win

Yeah, that is what I figured. Just saying it's not trivial - mine dropped 3%, crossing the silly 90% line, in about 3 years. Luckily I don't think I'm going to be needing it again, but I could imagine someone doing a 2 year masters and needing to take it again just due to the passage of time, which would be quite frustrating.

I believe it also changes over time. I've watched my score "slip" down the percentages over the years.

You definitely know more about the courses and workloads than I do, but it seems like you've got plenty of hard math to cover in your courses. If you are just planning on auditing the class, I guess you can probably just stop going if you have too much work. My point is just that doing well in something like Analysis (which I read as the subjects of Abstract Math and Advanced Math Analysis) is much more important than the bonus class of optimization. Either way, I'm sure you'll find a balanced work load and it sounds like you are taking all the right classes. Good luck!

In my opinion, given all the courses you've mentioned, you should be very well prepared. While there is always more you can learn, I would suggest focusing more on making sure to learn the topics in your classes deeply and thoroguhly as opposed to trying to cover more subjects. Even at the expense of other topics. For example, learning the material in these courses you initially listed should take priority over auditing an optimization course, which is a nice bonus but something that you can do with relative ease if you really understand the math. As for books suggestions, Rudin's Principles is the obvious choice, but I think most people's first encounter is a little intimidating given its sparing prose, lack of examples, and so on. I think once you are in class you will find that less the case, but I think two useful bridge books are Understanding Analysis by Abbott, which is a gentler introduction which I think is more accessibly written. It wont replace rudin, but it would probably be an easier introduction to the most important topics. Another one, which is not specifically analysis, but I think is very helpful is Thomas Sibley's Foundations of Mathematics. I have not come across an electronic version (if you do, let me know), but it is really a very accessible introduction to a lot of the topics that rudin either takes for granted or moves through quickly but are fundamental later on. For example, it spends a fair about of time explaining the basics of set theory, of how to write proofs, the foundations of functions and relations. Anyways, it sounds like you are well covered. Good luck with your courses.

I don't know much about what is required for an accounting PhD, but I know some have coursework in graduate microeconomics, in which case yes this would be very helpful. Multivariate calculus and linear algebra is probably the bare minimum, but I would also suggest a bit of optimization and real analysis. If you don't have exposure to these two it would definitely be challenging, but you could supplement with some gentler introductions like Chiang's Fundamental Methods of Mathematical Economics and something like the harvey-mudd analysis lectures on youtube. Most importantly, as I mentioned in my review of the math camp, you need a good amount of time. It's quite a lot of challenging material, but if you have to take grad micro it will be a big help to have at least seen it before.

Oh, oops, my first post with the review was not showing up initially, so I rewrote it, but now I see the first one is there as well. Sorry for the double post.

I took the stats course this year and I thought I’d leave a quick review for posterity. I took the corresponding math course last year and reviewed it in this thread (http://www.www.urch.com/forums/showthread.php?t=157763). The main points mostly carry over, so I won’t repeat all the details except to summarize that I thought it was excellent preparation and very, very demanding both in terms of time and the level of the content. The lectures were very useful and the professor and RA accessible and helpful during office hours. Some minor notes specific to the stats course: First, while the course is pretty much from first principles some preparation, especially in the form of real analysis is helpful. It is helpful because some of the concepts are directly related or analogous to concepts in analysis and because there is a fair amount of rigorous proof writing. Second, in addition to the probabilistic and statistical content, this course offers a fair amount of measure theoretic foundations. As far as I can tell comparing with the syllabus of other university’s statistics camps this is beyond what many (most?) programs cover. At times it can feel a bit abstruse and overkill, but I also think it is an asset as this is material one eventually will need and so early exposure is an advantage. Third, and more generally, when preparing for a PhD I had heard a lot about the math required, but often probability and statistics receives only a small mention. Having really only done one statistics and one econometrics course before, I thought that would be adequate, but now in retrospect, I’m very glad I took this class to fill in some of the higher level material I would not have had exposure to otherwise. Overall, I thought the course was excellent, if difficult, and would definitely recommend it to anyone interested in building up their probability and mathematical statistics background before starting graduate work.

A quick note for posterity: I ended up taking the stats camp course this summer in preparation for starting graduate courses this fall. I did the math course last year, which you can read my review of in this thread, and most of the main points stand: (1) I learned a ton and thought it was excellent preparation. (2) It’s very hard and takes a lot of time. Given those main points, I’ll just to add a few comments specific to this course. 1) This course goes above and beyond some of the statistics camps I’ve seen elsewhere by adding a fair amount of measure theory to the standard Casella and Berger readings. While more abstract and sometimes difficult to appreciate, I felt this added a lot of value either in covering some useful topics not in CB like conditional expectations or providing some additional depth. 2) While most of the material from the Math Camp course is not a prerequisite, and I assume they are meant to be taught simultaneously, there are certainly parts of the curriculum that take a solid understanding of analysis for granted. While not a full requirement, having an analysis level understanding of topics like continuity, properties of the real line and sequences, and so on, is very helpful. So again, like the Math Camp, I had a good, though challenging experience, and would definitely recommend it for someone want to prepare before starting a phd.

The math require for economics is extensive and varied, so "sufficient" is difficult since you will always need to pick up some odds and ends on the way. That said, this looks like an excellent set of courses to prepare: calculus, linear algebra, and proofs/analysis are the core of graduate economics. A couple quick thoughts: First, it looks like "further calculus" is comparable to some "calculus 2" courses in the US, given its focus on series and integrals. Have you taken multivariate calculus previously? I would imagine linear algebra might require it, but if not, this is probably the most important missing element. Second, have you taken any probability/statistics or econometrics? If not, these would be the next most important missing elements. Finally, while often covered in economics classes, one area that is a bit more applied, but useful to either take a course on or do some self-studying with would be optimization. This is a lower priority, but definitely would be another area to consider working on a bit once higher priority items have been covered.

A minor update here. After hearing back from most schools, I am upgrading my assessment of the signal from this course. As I mentioned, my case is probably not fully indicative because I think I had an overall decent profile with good research experience and letters of recommendation, so a sparser math background was a uniquely weak part of my profile. Nonetheless, most everything about my application was the same as last year, but I ended up getting funded offers from multiple schools that turned me down a year ago (I also had one more year of being an RA, but after having done a couple already I suspect that provided only a marginal contribution). Of course, this course does not replace doing well in numerous math classes, and I still think the courses best value is in the education and preparation rather than the signal, but in my best judgement it also made a significant difference in my two application rounds. If they offer the probability and statistics version of this this summer, I’ll probably enroll just to prepare for school.

So now that some time has passed I thought I might offer a brief review of this course for posterity. The TLDR version is three main points: 1) I thought the class offered excellent preparation; 2) I'm not exactly sure how strong a signal it was, but it was definitely non-trivial; and 3) The work load is very high, so if you are working do some preparation in advance. Some elaboration: 1) The course covers a wide range of material, including analysis, linear algebra, advanced calculus, optimization, and difference/differential equations. The readings, homework problems, online organization were all very well done and it did really feel like attending a class, just online. Because of the demanding amount of time and deadlines, it was considerably more active (and useful) than many self-paced math related MOOC (I've dabbled in a few). There were some minor glitches with typos in questions and so on, but they were small, corrected quickly and honestly you get plenty of that in classroom courses as well. The lecturers, professor, and RA were very good and very helpful. Over all, I learned a lot. Some material was mostly review, like some analysis and linear algebra, but I was able to fill in some gaps and get some practice. Other material, like optimization, was completely new for me. All together I feel considerably more prepared for graduate level economics than I did before the course and to me that was its greatest value. 2) It is impossible to determine exactly how strong of a signal it was for ad coms, but I applied to a wide range of schools last year and fell pretty short on what I think was mostly a thin math resume. I am still waiting to hear back from most schools after reapplying this year, but so far I have already gotten in a couple schools I did not last year, with out much else changing in my application. Will it replace earning As in a bunch of math classes in college? Of course not. But my best assessment is it significantly reduced a red flag in my applications. One thing to note is that its a bit difficult to know how to include it in applications. In my cover letter I mention taking the course, what it covered, and how I did, and where possible I tried to upload the certificate and syllabus to "supporting documents" pages, but often this was not possible: many applications only let you upload documents related to a school you register and so on. Probably 75% of applications I was able to submit all the documents, some I tried to mail the admissions people and ask them to include it, and so on. 3) As mentioned above, the coursework was very demanding. I presume it is designed based on the in classroom version of the course for incoming PhD students. Those people are full time students, so aspiring to do the course in a short time while working is not very realistic. The course was offered in a 4 week and 8 week pace, but the 8 weeks completed after applications were due. I presume if they offer it again in the future they will start it earlier to avoid this because initially the course was supposed to start earlier and then was pushed back. Though I did the 4 week course, I knew I wouldnt have time for it so read all the material and did practice problems in the month preceding it, effectively making my own 8 week course. Even then, it was very challenging and demanded a lot of self-discipline to do it on top of work. So make sure you have time and choose the 8 week if they start it early enough. Of course, all of that depends on your background. Math college majors likely don't need to do a bunch of prep, but if you're a econ student like me with a handful of math courses that you did okay in then definitely prepare in order to keep up if you are going to do the 4 week version while working. A small note: Some of the books used, like Rudin, Sundaram, and Simon and Blume are classic texts and if you are going back to a PhD you will probably need to own them anyways. Some however, such as the books used for the linear algebra and differential equations portions of the course were (in my opinion) needlessly obscure and expensive. Obtaining them (which regretfully I did) was very expensive, a hassle, and honestly they were not my favorite texts. Most of the material in them could be sourced more easily elsewhere. Frankly, in my opinion the course should probably just use the Angel de la Fuente as it has all the content they want to cover in one easily obtainable book, but regardless, my point is just to be cognizant that getting the readings is nontrivial. Anyways, I am not sure if they will be offering the course again, but in case they do I wanted to provide some perspective before I forgot. In sum, I thought it was very challenging, but extremely good preparation and also a modestly useful signal. Cheers.

I'm looking to add a few more schools to my list for applications. My interests are in monetary, financial, and, to a lesser extent, international macro economics. Schools in say the top 25 are well known and I'm planning on applying to a fair number, so I'm looking to add some programs more in the 25-50ish range to round out the application portfolio. I've looked at the US News, Tilburg, and Ideas RePec topic rankings, but while they are a good starting point often I'm finding these are poorly correlated with academic placements. For example, OSU seems to continually rank highly, but as far as I can tell they've had very few US academic placements in the last several years. I am also open to international options, again as long as they have some meaningful placements, so places like UPF and Frankfurt are definitely on my list, though I will probably return to the US, so that's mostly my focus. Like most people I'd of course prefer to be in a nice location (large or small town is fine) with a positive academic community (read attrition rate Some that I'm considering so far: Boston College, University of Washington (Seattle), UPF, Frankfurt, UBC, UC Irvine. Any other suggestions? Thanks!

I am currently a student in a US MA program (not following the consensus advice, I know) focusing on international economics and I may be considering applying to a phd program further down the line (need some more math and probably a research position after school). I like international economics a lot and I am trying to learn more about the schools that have strong programs. I have seen the US News rankings for international economics. However, I wanted to (1) get some second opinions, as I frequently read about these rankings being challenged in general and (2) ask about other schools not listed, given that a phd program is a long way off and I don't know if I'll be applying to the top programs or further down the list. Additionally, international economics involves many dissimilar but related elements, for example trade is more "micro-ish" while currencies/monetary/exchange rates are more "macro-ish." As such, some schools may be stronger or weaker in one particular dimension of international economics. As such, I was hoping to begin a discussion on what are some good programs, who are good professors and so on. I suppose it would be good to go ahead and field any other thoughts and advice on the field, subject, careers, etc. here.