Serena Ng

Highly recommended course. I never thought Econometrics would be so understandable. She really goes through everything in detail, skipping over what she says is of a difficulty beyond the scope of this course (she acknowledges her own hand-waving), and the tests are quite fair. She repeats the basics constantly and, if anything, you come out of this course with a very solid framework for thinking about Econometrics from a more advanced level than in Intro. But the math is really not that hard. The hardest thing you must do in terms of computation is matrix multiplication, which is pretty basic. The difficulty in the course, I think, comes from the more difficult topics like Asymptotics, but towards the end of the course it'll all be very, very clear if you stick it through. Like a previous reviewer, my math background was only the Calc I, Calc III, and Intro. to Stats required for the major, plus of course Linear Algebra (getting only a B+ there), and I did fine in the course and found it to be enjoyable and very clear and useful. It's definitely a worthwhile course, with not as bad a workload. Serena is also charming in her own quirky way, and the TA for the semester (Paul) was helpful as well (though I think he's graduating this year so he may not be around for the next course). The hardest part may have been, in all honestly, the 7 problem sets (the TA wrote them all, I thi…

First 15 chapters of Hansen econometrics textbook. OLS, asymptotics, hypothesis testing, IV, 2SLS, GMM, Extremum estimators, Panel Data.

I completed the first year phd econometrics .

X PRIME X INVERSE X PRIME Y After this class you will be reverently whispering this. In your sleep. Forever. You will rudely snap at wee passerby econ majors making snide remarks about econometrics. Never speak ill of econometrics. Perhaps only our fellow brethren of 4412 will understand the stunning beauty of GMM and time series. Great class, probably the best economics class you'll take at Columbia. Serena starts at the basics of econometrics and derives most things out of the building blocks. Her class is definitely worth taking notes for, as her lectures beat both textbooks for the class. It is clearly more mathematical than intro, and I'd say a background in probability and statistics (i.e., not 1211) should be a pre requisite. One attraction is the close association of the material we learn in class to academic economic history and current research. We get to know, but are not required to learn, about econometricians and econometrica. In short, she gives Susan Elmes a run for her money. The problem sets require MATLAB. So you'll learn MATLAB. Paul's problem sets help us practice what we learned in class. The experience of running basic simulations by itself is invaluable. Spend time on them. Savor them. A few things though. I hope Serena would consider posting her notes online. Her class lectures are clear, though there are a few organization issues when they go on the b…

Ordinary Least squares, hypothesis testing, penalized regression, bootstrapping, IV, Panel Data, GMM

We learned a huge amount of topics related to macroeconomic methods in econometrics.

tl;dr version: If you're interested in serious economics, like statistics and don't have an allergy to linear algebra- you owe it to yourself to at least try this course. It may be one of the best you take at Columbia, and if it isn't for you, you'll know from the start. Prof. Serena Ng is also one of the most perfect professors I've had the privilege to learn from. One of the best classes I've ever had at Columbia, due primarily to Prof. Ng's amazing teaching. And that's no trivial or predictable statement for a class titled "Advanced Econometrics". If you liked Econometrics or Statistics (and even mores if you also like Linear Algebra), and are willing to put in some effort, this is the class for you. You'll know off the bat whether this is a class you want to take: the first few lectures are a very good sample of the rest of the course, and she'll give a tough tone to make clear to everyone that this is not a light course. However, once the drop date passes (and even before), she'll show more of her friendly, approachable side, and this is what really matters. If you ever go to office hours you'll find a friendly listener; after warning people in class that those who weren't serious shouldn't stay, I became a bit worried and asked her personally if I should stay during office hours. To my surprise, she actively encouraged me to stick with the class. I can't quite explain ju…

Linear regression in matrix notation with small sample and asymptotic theory, GMM, tools for panel data, bootstrapping, penalized regression, IV regression.

(i) Setting up numerical optimization-type problems, e.g. covariance structure estimation, indirect least squares, GMM estimation. (ii) Kalman Filtering (iii) Bayesian computation techniques, e.g. Gibbs sampling, Metropolis-Hastings. (iv) Time series concepts, including diagnostics, VAR, SVAR.

I want to echo the review below. This was one of my favorite classes here at Columbia, mainly due to Professor Ng being an excellent teacher. She told us that she had entirely redone her syllabus, tossing out the stock slides she used in the past and making her own materials. She also teaches what the class wants to learn and gives the students a choice over what the last couple of topics will be (she gave us a choice between VAR or PROBIT/LOGIT). I went into this class with a very weak math background (stats 1211, Calc III, and linear algebra) but the course was still entirely manageable. Professor Ng teaches to make sure you understand the concepts and is not as concerned with whether you can crunch numbers or know everything about statistical derivations. She repeats the important concepts a lot and it was always very clear what she thought was important and what material we would be tested on. She can be a little stern in class (like Professor Elmes), but I like that style of teaching and gained a lot from it.