Top products from r/econometrics

Both time series and regression are not strictly econometric methods per se, and there are a range of wonderful statistics textbooks that detail them. If you're looking for methods more closely aligned with econometrics (e.g. difference in difference, instrumental variables) then the recommendation for Angrist 'Mostly Harmless Econometrics' is a good one. Another oft-prescribed econometric text that goes beyond Angrist is Wooldridge 'Introductory Econometrics: A Modern Approach'.

For a very well considered and basic approach to statistics up to regression including an excellent treatment of probability theory and the basic assumptions of statistical methodology, Andy Field (and co's) books 'Discovering Statistics Using...' (SPSS/SAS/R) are excellent.

Two excellent all-rounders are Cohen and Cohen 'Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences' and Gelman and Hill 'Data Analysis Using Regression and Multilevel/Hierarchical Modelling' although I would suggest both are more advanced than I am guessing you need right now.

For time series I can recommend Rob Hyndman's book/s on forecasting (online copy freely available)

For longitudinal data analysis I really like Judith Singer's book 'Applied Longitudinal Data Analysis'.

It sounds however as if you're looking for a bit of a book to explain why you would want to use one method over another. In my experience I wanted to know this when I was just starting. It really comes down to your own research questions and the available data. For example I had to learn Longitudinal/fixed/random effects modelling because I had to do a project with a longitudinal survey. Only after I put it into practice (and completed my stats training) did I come to understand why the modelling I used was appropriate.

There are lots of books on Amazon, but this seems to be what you'd be looking for: http://www.amazon.com/Applied-Econometrics-R-Use/dp/0387773169

Personally, I wouldn't spend too much money on a book teaching you how to code. When it comes to programming (especially higher level data programming) there are tons of free resources online which can help you figure out what to do. This is likely the best way for you to go with regards to preparing for a job using R, SAS, Stata, Python, ect. since you'll likely be asked to program something on the job and have to use online sources to learn how to do it. Additionally, I'd advise you to look into resources such as this: http://www.r-bloggers.com/how-to-learn-r-2/ which lead you through data programming and analysis in R. Hopefully this was helpful!

P.S. Python is extremely useful (maybe not particularly for econometric analysis) and I wouldn't be dissuaded from learning it if I were you--lots of employers like to see it on a resume.

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.

This is indeed a good book, but it really has an algebraic approach to the field. It is def. useful, but lacks a lot in treatment of probabilistic aspects. There is a huge focus on Gauss-Newton regressions which you might not need now. Bootstrapping is also very prominently displayed (research area of the authors).

If you like the style though, consider http://www.amazon.com/Estimation-Inference-Econometrics-Russell-Davidson/dp/0195060113 by the same authors. It gives a better treatment.

If you have a pre-specified functional form that you are using to fit data, like the solow growth model, it is a bit different from econometric estimation, the term typically used is "Calibration."

I don't have it on hand, but IIRC this book ABC's of RBCs does a lot of empirical examples, and at least has some code samples using the Solow growth model.

The best book is probably the following book;

https://www.amazon.com/Econometric-Methods-Applications-Business-Economics/dp/0199268010

You do need some prior knowledge of statistics, algebra (some economics can't hurt) and calculus if you want to go through it effectively.

http://www.amazon.com/Econometric-Methods-Applications-Business-Economics/dp/0199268010

I had this book, apart from Wooldridge and Greene later on, in an introductory course in my Master's program. It has helpful exercises and has a very nice approach of easing one into more and more matrix algebra.

Well I don't have that book, but I have heard it is excellent. The Gelman book I was referring to in my last post is this one

First of all, I recommend you make Python your first language.

Secondly, econometrics is reasonably straightforward when taught well. The equations and derivations are reasonably straightforward. I'd recommend reading Wooldridge's book, which is very simple and straight forward.

Thirdly, the choice between Python 2 and 3 for econometric work is immaterial, so it won't have a dramatic impact on your work either way. I'm too lazy to convert to Python 3, so I use 2.7.

Lastly, as a point of reference, I started programming with STATA, then moved to R, and then moved to Python.

What's your field of interest anyways? Economics?

Edit: The one other book I will throw out there if you are serious is Stachurski's A Primer on Econometric Theory. It's one of those books I wish I had earlier. It fills in all the background that many books or courses expect you to have.

Mostly Harmless Econometrics. Explains things at an undergraduate level but still a good resource for looking back on at all levels. (I have a PhD in Econ and have this on my shelf.)

Honestly, I don't think there's a lot of good books written on pricing and bundling yet. Power Pricing is good Nagel, Hogan, Zale is also good, but neither are too technical because... you know... marketing.

On the stats side, depending on what kind of shop you're with and what data you have, you'll be estimating demand curves, and doing conjoint analysis. Those are still state of the art afaik.

I've seen tim salimans reference this book:
http://www.amazon.com/Contemporary-Bayesian-Econometrics-Statistics-Probability/dp/0471679321

Kind of expensive though.

Two recommendations for asset pricing/financial econometrics
Cochrane (2005): https://www.amazon.com/Asset-Pricing-John-H-Cochrane/dp/0691121370
Campbell, Lo and MacKinlay (1998): https://www.amazon.com/Econometrics-Financial-Markets-John-Campbell/dp/0691043019

1. Mostly Harmless Econometrics
   
2. Econometrics for Dummies

Wooldridge's introductory text is the standard.

Hamilton