OneArmedEcon

One other thought: it's very much the case that different disciplines have very different "language" and developing "fluency" with various disciplines is essential if you're interested in doing interdisciplinary work. And just like learning a foreign language, the best way to gain proficiency is through regular interaction/immersion. And it's not always fun, but you can often learn something from an unpleasant interaction that helps you better interface in future conversations.

I'll emerge from my long hiatus to briefly comment since I was one of the people who first suggested to H-man that he might have greater freedom to explore his interests in sociology than economics. My first thought is that the first year of a PhD program is unlike the remainder of your academic career in that you are taking classes with an entire cohort, many of whom do not share your interests or even worldview. Economics is more epistemologically homogenous than other social sciences, so there's more common ground--but there are still differences in basic values, such as how to reconcile the tradeoff between equity and efficiency, that will impact policy preferences (if that's what your interests are). Fortunately/unfortunately, in economics you seldom get into real-world applications in a first year of an econ program, so these potential disagreements are less likely to arise. The good news is that by the second year of your program, you'll be taking courses that do interest you and interacting mostly with people who share those interests. And once you get past the coursework, you'll interact regularly with an even smaller group of people. I can identify with not being in accordance with the prevailing political atmosphere. When I'm around economists discussing educational policy, I'm typically far left in terms of policy preferences, concerns, etc. But around education researchers, I'm often considered too conservative because of how I analyze issues (even when my policy preferences are essentially the same as a critical theorist's, for example). But, ultimately, none of these frictions really matters in the long run. So my advice (not just to H-man, but all grad students) would be to engage with people with whom conversing is an engaging and enlightening experience--often times that will be with people with whom you have fundamental epistemological disagreements--and circumvent those who have little to offer in terms of broadening your thinking. And, as I mentioned earlier, as you move through your program, that will necessarily include a much smaller set of people at your home institution, but hopefully you'll make connections outside your program that will be rewarding. And quite honestly, the "friends" you have outside the program will probably serve your professional aspirations more in the long run.

Look at their CV and enter whatever they list as their current, primary academic position.

I believe that part of what the USNWR ratings take into account is acceptance rate. Given that Vanderbilt doesn't charge an application fee (or at least they didn't when I was an applicant) and being a well-funded private university with a small program, I would imagine that their acceptance rate is far, far lower than comparably ranked (i.e., Top 30-50ish) programs.

If your objective is to be well-prepared for first year metrics, then that course will likely be of minimal value. It sounds to me that the course will be a survey of advanced econometric methods where breadth is given priority over depth. In most first year metrics sequence, the focus is on depth of the basics (excruciating depth, actually) whereas it's not until 2nd year and beyond that you get into these more advanced methods. If your faculty adviser is saying not to take it, then listen to him.

To add to what tm_member said... the problem isn't necessarily with how the adcom will interpret the low ranked publications for purposes of admission. Most would realize that you're going to write papers after several years in a PhD program and that those papers were written as undergrad. Rather, when you go on the job market, search committee members will give your CV a very cursory look as they're trying to figure out who to offer an interview to during ASSA. Since many grad students don't publish much (if at all) in graduate school (in large part because you only want to publish in highly ranked journals), the low ranked publications will be the only thing that will appear on the CV. It's possible that it will not occur to them that those publications were produced before the grad program (bear in mind that they're reviewing hundreds of CVs and choosing which 20-30 to interview, so it's a very cursory review). So there is a very real possibility that they will make an inference about your present research capabilities. That said, the OP's publications are in outlets so low ranked (no offense) that a search committee member would easily pick up on the fact that they were done prior to grad school, since no academic adviser in their right mind would allow a student to publish in a journal with an impact factor that is essentially a rounding error. The potential for false inference is greater for known-but-mediocre journals. On the other hand, they're probably not going to help his admissions chances at all.

Intro (or Baby Wooldridge) is on a comparable level to Stock and Watson. Wooldridge's other text (it's actually Cross-Section and Panels, not Time Series) is a very advanced metrics text that is typically taken in a 2nd or 3rd year of a PhD Econ program. It's what applied econometricians keep on their bookshelves for reference. That is, it's intended for an audience who has taken at least a full year of PhD Econometrics. Las fall I took the first semester of Panel Data course (actually from Wooldridge). It was one of the more difficult courses that I've ever taken and, in addition to a full year of PhD Metrics, I took a full year of MA-level probability and statistics (based on Casella and Berger) the year before I started my doctorate. I would not recommend the Wooldridge's advanced text for self-study, at least not if all you have taken is BA-level Metrics and MA-level Stats. My recommendation would be to work through Bruce Hansen's lecture notes before attempting advanced Wooldridge. They're free and cover the same material as something like Hayashi or Greene (those are the two most popular first year metrics texts--Greene is very comprehensive, but probably not ideal for self-study). I also found Goldhaber's "A First Course in Econometrics" to be very helpful for developing intuition with respect to the classical linear model. Here's a link to Hansen's lecture notes: http://www.ssc.wisc.edu/~bhansen/econometrics/Econometrics.pdf

Not every employer will even ask to your transcripts and, even among those who do, many won't put a lot of weight on mediocre grad school grades. The only future option that you're imperiling by poor academic performance (and dropping out of a PhD) is doing a different doctorate down the road.

If you are absolutely sure that this isn't what you want to do, then walk away now and do something that you will enjoy. Life is too short and there's a huge opportunity cost to doing a PhD. If you are positive that it's not what you want to do, then the sooner you make the change the happier that you will be. About half of people who enter PhD programs will not finish, most of whom leave a program by their choice (at least in part). As others have said, the nature of the challenges change after your first year, but there are still significant ups and downs throughout the entire program. It has to be something that you want for yourself and if you've decided that it's not something that you want to do, then go find something else. The worst reason in the world for staying in a PhD program is because you're afraid of what other people will think of you if you leave. Be sure that it's not mid-semester blues or whatever, which it may or may not be. Without knowing you, no one on an online forum can really diagnose that. So I would talk about your situation with people who know the pre-grad school you (probably best to just discuss it with people outside your program until you've made a final decision). Logistically, if you already have a Masters and don't see marginal value in getting one from your current institution, then there's no point in sticking around for the full first year. It may even make sense to not even finish the semester--just be sure that you've completed enough of the fall semester to make sure that you don't have to return any fellowship money or otherwise incur any financial penalty (if you have a union, that's worth an email to clarify dates). Without knowing you and your actual situation, I'm not sure that there is too much more that can be said other than be absolutely sure that leaving is what you want. I seriously would talk it through with people who know you--although a high level of introspection will be required, it's helpful to get feedback. If it's for the right reasons, then it makes the most sense to leave as soon as practically as possible. It might be helpful to make an appointment with someone at your campus counseling center, not because you're crazy but often they can provide some perspective.

Random thought... the adoption of EJMR lingo and memes is not a particularly helpful development for TM, IMHO.

Look at the editorial board of the leading field journal. Look at the program committee for the field's major conference (e.g., SOLE for labor). Look through recent issues of JEP/JEL and find who has been invited to write articles for fields/topics that you're interested in.

I don't really know that you develop much in the way of economic intuition (as compared to a typical intermediate micro course) by studying consumer/producer theories with more math. Since nearly all students in a MA program will have taken intermediate micro, it just seems redundant. On the other hand, the typical intermediate micro course does not cover game theory in as much detail. In terms of developing economic intuition, understanding strategic interaction is far more helpful than slightly more mathematically intense applications of optimization theory. Ideally IMHO, a MA program would have a required conventional micro course and a separate game theory course. I would eliminate Macro as a required course.

I used Perloff in my BA-level intermediate micro and then Nicholson in my MA-level advanced micro. Both are more mathematically technical than Baby Varian (which was the text for the intermediate micro that I TAed for). My recollection is that Perloff was more demanding than Nicholson, but part of that might be because it was my first time engaging with micro theory. I only used Nicholson as a reference when I was taking my MA-level course. If I were going to teach intermediate micro, I'd choose Baby Varian. I don't know about a MA-level micro theory course... maybe Grad Varian? I found Grad Varian to be a very useful supplement to MWG when I was doing Fall PhD Micro and it probably would have been more helpful to me than Nicholson. Kreps also has a new graduate-level micro theory text that is supposed to be very good and more intuitive than MWG, although I'm not too familiar with it since it came out a few years after I did my first year courses (it's sitting on my bookshelf, though). My final thought is if a MA Econ program had only one micro theory course, a very strong argument could be made for doing mostly game theory rather than full treatment of producer and consumer theories. But I've strayed far from the OP's question.

If you find yourself hating your life two months into starting something that you worked hard to get into, then grad school is right up your ally.

I don't know. Maybe Heckman has influence over state legislatures?

At some places stuff like that would go under the professional development section of your CV.

What are you interested in studying? What do you want to do with the degree?

W's don't impact your GPA, but send a very negative signal with respect to your ability to persist. Employers won't care, but graduate programs will. If you can get a B or better (or even lower), then it's much preferred to a W, IMHO. One low grade in a lower division math class is not as harmful as a W. Tough it out. ODE's is tedious, but conceptually not difficult.

I'm trying to think of a scenario where "answering honestly" isn't your dominant strategy.

That's interesting, but different disciplines have different norms with respect to whether a mention of the software package is used within the article. For example, I have very seldom read empirical sociological journals that don't specify the data analysis software used (many actually specify the command; e.g., "xtreg"). On the other hand, in economics and public policy, that's far less common: the models are specified and the different estimators clearly defined. I suspect that SPSS's prominence in the "Google scholar" figures and so forth is that SPSS is most commonly used in disciplines whose journals require specification of any data analysis software used. In terms of prevalence of R on blogs and so forth, that is due not only to its popularity, but also because it's open-source and it really doesn't have official documentation in the same way that other analysis software does. Which is to say, I'm not really sure that you can infer anything about it's relative use from there being more blogs dedicated to R than others. I'll repeat what I advised earlier: learn to use the software package used by people with whom you're going to collaborate. No package strictly dominates (although some do certain things better than others), although I suppose one could argue that SPSS is strictly dominated.

Use whatever data analysis package that: (a) you're comfortable with; and (b) your immediate colleagues (especially co-authors) use. SAS is superior to Stata with respect to a few things (handling of really massive datasets and better native capabilities for producing graphics) while Stata does pretty much everything else as well as has an easier syntax (in my opinion). But for the vast majority of empirical researchers, either program (or R or even SPSS) is going to be adequate. If you have a newer i5 or i7 processor, plenty of RAM, and a multiprocessor version of Stata, the performance on most functions is going to be essentially the same as compared to the same machine running a current version of SAS. If you're looking at non-academic positions, SAS is probably a better line on your resume than Stata (although that's not universally true). And you can probably get access to the free webversion of SAS as a student (my university has it; not sure if all do). But once you know one program, it's much easier to learn another data analysis software package. So again, my advice is to use whatever your collaborators use so that you can easily work with eachother's syntax files.

The bias on the job market against interdisciplinary degrees is mostly academic positions. If you're doing empirical work, then in large part the methodological training is far more important than the theoretical. Get the best methodological training possible and a sufficient amount of theoretical knowledge and you'll have a marketable skill set for the private or public sector. Which, honestly, if your PhD is not from a top university, then you're very likely to wind up in one of those jobs anyway. So if an interdisciplinary degree makes you happy and you can acquire strong methodological skills that will make you marketable on the job market, then it's worth considering if you're not a Top 10-15ish candidate, IMHO.

You'd have a better idea of the details, Tommy Chong. ;) FWIW, here's what shows up whenever I register for classes at my university: FERPA Annual Notice Higher Education Opportunity Act (HEOA) – Student Consumer Information Federal Student Financial Aid Penalties for Drug Law Violations Federal law provides that a student who has been convicted of an offense under any federal or state law involving the possession or sale of a controlled substance during a period of enrollment for which the student was receiving financial aid shall not be eligible to receive any federal or institutional grant, loan, or work assistance during the period beginning on the date of such conviction and ending after the interval specified in the following table. If convicted of an offense involving: [TABLE=width: 100%] [TR] [TD] Possession of a Controlled Substance [/TD] [TD] Ineligibility Period [/TD] [/TR] [TR] [TD] First offense [/TD] [TD] 1 year [/TD] [/TR] [TR] [TD] Second offense [/TD] [TD] 2 years [/TD] [/TR] [TR] [TD] Third offense [/TD] [TD] Indefinite [/TD] [/TR] [TR] [TD] Sale of a Controlled Substance [/TD] [TD] Ineligibility Period [/TD] [/TR] [TR] [TD] First Offense [/TD] [TD] 2 years [/TD] [/TR] [TR] [TD] Second Offense [/TD] [TD] Indefinite [/TD] [/TR] [/TABLE] A student whose eligibility has been suspended based on a conviction for possession or sale of a controlled substance may resume eligibility before the end of the ineligibility period if: A. the student satisfactorily completes a drug rehabilitation program that: 1. complies with the criteria prescribed in the federal regulations; and 2. includes two unannounced drug tests; B. the student successfully passes two unannounced drug tests conducted by a drug rehabilitation program that complies with the criteria prescribed in the federal regulations; or C. the conviction is reversed, set aside, or otherwise rendered nugatory.

Just cover your bases and disclose it. The adcom won't care. The only way that it becomes an issue is that if it gets discovered by the registrar's office or something and the university has a policy against disqualifying any offer made to someone who misrepresents themselves on an application. The only other thing to be aware of about drug possession charges is that certain offenses can disqualify you from federal student loans, so keep that in mind if you're considering any offers that aren't funded in the first year.

I couldn't disagree more. A better hedge against a change in the delivery of postsecondary courses is to make sure that you maximize the skills that are valued by non-academic employers. Spending time on improving teaching capacities at the expense of improving your research potential and skillset is precisely the worst human capital investment decision that a current graduate student could make at this time.