Reddit reviews Time Series Analysis

Free pdf of this book. We used it in our grad class and I thought it did a good job in most instances.

A ton of people seem to like the one by Hamilton. I have never read it but it got high reviews. It seems to be written for Econometricians more so than statisticians.

The important part of this question is what do you know? By saying you're looking to learn "a little more about econometrics," does that imply you've already taken an undergraduate economics course? I'll take this as a given if you've found /r/econometrics. So this is a bit of a look into what a first year PhD section of econometrics looks like.

My 1st year PhD track has used
-Casella & Berger for probability theory, understanding data generating processes, basic MLE, etc.

-Greene and Hayashi for Cross Sectional analysis.

-Enders and Hamilton for Time Series analysis.

These offer a more mathematical treatment of topics taught in say, Stock & Watson, or Woodridge's Introductory Econometrics. C&B will focus more on probability theory without bogging you down in measure theory, which will give you a working knowledge of probability theory required for 99% of applied problems. Hayashi or Greene will mostly cover what you see in an undergraduate class (especially Greene, which is a go to reference). Hayashi focuses a bit more on general method of moments, but I find its exposition better than Greene. And I honestly haven't looked at Enders or Hamilton yet, but they will cover forecasting, auto-regressive moving average problems, and how to solve them with econometrics.

It might also be useful to download and practice with either R, a statistical programming language, or Python with the numpy library. Python is a very general programming language that's easy to work with, and numpy turns it into a powerful mathematical and statistical work horse similar to Matlab.

Definitely read both books, although you may appreciate Cochrane's more if you like asset pricing. Keep an open mind -- in my experience many students enter PhD programs planning on doing asset pricing, then move to corporate finance. That is what I did.

I'm not aware of any recent machine learning asset pricing papers in the top journals, so I can't point you in that direction (although, to be honest, I don't do asset pricing, so I'm not the best resource). You may like the Santa Fe market -- which is a simulated financial market. It's a little old, but you may find value in it.

If you really like econometrics, I'd suggest taking a look at Hamilton's Time Series Analysis or Campbell, Lo, and MacKinlay's The Econometrics of Financial Markets. They're both very mathy, as they are more about the econometric techniques used in empirical research than they are about the research itself. You'll likely run into one or the other in your grad program.

For time series analysis, which you would use to do forecasting, you can't beat Hamilton's Time Series Analysis if you really want to do it right. Supplementing it with a less technical text like Nate Silver's book, as /u/granito suggested is a good idea. For econometrics outside of time series I second Angrist and Pischke's Mostly Harmless Econometrics. Unfortunately, I don't know of a great time series book with a similar style to MHE.

Hamilton