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Hello fellas! First post here. I was wondering on taking these courses, but my time slots are limited. Assuming I would like to concentrate in macro in the future, in order or priority, which ones should I take? Which ones give a better signaling value to admission committees? I was thinking that Time Series might be more useful but always thought that math courses for micro theory give a better signal.

Hi, I'd be very interested and grateful to read your thoughts on my situation. I'll start with my profile: PROFILE: Type of Undergrad: Maths, US News Global Top 10 (I'm from outside the US); econ courses from a programme that's little known internationally, but that's well-respected and places well onto master's programmes. Undergrad GPA: First class. Type of Grad: Not started yet; offer for Econometrics and Mathematical Economics MSc at LSE. GRE: Will take later. Math Courses: Real analysis (B+), Calculus & probability (B+), Linear algebra (first year A-, second year A-), Metric spaces & complex analysis (A-), Topology (B+) + quite a few more that are very pure and not very relevant. Econ Courses: Intermediate micro (A/A+), Intermediate macro (A/A+), Intermediate econometrics (A/A+), Maths for economics (A/A+). Research Experience: None that's relevant, will aim to do an RA/pre-doc after my master's. (Grades are listed using standard conversions -- we don't use A, B, C etc. in my country.) I couldn't take econ courses during my undergrad (students study specific programmes and outside options are very limited) but since graduating, I've taken classes in intermediate micro, macro, econometrics and maths for economics, all A or A+, depending on how you convert. I don't have any stats courses on my transcript but I've self-studied first and second year stats and second year probability. I did well in my undergrad overall but, as you can see, my grades in the courses that matter most for econ PhD applications aren't great. My undergrad was very rigorous and tough, so I'd like to think that these grades aren't as bad as they look, but they certainly don't look very good. It might be relevant that the first three (up to first year LA) were first year courses and I have an upwards trajectory from there. I have an offer for the MSc in Econometrics and Mathematical Economics at LSE, which I'll probably accept. I'm wondering how much these undergrad maths grades will matter for PhD applications after the MSc. I have some time to study before the MSc so I'm considering re-taking important courses/filling in important gaps, e.g. via NetMath from University of Illinois, Urbana-Champaign -- this was recently suggested on another thread. If my undergrad grades will be largely superseded by my MSc grades though, then it doesn't seem worth doing any formal study for credit before the MSc. (I would still self-study to prep for the MSc as it's very demanding, but I would go about this in a somewhat different way.) My questions are: (i) How much will my undergrad maths grades matter for PhD applications after my master's? (ii) How much would re-takes/additional courses compensate for my not great undergrad grades? (iii) If I take some courses for credit, which courses should I prioritise taking? Any other thoughts or suggestions are very welcome! Details on course content (feel free to ignore if not helpful): I'm not entirely sure how my maths courses map onto US courses/sequences as the system is quite different here and not at all standardised. E.g. My calculus course included some material I think is found in US differential equations courses. Calculus had some proofs but everything else was heavily proof-based. For brevity, I won't list everything but will hopefully give you a sense of what was covered. Analysis: sequences, series, continuity, differentiability, Riemann integration. Calculus: ODEs, partial derivatives, simple PDEs, parametric equations, line integrals, Jacobians, double integrals, surfaces, directional derivative. Probability: probability space and axioms, conditional probability, law of total probability, discrete and continuous random variables, joint, marginal, conditional distributions, conditional expectation, random walks, probability generating functions, Weak Law of Large Numbers. Linear algebra (first year): using matrices to solve systems of linear equations, vector spaces, subspaces, bases, linear transformations, Rank-Nullity Theorem, bilinear forms and real inner product spaces, eigenvectors, Spectral Theorem. Linear algebra (second year): abstract vector spaces, rings, quotient spaces, dual spaces, adjoints for linear maps, orthogonal maps. Metric spaces: metric spaces, isometries, continuity, completeness, Contraction Mapping Theorem, connectedness, compactness. Topology: abstract topological spaces in terms of open sets, continuity, connectedness, accumulation points, basis of a topology, product topology, quotient topology, abstract simplicial complexes.