Best 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.

Well, yes, but his book dedicated to exponential smoothing is the better source.
Forecasting with Exponential Smoothing: The State Space Approach (Springer Series in Statistics) https://www.amazon.com/dp/3540719164/ref=cm_sw_r_cp_api_uGH8Bb3W8X7ZV

yeah wooldridge is so solid! there's so much complimentary material out there as other people have said. also lots of solution sets!

Personally like Doughtery's Introduction to Econometrics, it's got a nice basic tone. tries to provide intuition. v accessible

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.

There are a few approaches you can take:

• ignore the missing data: such as use only complete cases- sounds like this is what you are currently doing.
  
• impute missing data values using techniques. Some of these are as simple as imputing the mean average of the attribute to where it is missing.
  
• use a different analytic technique that can handle missing data- many of these are developed from machine-learning, so there might be issues with communicating your analytic process to economists.
  
  You are probably going to have to develop a approach based on a combination of a variety of techniques, which is a different mindset compared to the way most people do regression. I would suggested looking at some resources like this book, Applied Missing Data Analysis.
  
  While this is a big topic that has generated a lot of research, I think it is a pity that it is often hard to find good examples of how to handle missing data that are accessible to practitioners.
  
  I don't know what tools you are using for analysis, I would suggest R. It has a variety of packages that can help with missing data, especially imputation, such as Amelia and mice.
  
  Sounds like you have a nice meaty problem on your hands, congratulations and good luck!

Stock and Watson's introductory text is a pretty good starting point. Plus I'm sure you'll find a PDF online somewhere.

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.

I recommend you look into a copy of Peter Kennedy's "Guide to Econometrics." I found that book to be much more useful than most econometrics texts. There are at least a dozen textbooks from both undergrad and grad school on my shelves at home, but the one I keep in my office is Kennedy. The handy thing about it is that each section is divided into three sections, the first in plain language, the second a mix of language and maths, and the third fully technical maths (like you would see in most textbooks).

There are also a lot of good online resources. Check out Ben Lambert's youtube channel.

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.

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.)

https://economics.sas.upenn.edu/big-data-papers-slides

I would also recommend this book
https://www.amazon.com/Structural-Vector-Autoregressive-Analysis-Econometrics/dp/1316647331/ref=sr_1_1?ie=UTF8&qid=1515678181&sr=8-1&keywords=lutz+kilian

Hamilton

Wooldridge's introductory text is the standard.