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