Reddit reviews Econometric Analysis (7th Edition)

Trigger warning: MMT

Krugman's post today isn't too bad. Choice excerpt:

>Now, arguing with the MMTers generally feels like playing Calvinball, with the rules constantly changing: every time you think you’ve pinned them down on some proposition, they insist that you haven’t grasped their meaning.

...though Krugman is leaving himself room for embracing MMT in a "the enemy of my enemy is my friend" fashion as the political cycle heats up:

>I really don’t want to spend time arguing with the Modern Monetary Theory people; after all, we agree on basic policy issues right now, and they are never likely to have as much destructive influence as the deficit scolds.

But let's talk about the economics.

Krugman uses standard IS-MR logic to think through some MMT claims.
For an introduction to that model, please see here. (Link regrettably contains spaces, so let me know if it is broken on your browser.)
I think the MMTers will complain that he doesn't get it; that in MMT-land, the IS curve is vertical, and as such monetary policy is disconnected from aggregate demand.
In graphical terms, Krugman is arguing in terms of the second figure in this Nick Rowe post, while MMTers want to argue in terms of the third figure.

Now those figures mainly differ in their proposed interest elasticity of output. God, if only there were some discipline out there that could test economic propositions, and if only someone, somewhere, had investigated the interest elasticity of output. If either of those two things were true, we could adjudicate these debates and go home. Alas, nobody in the history of economics has ever written down a formal framework for testing economic propositions, and nobody has ever thought to
test
the interest sensitivity of output, so I guess we're stuck with writing walls of text at each other.

I love to argue about theory and foundations, but when the question boils down to, "Is the IS curve vertical?" then let's bloody well test whether the IS curve is vertical!

P.S. My challenge still stands.

Oh god, please don't be a Taleban.

Sure as anything, Taleb's books won't tell you anything but his opinion that everyone but him in finance and economics is shortsighted and stupid. All the while ignoring the agency problems that can explain catastrophic crashes without resorting to name-calling.

Massive hybris by one who tries to be seen as a "philosopher". Maybe if he had remained silent.

As to economic forecasting - get a good theoretical grounding with the Greene book, but you probably also want a more applied book.

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.

I've heard this line before.

Normally, I would say, see Professor Granger's work on statistical proof of causality, for which (I think) he won the 2003 Nobel Prize.

But, also, I can recommend the two most standard textbooks on use of statistical methodology for economics (which one uses to prove ones argument mathematically), for students in second and third-year econ courses requiring use of empirical methodology.

• Wooldridge
  
  
• Greene
  
  

Basic logic and an understanding of statistics. Don't make low effort comments.

Take a class or read an undergraduate textbook on econometrics

https://www.amazon.com/Econometric-Analysis-7th-William-Greene/dp/0131395386

https://www.amazon.com/Introductory-Econometrics-Modern-Approach-Economics/dp/1111531048

(Free Hansen book if you understand linear algebra)
https://www.ssc.wisc.edu/~bhansen/econometrics/