Reddit Reddit reviews Econometric Analysis of Cross Section and Panel Data

We found 2 Reddit comments about Econometric Analysis of Cross Section and Panel Data. Here are the top ones, ranked by their Reddit score.

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Econometric Analysis of Cross Section and Panel Data
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2 Reddit comments about Econometric Analysis of Cross Section and Panel Data:

u/NellucEcon · 3 pointsr/AskSocialScience

I'm not sure about an online course, but I can recommend some econometrics textbooks.

Goldberger's "A Course in Econometrics" is well written and covers a lot of important ideas. I especially like his treatment of residual regression in chapter 17 (I think): https://www.amazon.com/Course-Econometrics-Arthur-S-Goldberger/dp/0674175441/ref=sr_1_1?ie=UTF8&qid=1465847395&sr=8-1&keywords=goldberger+econometrics

Many people teach regression as minimizing the squared residual from a linear model. While that's a correct way to think about it, in my opinion it is easier to understand regression as performing matrix algebra on a data-generating process. That is, a linear model says that x causes y according to

y = xb + e

where y is an observed column vector of length n (for number of observations) x is an observed matrix, possibly including a constant, e is unobserved, and b is a parameter (vector) to be estimated. Well, just do algebra on it.

you want to "move" x to the left-hand side, but x doesn't have an inverse. Instead, multiply both sides by the transpose of x, which is x', and then you have x'x in front of b. If this can be inverted, then multiply both side by it's inverse. (x'x)^-1 x'x cancels, yielding

(x'x)^-1 x'y=b+(x'x)^-1 x'e

if (x'x)^-1 x'e=0, then you have just solved for b. In expectation, this is true under the OLS assumptions, and as the sample gets large, it is approximately true in sample. This is why OLS can recover b if the error is orthogonal to x. If not, then OLS gives you biased estimates of the causal parameter b.

Regression algebra is indeed quite simple. This makes regression algebra satisfying -- you are doing something extremely powerful without requiring comparably sophisticated mathematical technology.

Anyway, Goldberger's treatment of regression algebra really clicked for me, especially making sense of residual regression (why "all else equal" makes sense). You don't need to read every chapter. Chapter 17 works pretty well on it's own, for example. But the other stuff is useful as well.


"Mostly Harmless Econometrics" is not too hard to read without coursework forcing you to focus: https://www.amazon.com/Mostly-Harmless-Econometrics-Empiricists-Companion/dp/0691120358/ref=pd_sim_14_6?ie=UTF8&dpID=51qgNUMbyXL&dpSrc=sims&preST=_AC_UL160_SR104%2C160_&refRID=NY8XZVBAX0ZHXXV69SAT

You might as well get Wooldridge's graduate level textbook on panel data econometrics -- you'll probably need to buy it in grad school anyway. It's hard to make sense of until until you've been forced to work through a lot of the math. After your first quarter or two of graduate level course work you should be comfortable enough with the material to teach yourself anything in this textbook. Before that though and you might not have the discipline or background to make heads or tails of this: https://www.amazon.com/Econometric-Analysis-Cross-Section-Panel/dp/0262232197/ref=sr_1_7?s=books&ie=UTF8&qid=1465847582&sr=1-7&keywords=wooldridge

u/jambarama · 2 pointsr/AskSocialScience

Beyond intermediate texts, my classes ended up just reading papers from econ journals. You may want to pick up an econometrics text, get familiar with the methods, then read papers (here is a list of the 100 most cited).

I wrote my opinions on econometric textbooks I've used for another reddit comment, so I just pasted it in below. If you get into it, I'd recommend reading a less rigorous book straight through, then using a more rigorous text as reference or to do the practice stuff.

Less Mathematically Rigorous

  • Kennedy - survey of modeling issues without the math. More about how to think about modeling rather than how do it. Easy to read, I liked it

  • Angrist - similar to Kennedy, covers the why & how econometrics answers questions, very little math. Each chapter starts with a hitchhikers guide to the galaxy quote, which is fun. Just as good as Kennedy

  • Long - this book is more about just "doing stuff" and presenting results, absolutely non-technical, but also dodges the heavy thinking in Angrist & Kennedy so I wasn't a big fan

  • King - covers the thinking of Angrist & content of Maddala. It is more accessible but wordier, so give it a go if Kennedy or Angrist are too much. It is aimed at Poli Sci rather than econ.

    Middle of the Road

  • Gujarati - I used this for a class. It wasn't hard to follow, but it mostly taught methodology and the how/why/when/what, and I didn't like that - a little too "push button" and slow moving.

  • Woodlridge - a bit more rigorous than Gujarati, but it was more interesting and was clearer about motivations from the standpoint of interesting problems

  • Cameron & Trivedi - I liked the few chapters I read, the math is there, but the methodology isn't driven by the math. I ddin't get too far into it

    More Mathematically Rigorous

  • Greene - lots of math, so much it was distracting for me, but probably good for people who really want to learn the methodology

  • Wooldridge - similar to Greene, you need a solid understanding before diving into this book. Some of the chapters are impenetrable

  • Maddala - this book is best for probit/logit/tobit models and is somewhat technical but dated. My best econometrics teacher loved it