What are /r/econometrics' favorite Products & Services?

From 3.5 billion Reddit comments

Most likely you are either:

1. inexperienced with excel, data, programming.

2. completely unaware that data cleaning/munging is 80% of what people do with their time in data science/analysis.

When I did my Masters degree we used Applied Econometric Time Series 3rd edition by Walter Enders. I’m sure there’s a newer version available but I used this text book extensively.

https://www.amazon.com/Applied-Econometric-Time-Walter-Enders/dp/0470505397

Yes, if you check the preview of the book on amazon on this link https://www.amazon.com/-/es/Marlon-Saito-ebook/dp/B08KJ1322G ; among the advanced themes of the book are: IV, SUR, System of Regression Equations, Probit and Logit, Panel Data (FE and RE), SARIMA, GARCH, VARMA, Cointegration and VECM, and Seasonal Decomposition.

Here is the Amazon link

https://www.amazon.com/Introductory-Econometrics-Modern-Approach-Standalone/dp/130527010X/ref=sr_1_fkmr1_1?keywords=woolridge+applied+econometrics&qid=1559939559&s=gateway&sr=8-1-fkmr1

I also wrote my thesis on GARCH models! I used Tsay (2010) a lot. Also I'd recommend looking into the papers of Engle and Bollerslev. Else I'd just go off the references in Tsay, as he covers much of the immediate methodology.

So by "random walk" I'm assuming we're talking about a scenario in which we start at zero and then randomly add 1 or subtract 1 at each step in the process.

Var(Yt) is the variance of the current step

Var(Yt-1) is the variance of the past step

Var(Et) is the variance of whatever we're adding or subtracting (the length of each step on our random walk)

So imagine you're on a random walk. You start at 0 and flip a coin. If it's heads you go up 1, tails you go down 1. After taking your first step, you're either at positive or negative 1. There are only two places you could be.

Now imagine you flip the coin 500 times and take 500 steps. You could have flipped all heads and be at 500, or you could have flipped tails 500 times and be at -500, or you could be anywhere in between.

More steps = more variance.

Yt represents the same number of steps as Yt-1 + Et, so they have the same variance. But Yt-1 represents 1 less step than Yt. Less steps means less variance. Therefore, Var(Yt-1) < Var(Yt).

Full disclosure: I don't know shit about this topic and I'm just making guesses based off the image and what i recall from "A Random Walk Down Wall Street."

>Regression and Other Stories (kind of the successor to the above) https://www.amazon.com/Regression-Stories-Analytical-Methods-Research/dp/110702398X

I will have mastering metrics on my way. Thanks for mentioning those resources to me.
I feel math proof is important but it is not as important as the intuition developed from solving problems with statistical packages. Math is one hurdle, but I can come back later for it. The type of proof I am concerned about is questions like why OLS has a minimum variance... It kinda slows me down

See https://www.amazon.com/Matrix-Algebra-Econometric-Exercises-Vol/dp/0521537460

It has a lot more than the basics but has tons of problems with solutions.

A couple things:

• Microsoft Word has LaTeX compatibility now for math equations. You no longer have to do that god-awful long process for clicking on each math symbol, nor do you need to learn all the writing formatting in LaTeX, which is arguably the biggest learning curve.

• Start with LaTeX tutorials in places like Overleaf. Overleaf is mostly used for online LaTeX writing, but they have a great tutorial section, too.

I REALLY wish I did know of a good source. I kind of learned as a kid, programming in BASIC, then had a college course in PASCAL (waaay back in the 1980's). So, I just kind of fudge my way through programming, mostly in R now, but I have a lot of collaborators who use Matlab, so I can fudge a little in that.

I am sure there are some decent Intro to Programming MOOCs. Perhaps this Coursera one using Matlab?? Or this one with R?. Of these two, the R one is shorter, but the Matlab looks a little better (from my brief look). Playing around with both is always an option, since they are free!

Thanks for the post. What they mean by quintile level data is country level data desegregated by asset quintile (a type of proxy for income quintile, since DHS does not ask about income or consumption).

Btw, here's the commentary I was referring to: http://www.filedropper.com/ravallionmdginfrastrucutre

It's by Ravallion, who used to be the Chief Economist of the WB's Research Department.

So for okuns law, what kind of graphs should i have? I already have scatter plots which make sense, but what else? I have the regression values which you get after computing regression and im not sure which code im supposed to use...

This is the different sets of data I've gathered. The last one is regression in stata, the rest are in excel.

https://drive.google.com/file/d/0B1zX5EYY12vFdmRFZGxvSzRzSzg/edit?usp=sharing

> es, if you check the preview of the book on amazon on this link https://www.amazon.com/-/es/Marlon-Saito-ebook/dp/B08KJ1322G ; among the advanced themes of the book are: IV, SUR, System of Regression Equations, Probit and Logit, Panel Data (FE and RE), SARIMA, GARCH, VARMA, Cointegration and VECM, and Seasonal Decomposition.

Cool! thx and sorry for not reading into the summary. It should be really useful.

If you have a practical goal of finding a relevant job, I think you'll be better of learning descriptive data analysis and basic machine learning.

Learn to use the tidyverse packages in R, https://r4ds.had.co.nz/, and maybe try to read https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370

New book out called “Time Series Econometrics: Learning by Replication” It builds slowly, lots of examples, etc ...

https://smile.amazon.com/gp/product/3319982818?pf_rd_p=c2945051-950f-485c-b4df-15aac5223b10&amp;pf_rd_r=TRW61Q1Y0HDW01MNSVWP

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

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.

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.

There's a couple ways to do it. Heckman selection is a popular two-stage solution. Alternatively you could use a simultaneous regression where you assume the covariance between error terms is non-zero (like a system of SUR) but this can be computationally difficult especially if it isn't solvable analytically. Chapter 18 (pp. 808-810 specifically) of Greene's Econometric Analysis as suggested above covers a nested logit model as well.

>These are not textbooks.

Most definitely this. MHE doesn't have most of the theory behind our most common estimation techniques, because it is more than anything else a practitioners' guide to treatment-effect econometrics. Which is not to say it's not good, only that one needs relative fluency in econometrics to use the text for its intended purposes.

For OP's purposes, I would not use this as a beginner's guide to econometrics. For this, I'd recommend picking up something like baby Wooldridge, then grab MHE when acquainted with some of the stuff.

Lutkepohl's book (https://www.amazon.com/New-Introduction-Multiple-Time-Analysis/dp/3540262393) is really the bible for VAR estimation. A Google search nearly always provides free ways to obtain a digital copy.

Sorry to hijack your thread but do you mind sharing some textbooks in R about OR? I am more interested in simulation and optimization than predictive modeling. Anything from introductory to intermediate is cool. I am looking for something like this [1] but in R.

[1] https://www.amazon.com/Practical-Management-Essential-Textbook-Resources/dp/1111531315

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'm a writer. Just co-authored a book called Everydata about misleading data. My co-author has a PhD in econometrics, and I'm interesting in learning more about advanced topics.

As a political scientist for a primer on the math, take a look at Jeff Gill's book on Math for Political Scientists.

http://www.amazon.com/Essential-Mathematics-Political-Research-Analytical/dp/052168403X

He teaches Bayesian 1 & 2 (when I was there) at ICPSR. The linear algebra you will need is not equivalent to a pure Math course in Linear Algebra, but you should have a good foundation before taking econometrics.

I would also recommend spending some time learning about the concepts in a econometrics course rather than the technical side of it. In political science, we have tons of undereducated Ph.Ds that can interpret any model put in front of them, but we have relatively few who understand what the technical portion is doing and explain it thoroughly. This is what often results in good publications and research.