Phd in Economics - Fall 2020 Cycle

[JonSnowLives]

PROFILE:

Type of Undergrad: B.S. in Economics (Major) with Math (Minor) from Top-80 ranked Econ program in the US (large - public research university)

Undergrad GPA: 3.90/4.00 overall ( 4.00 in Econ, 3.80 in Math) - Summa Cum Laude

Type of Grad: M.A. in Economics from Top - 3 Canadian

Grad GPA :4.0/4.0

GRE: GRE: Q169 / V162 / AWA 4.5

 

Econ Courses :Principles of Micro (A+), Principles of Macro (A), Intermediate micro (A+), Intermediate Macro (A),Econometrics (A+), Mathematical Economics (A) and many more (all A's / A+ 's)

 

Math Courses: Calc 1-3 (A-,A,A), Abstract Algebra(A+), Probability theory (A), Mathematical Statistics (A-), Linear Algebra (A+), Differential Equations (B+), Theoretical Concepts of Calculus (A+), Complex Analysis (A)

Econ Courses (grad-level):Micro -Thoery (92/100), Macro Theory (94/100), Econometrics -I (91/100), Quantitative methods (94/100), Labor Economics, International Trade, Econometrics - II, Financial Econometrics (All in progress)

 

LOR's : From Grad Micro, Macro, Metrics Proff ( all from top-20 US). Should be good because I was at the top of the cohort in their classes and they know me well

 

Research Experience: Summer thesis

 

Appyling to : Top 5 Canadian ( I am a Canadian PR), Top-20 US.

 

Concerns : (i) Time lag between between undergrad and grad ( I was working for about 5 years)

(ii) Strength of undergrad program ( Econ department not that reputed )

(iii) More math classes ? Missing Real analysis but took Complex Analysis Instead.

(iv) Lack of publication / solid research experience

 

Any comments or advice on my chances will be greatly appreciated ( especially which schools I have realistic chance at and what areas I can improve)

 

Thanks.

 

[leolin]

I think more proof-based maths courses will strengthen your profile a lot, and I believe real analysis is a must;

 

I wouldn't worry about lack of publication if I were you, but only one summer thesis does not prepare you for research very well. Will your thesis supervisor write you a LOR?

 

Try to get some RA experience to improve your not-so-solid research experience.

 

What is your job for the 5-year time lag? If that somehow includes some research experience you might be fine with it.

 

With your current profile, I'm not sure if you have a fair chance with UBC or UoT. But for other Canadian schools you should be fine in my view. Top 20 PhD programs in the US might be out of reach with your current profile, unless your 5-year job includes significant research experience. I would suggest you apply for Top 40 US programs, and you might be waitlisted for some Top 20 programs.

[tutonic]

I think more proof-based maths courses will strengthen your profile a lot, and I believe real analysis is a must;

 

I wouldn't worry about lack of publication if I were you, but only one summer thesis does not prepare you for research very well. Will your thesis supervisor write you a LOR?

 

Try to get some RA experience to improve your not-so-solid research experience.

 

What is your job for the 5-year time lag? If that somehow includes some research experience you might be fine with it.

 

With your current profile, I'm not sure if you have a fair chance with UBC or UoT. But for other Canadian schools you should be fine in my view. Top 20 PhD programs in the US might be out of reach with your current profile, unless your 5-year job includes significant research experience. I would suggest you apply for Top 40 US programs, and you might be waitlisted for some Top 20 programs.

 

He already has an A in Complex Analysis. There's no need to take Real Analysis. The best thing to do till you apply in Fall '20 is to work as an RA for someone at the place you did your masters in. It'll get you better letters, and also remedy the deficiency in your profile (lack of research experience).

[JonSnowLives]

Thanks so much guys for all you valuable inputs and being realistic; I would definitely try to get a research job following my summer thesis. My 5 year job experience was more in teaching rather than research to be honest, so that shouldn't count for much I presume.Couple of my Math-classes have been proof-based but I wish I had real analysis ( it wasn't being offered during the term I intended to take it). Anyways, I would hedge my bet and apply to more top-30ish US and fingers crossed for UofT, UBC, Queens. I think my letters of recommendation would be solid based on the courses I took, and I would definitely try to get into research.

[JonSnowLives]

Any more valuable comments and advice will be highly recommended and appreciated. My research interests revolve around International Trade and Dynamic Macroeconomics, so any suggestions as to which schools fit well for me would be very helpful.

[leolin]

He already has an A in Complex Analysis. There's no need to take Real Analysis. The best thing to do till you apply in Fall '20 is to work as an RA for someone at the place you did your masters in. It'll get you better letters, and also remedy the deficiency in your profile (lack of research experience).

 

Personally I don't think complex analysis could be a substitute for real analysis, even though complex analysis is more mathematically advanced than the real. Even though the logic for writing proofs is always similar, I think real analysis is more prone to Econ than complex analysis.

 

But yes RA is the right thing to do for sure.

[richardsorge]

I think your biggest issue is the 5 years gap between your undergrad and grad studies. Adcoms may think that your math skills have become blunt especially if your master's program is not known rigorous enough. But still your chances for top20 is high, I believe.

[JonSnowLives]

Thanks all to your wonderful inputs. I am taking a graduate level Advanced Real Analysis Course this summer along with my research. Sounds like a good plan?.. In case, as the previous poster mentioned that the adcoms might doubt my math skills .Just to mention, the Math department at my grad school is very well reputed worldwide.

[leolin]

Graduate real analysis is indeed what you need. You should study some measure theory in the course. If you do it very well and get to do some RA jobs before application, I'll be surprised if you could not get to any of the Top 20.

 

I'm not familiar with macro and trade so I could not give you specific advice on which schools to apply.

[JonSnowLives]

Graduate real analysis is indeed what you need. You should study some measure theory in the course. If you do it very well and get to do some RA jobs before application, I'll be surprised if you could not get to any of the Top 20.

 

I'm not familiar with macro and trade so I could not give you specific advice on which schools to apply.

 

Thanks!

[chateauheart]

I disagree strongly with leolin's advice. It's ridiculously off the mark.

 

You have a MA from a top Canadian program, and you were top of your cohort. In terms of academic coursework, you are probably in the 98th+ percentile *within* the selective pool of applicants to top 20 programs. Additionally, your research interest and experience seem to be in empirical work. There is very little value, in either the short run or long run, from taking further graduate math. You're set for PhD applications. Any marginal effort should be focused on RA experience and building rapport with your letter-writers - but even that is not necessary for getting into a top Canadian PhD (or a equivalent top 20 program in the USA). Ask your professors what proportion of MA students they admit into their PhD programs per year just based on their master's performance, and chances are you're well within the quota. There's no uncertainty here.

 

You've received conflicting advice, so let me explain the difference in our experience here. I personally took grad real analysis as a 19 year old, and another grad probability course at 20. I don't think they helped me a single bit in my graduate econ career. I've never looked back into Billingsley (the reference text) even once after I finished the course. And, judging from my conversation with two top 5 adcom members after my application season, the grad math courses were not key to my application success. They were, on the other hand, a huge expenditure of time.

 

Looking back at leo's profile, on the other hand, he's a current applicant in this cycle, and has an unusually lopsided profile himself - little coding and too much math, as other posters had pointed out to him. He also seems to be underperforming his expectations in this cycle. I realize I'm being a jackass by digging up another poster's background, but it's bizarre to me that an underperforming applicant doesn't seem to realize his understanding of the admissions weights is flawed, and is now offering the same wrong approach to other applicants. Flagrantly wrong advice like this - the undue obsession with grad-level math and lack of emphasis on research experience - is frustrating as hell, especially when we've been pointing it out on this forum for 5 consecutive years, and every cycle we have dozens of cases reaffirming the forum's consensus. We've had an applicant with 4.0 in 2 years of grad math at an Ivy league university who got significantly worse admissions results than another applicant from a comparable institution with a 3.7 undergrad GPA, 1 grad micro course, and 1 year of RA experience.

 

Conditional on already having rigorous grad econ courses, I advise *every* applicant against taking grad real analysis unless they have a substantiated belief they'd be specializing in micro theory or econometric theory. By substantiated belief, I mean something like 1 year of working in a supervised research project in either of those topics. Chances are you should already be enrolled in a grad econ program by the time you can make that decision.

Edited April 19, 2019 by chateauheart

[leolin]

I disagree strongly with leolin's advice. It's ridiculously off the mark.

 

You have a MA from a top Canadian program, and you were top of your cohort. In terms of academic coursework, you are probably in the 98th+ percentile *within* the selective pool of applicants to top 20 programs. Additionally, your research interest and experience seem to be in empirical work. There is very little value, in either the short run or long run, from taking further graduate math. You're set for PhD applications. Any marginal effort should be focused on RA experience and building rapport with your letter-writers - but even that is not necessary for getting into a top Canadian PhD (or a equivalent top 20 program in the USA). Ask your professors what proportion of MA students they admit into their PhD programs per year just based on their master's performance, and chances are you're well within the quota. There's no uncertainty here.

 

You've received conflicting advice, so let me explain the difference in our experience here. I personally took grad real analysis as a 19 year old, and another grad probability course at 20. I don't think they helped me a single bit in my graduate econ career. I've never looked back into Billingsley (the reference text) even once after I finished the course. And, judging from my conversation with two top 5 adcom members after my application season, the grad math courses were not key to my application success. They were, on the other hand, a huge expenditure of time.

 

Looking back at leo's profile, on the other hand, he's a current applicant in this cycle, and has an unusually lopsided profile himself - little coding and too much math, as other posters had pointed out to him. He also seems to be underperforming his expectations in this cycle. I realize I'm being a jackass by digging up another poster's background, but it's bizarre to me that an underperforming applicant doesn't seem to realize his understanding of the admissions weights is flawed, and is now offering the same wrong approach to other applicants. Flagrantly wrong advice like this - the undue obsession with grad-level math and lack of emphasis on research experience - is frustrating as hell, especially when we've been pointing it out on this forum for 5 consecutive years, and every cycle we have dozens of cases reaffirming the forum's consensus. We've had an applicant with 4.0 in 2 years of grad math at an Ivy league university who got significantly worse admissions results than another applicant from a comparable institution with a 3.7 undergrad GPA, 1 grad micro course, and 1 year of RA experience.

 

Conditional on already having rigorous grad econ courses, I advise *every* applicant against taking grad real analysis unless they have a substantiated belief they'd be specializing in micro theory or econometric theory. By substantiated belief, I mean something like 1 year of working in a supervised research project in either of those topics. Chances are you should already be enrolled in a grad econ program by the time you can make that decision.

 

I think it's alright to look at my profile. They are published and public and everyone can see it. But you turn an open discussion into attacks and as an economist, I'm not sure if anyone will respect you even though your view might be correct.

 

As for my view, I think OP has the ability to do both grad real analysis and RA concurrently and does an excellent job in both, which is why I recommend OP to do both at the same time.

 

Of course, you can challenge my view, by arguing that the selection committee don't care about maths as long as it's sufficient, or OP already has a good preparation in maths, etc. I don't know what the selection committee at Top 20 think so what you said might be correct. As for the preparation, there's no precise way to define what constitutes a good preparation. You might think OP is very strong in maths, while I think it could be slightly improved.

 

Also are good RA jobs that easy to land on, and those jobs could fill in the gap of 6 months (which I would assume is the time OP has before applying for Fall 2020 entry)? These are the questions that you need to think about when recommending OP to devote all his time into RA jobs, while grad real analysis course is available for him to take.

 

How much time does the grad real analysis take up? For me I don't think it is a huge expenditure of time. It's just like any normal courses, which is why I think real analysis is a must (not taking a huge amount of time while credibly signal any maths ability). But if OP finds it otherwise and doing grad real analysis affects the performance on RA, then of course RA should be prioritized. I have never said maths is more important than research and you have wrongly accused me.

 

You also mentioned the coding issue, and I did forget to mention that OP could improve coding since he's doing empirical stuff. However, I am dedicated to be a micro theorist so coding is not as important as maths (although I do agree having more coding is not harmful), and it seems to you not having coding is one of the reasons why I'm an underperformer to you.

 

We are all trying to help, and I do get a lot of suggestions on Urch. After I am admitted to my dream program I thought I should come out and help others, just like how people used to help me. I respect you in the way that you helped a lot of people over the past years, but what you said is just disgusting and show no respect as an top economist.

Edited April 24, 2019 by leolin

[therealslimkt]

The OP is clearly not lacking math rigor. Its probably uncommon to have all those other courses and not analysis, but in this case it will probably help. Furthermore, real analysis is usually seen as desirable because it shows ability to handle the first year: the OP has already taken graduate micro and is getting a (hopefully strong) letter from that prof. Logically nothing points to real analysis as being the weakest link here. Graduate analysis seems like a bad use of time.

 

This looks like many profiles posted here in the past where more experienced posters said "get a full time RA job and you are a top 10 shoo in". I would defer to chateau on their estimation that its not strictly necessary for top 20/Canadian top 5, but your admission chances are monotonically increasing in RA experience, and you always want to get accepted to the place you're on the margin for...

[JonSnowLives]

I disagree strongly with leolin's advice. It's ridiculously off the mark.

 

You have a MA from a top Canadian program, and you were top of your cohort. In terms of academic coursework, you are probably in the 98th+ percentile *within* the selective pool of applicants to top 20 programs. Additionally, your research interest and experience seem to be in empirical work. There is very little value, in either the short run or long run, from taking further graduate math. You're set for PhD applications. Any marginal effort should be focused on RA experience and building rapport with your letter-writers - but even that is not necessary for getting into a top Canadian PhD (or a equivalent top 20 program in the USA). Ask your professors what proportion of MA students they admit into their PhD programs per year just based on their master's performance, and chances are you're well within the quota. There's no uncertainty here.

 

You've received conflicting advice, so let me explain the difference in our experience here. I personally took grad real analysis as a 19 year old, and another grad probability course at 20. I don't think they helped me a single bit in my graduate econ career. I've never looked back into Billingsley (the reference text) even once after I finished the course. And, judging from my conversation with two top 5 adcom members after my application season, the grad math courses were not key to my application success. They were, on the other hand, a huge expenditure of time.

 

Looking back at leo's profile, on the other hand, he's a current applicant in this cycle, and has an unusually lopsided profile himself - little coding and too much math, as other posters had pointed out to him. He also seems to be underperforming his expectations in this cycle. I realize I'm being a jackass by digging up another poster's background, but it's bizarre to me that an underperforming applicant doesn't seem to realize his understanding of the admissions weights is flawed, and is now offering the same wrong approach to other applicants. Flagrantly wrong advice like this - the undue obsession with grad-level math and lack of emphasis on research experience - is frustrating as hell, especially when we've been pointing it out on this forum for 5 consecutive years, and every cycle we have dozens of cases reaffirming the forum's consensus. We've had an applicant with 4.0 in 2 years of grad math at an Ivy league university who got significantly worse admissions results than another applicant from a comparable institution with a 3.7 undergrad GPA, 1 grad micro course, and 1 year of RA experience.

 

Conditional on already having rigorous grad econ courses, I advise *every* applicant against taking grad real analysis unless they have a substantiated belief they'd be specializing in micro theory or econometric theory. By substantiated belief, I mean something like 1 year of working in a supervised research project in either of those topics. Chances are you should already be enrolled in a grad econ program by the time you can make that decision.

 

Thanks a lot for shedding new light into the topic ! Students from previous years have been placed at top Canadian programs (Toronto, UBC, Queens) constantly and at top places in the US (Stanford, Princeton, Cornell, Penn, and few others I heard) and in the UK (LSE, UCL, and so on). I am trying to get some RA experience and was mulling over taking the Analysis class because I have time.But RA experience is top on my list. And my LOR's should be good I believed because my proffs know me very well in those grad classes ( I had a really good rapport with them).

[JonSnowLives]

The OP is clearly not lacking math rigor. Its probably uncommon to have all those other courses and not analysis, but in this case it will probably help. Furthermore, real analysis is usually seen as desirable because it shows ability to handle the first year: the OP has already taken graduate micro and is getting a (hopefully strong) letter from that prof. Logically nothing points to real analysis as being the weakest link here. Graduate analysis seems like a bad use of time.

 

This looks like many profiles posted here in the past where more experienced posters said "get a full time RA job and you are a top 10 shoo in". I would defer to chateau on their estimation that its not strictly necessary for top 20/Canadian top 5, but your admission chances are monotonically increasing in RA experience, and you always want to get accepted to the place you're on the margin for...

 

Thanks so much!

[Rohanps]

I disagree strongly with leolin's advice. It's ridiculously off the mark.

 

...

 

Looking back at leo's profile, on the other hand, he's a current applicant in this cycle, and has an unusually lopsided profile himself - little coding and too much math, as other posters had pointed out to him. He also seems to be underperforming his expectations in this cycle. I realize I'm being a jackass by digging up another poster's background, but it's bizarre to me that an underperforming applicant doesn't seem to realize his understanding of the admissions weights is flawed, and is now offering the same wrong approach to other applicants.

 

I thought personal attacks weren't allowed on here; or is it one rule for some and a different rule for others?

[leolin]

I disagree strongly with leolin's advice. It's ridiculously off the mark.

 

You have a MA from a top Canadian program, and you were top of your cohort. In terms of academic coursework, you are probably in the 98th+ percentile *within* the selective pool of applicants to top 20 programs. Additionally, your research interest and experience seem to be in empirical work. There is very little value, in either the short run or long run, from taking further graduate math. You're set for PhD applications. Any marginal effort should be focused on RA experience and building rapport with your letter-writers - but even that is not necessary for getting into a top Canadian PhD (or a equivalent top 20 program in the USA). Ask your professors what proportion of MA students they admit into their PhD programs per year just based on their master's performance, and chances are you're well within the quota. There's no uncertainty here.

 

You've received conflicting advice, so let me explain the difference in our experience here. I personally took grad real analysis as a 19 year old, and another grad probability course at 20. I don't think they helped me a single bit in my graduate econ career. I've never looked back into Billingsley (the reference text) even once after I finished the course. And, judging from my conversation with two top 5 adcom members after my application season, the grad math courses were not key to my application success. They were, on the other hand, a huge expenditure of time.

 

Looking back at leo's profile, on the other hand, he's a current applicant in this cycle, and has an unusually lopsided profile himself - little coding and too much math, as other posters had pointed out to him. He also seems to be underperforming his expectations in this cycle. I realize I'm being a jackass by digging up another poster's background, but it's bizarre to me that an underperforming applicant doesn't seem to realize his understanding of the admissions weights is flawed, and is now offering the same wrong approach to other applicants. Flagrantly wrong advice like this - the undue obsession with grad-level math and lack of emphasis on research experience - is frustrating as hell, especially when we've been pointing it out on this forum for 5 consecutive years, and every cycle we have dozens of cases reaffirming the forum's consensus. We've had an applicant with 4.0 in 2 years of grad math at an Ivy league university who got significantly worse admissions results than another applicant from a comparable institution with a 3.7 undergrad GPA, 1 grad micro course, and 1 year of RA experience.

 

Conditional on already having rigorous grad econ courses, I advise *every* applicant against taking grad real analysis unless they have a substantiated belief they'd be specializing in micro theory or econometric theory. By substantiated belief, I mean something like 1 year of working in a supervised research project in either of those topics. Chances are you should already be enrolled in a grad econ program by the time you can make that decision.

 

One last thing, I won't tell you the name, but the underperformer did get to a top theory school outside top 20, which is exactly my expectation.

Edited April 24, 2019 by leolin

[Kaysa]

Chateu's advice is spot on. Leolin's intentions might be in the right place but leolin's advice is wrong.

 

OP's profile is solid. Landing an RA would be great as long as OP's mentor actually provides good guidance on how to do research, or is able to write OP a solid recommendation letter. Getting such an RA is easier said than done though. Learning python often helps.

[JonSnowLives]

Chateu's advice is spot on. Leolin's intentions might be in the right place but leolin's advice is wrong.

 

OP's profile is solid. Landing an RA would be great as long as OP's mentor actually provides good guidance on how to do research, or is able to write OP a solid recommendation letter. Getting such an RA is easier said than done though. Learning python often helps.

 

Thanks !! Is python significantly different than MATLAB/STATA/R ?

[startz]

Thanks !! Is python significantly different than MATLAB/STATA/R ?

Matlab and R are general purpose programming languages with a focus on matrix and scientific/statistical applications. Stata is a statistical package with a programming language tacked on.

 

Python is a general purpose programming language that is used in a wide variety of arenas. If you can program well in Matlab or R then learning Python is not that hard. Being good at Stata doesn't necessarily get you to the same point.

[leolin]

Ithought personal attacks weren't allowed on here; or is it one rule for someand a different rule for others?

 

Thanks for standing up for me. I have dedicated rationaland non-attacking responses to him and I do not wish to have more conversation withuncivilized person, which is a waste of time.

 

Edited April 24, 2019 by leolin

[Kaysa]

Thanks !! Is python significantly different than MATLAB/STATA/R ?

 

Python is most similar to R. I recommend python because it is fast, easy to read, and is the best at scraping data. Scraping data is in demand because it is a great way to construct a novel data set.

 

Matlab is very niche. I never used it, and most of my colleagues don't use it either.

 

Stata is easy. You can pick up the basics in a couple hours, and in most cases that it all you will ever need to do most economic reasearch. However, in some cases stata makes simple tasks complicated and complicated tasks very laborious.

 

R is harder to learn than stata, but it has an impressive statistical library. Unfortunately, that library also has many bugs and quality control issues are prevalent. R is also slow and difficult to read. I quit using R because I could not read my old code for past empirical projects.

 

Python is harder to learn than stata, but it is still not that hard to learn. It's documentation and online resources are excellent, unlike stata. It's statistical library is good and growing. I like python because it is very easy to read. You can go back to old projects and pick up from where you left off quickly. It is also the best at scraping data, which is important right now. A lot of novel data sets are starting to come from scraping websites or other sources.

 

I will note that R is easier when it comes to running any analysis, but python is way easier when it comes to data cleaning. Most projects are 90% data cleaning, which is why I really like python.

[JonSnowLives]

Python is most similar to R. I recommend python because it is fast, easy to read, and is the best at scraping data. Scraping data is in demand because it is a great way to construct a novel data set.

 

Matlab is very niche. I never used it, and most of my colleagues don't use it either.

 

Stata is easy. You can pick up the basics in a couple hours, and in most cases that it all you will ever need to do most economic reasearch. However, in some cases stata makes simple tasks complicated and complicated tasks very laborious.

 

R is harder to learn than stata, but it has an impressive statistical library. Unfortunately, that library also has many bugs and quality control issues are prevalent. R is also slow and difficult to read. I quit using R because I could not read my old code for past empirical projects.

 

Python is harder to learn than stata, but it is still not that hard to learn. It's documentation and online resources are excellent, unlike stata. It's statistical library is good and growing. I like python because it is very easy to read. You can go back to old projects and pick up from where you left off quickly. It is also the best at scraping data, which is important right now. A lot of novel data sets are starting to come from scraping websites or other sources.

 

I will note that R is easier when it comes to running any analysis, but python is way easier when it comes to data cleaning. Most projects are 90% data cleaning, which is why I really like python.

 

I am good with R so Python seems doable.

[chateauheart]

[deleted] Edited April 25, 2019 by chateauheart
rude

[Kaysa]

I am good with R so Python seems doable.

Absolutely. Python is much easier to do then R, and its piping capabilities are really nice once you get them running.