For reference, I have been out of school for a couple of years working an industry job, and haven't had the opportunity to do much actual math since leaving undergrad. I'm worried that I will be behind at the start of programs if I don't refresh my math knowledge over the coming 7-8 months, even with a pre-program "boot camp". Let's assume for exposition that I have forgotten essentially everything Math and Statistics-related, including even some topics from single-variable Calculus. Some topics require only quick review, others more extensive (e.g. I spent 30 minutes today remembering how to do implicit differentiation and getting myself back to "exam ready" for that topic, but probably would want a few weeks to go back through my Real Analysis course). My current plan is to spend a small amount of time reviewing basic (single- and multi-variable) calculus, then moving on to a fuller review of more complex topics (bounded optimization, Rudin, ODE, etc.). Questions for the forum: What Math and Stat topics should I focus most on to prepare myself for the first year of Econ PhD? How would you go about self-studying to "get back up to speed" on the topics from 1? What are your favorite textbooks? Post textbooks or online resources to check out (e.g. Khan Academy), study strategies, etc. For Prob/Stats specifically, what is most important to review? I've been "reg monkeying" for 2+ years, so I'm confident on a lot of Metrics stuff, but have forgotten/grown rusty with more basic topics (I'm sure I would not do well on a "Combinations and Permutations" test, for example). Please also feel free to riff off of these questions however you wish. Any advice is highly appreciated! Thanks for any and all help, and best wishes for a stress-free application season! - Tenuki

I am working on strengthening weak math grades. I recently took Real Analysis and Linear Algebra I via Harvard Extension school, and next on my mind is probably retaking multivariable calculus. Harvard Extension doesn't offer it this semester, and so I am exploring other options. There is a multivariable course at UT-Austin extension (Sequences, Series, and Multivariable Calculus | University Extension | The University of Texas at Austin) is there any benefit of waiting until Harvard offers it again, or can I take it at UT-Austin, or some other school for that matter, and not risk any eyebrow raises for the sake of reputation ad bouncing around different schools? This course is also self-paced which is a pro for me, I'm not sure if ad coms would be aware of that distinction. Any other courses you know of that you'd recommend? As an aside, there is also a more advanced analysis course at the Harvard extension school this semester called "Real Analysis, Convexity, and Optimization" that seems relevant but I'm not sure if I need to do that as a form of going a bit above and beyond, or if I can stick with the basics of fixing my previous bad grades.