Reddit mentions: The best econometrics books

Alright I hope you get this. Sounds like you are a lot like I was growing up. I would read a book a week and listen to two. haha. these were books i had to grow into a lot of times. so don't get discouraged. some of these are tough but they'll help you in the long run. promise.

anyways.. here's my list.

Foreign Policy

-Dying to Win- Science and strategy behind suicide terrorism

-Imperial Hubris- good book by a CIA vet on what to expect because of US foreign policy

-Blowback- Same type of book as above, but better.

-The Looming Tower- a good history and account for Sept 11






Economics and Money

-Freakonomics- Ever wonder about he economics of drug dealing, including the surprisingly low earnings and abject working conditions of crack cocaine dealers? This book is fantastic.

-Outliers- Gladwell is a master of minute detail. This book helps you focus on the future.

-Blink-Great book on intuitive judgement

-The Age of Uncertainty- the best book I've ever read on the fight between Capitalism and Communism

Biology and Science

-Why Do Men Have Nipples- a general Q&A book. Good for info you can use at a party or to impress somebody. really random stuff.

-A Short History of Nearly Everything- Humorous take on some heavy heavy science. Easier to read than people think.

-The Ancestors Tale- It was hard picking just one Dawkins book, so I gave you two.

-The Greatest Show on Earth- Dawkins is the world-standard for books on biology and evolution in layman's terms.

Good Novels

-1984-Hopefully no explanation needed

-A Brave New World- a different type of dystopian universe compared to 1984. read both back to back.

-The Brothers Karamazov- My favorite piece of Russian Literature. It made me think more than any other book on this list honestly. I can't recommend it enough.

-Catch-22- There are so many layers to this book. So much symbolism, so much allusion. You must pay attention to get the full affect of this book. Great satire. Masterfully written.

-Alas Babylon- Yet another dystopian novel. This time about what would happen after a world wide nuclear war.

-Slaughterhouse-5 Vonnegut is a badass. And that's really all there is to know. I read this book in one day. It was that good. Satire on WW2.

Philosophy

-Sophies World- Good intro to a lot of basic principles of the major philosophers

-Beyond Good and Evil- Nietzche can get REALLY depressing because he is a nihilist but this book is extremely quotable and will give fresh perspective on a lot of things.

-Atlas Shrugged- Ayn Rand's masthead. Its a novel, but its also a commentary on her precious objectivism.


So there you have it. My short list of books to read. I can get deeper into certain subjects if you want me to. Just PM me.

Yes but the question is why investor 1's should be significantly different than investor 2's in regards to their covariates. If you have issues with the methodology of the paper, you have an issue with every single pharmaceutical study ever run.

In addition, the issue isn't really whether investor 1's and investor 2's balance (though they should and do), it is whether there is a significant difference within investor 2's. Category B and C are being compared to the category A baseline, which captures the possible prior-networking effects you mention.

>The follow up survey relies on honesty

To a degree yes. When the vast majority of your responses are weighted 90%+ in one direction or the other? Probably no issues there.

>something that people with some experinece and some sense already know

Yes, but there's the question of quantifying it. It doesn't strike you as interesting that knowing a friend owns an asset vs. knowing they want to buy it vs. knowing they bought it and currently own it have 3 very different levels of influence on your purchase decision?

EDIT: Since I think we're rapidly approaching the point of intransigence, I will just say this: I think you need to give the field as a whole a bit more credit. I know that's a tough ask considering your opinion that the field as a whole is bunk. I'd ask you to consider that most of the economists that I've met are seriously intelligent, with a great grasp of graduate level mathematics, statistics and of course, economics. Many share your views about basic fallacies in the field, others don't. The long and short of it is: they spend months of their lives thinking about these topics. Don't get me wrong, there are bad papers and there are bad economists. That said, most papers published in top journals are actually pretty good, and are actually pretty interesting. They're not out to get you, they're not out to get anyone. They're just searching for causal relationships in an environment which makes causal relationships very difficult to find.

If you're interested in reading more about how economics works, I'd suggest reading Mostly Harmless Econometrics. I find it a lot better than the more popular Freakonomics in explaining the basic (modern) tenents of applied economic research. Anyway, have a good day, I hope this discussion wasn't a complete waste of your time!

This is definitely the right sub. A few notes:

1. You can approach applied economics papers with just a semester of economic statistics / econometrics and a semester of intermediate theory. Applied papers can be either micro or macro. For example, at this point you should be able to comfortably read Mankiw, Romer, and Weil (1992).
   
   Most applied economics paper boil down to I ran a regression of Y on X. The key question you have to ask yourself is, "Do I buy their identification?" To assess such claims, you don't need much more than a semester or two of econometrics and critical thinking. Sure, some of the more obscure estimators might be beyond your ability, but the broad swath of applied papers boil down to some kind of instrumental variables regression.
   
   A somewhat more advanced paper that should still be readable to you is Gali and Gertler (1999).
   
   The game here is: write down your model to be estimated; argue that you have a good identification strategy; show us the tables of coefficients; argue that your results are robust; tell me why I should care. The trick is clean identification and economically & statistically significant results.
   
   Resources: your companion here should be Mostly Harmless Econometrics.
   
   
2. Then there are theory papers, which can be micro or macro. These will generally set up an economic model and prove some results analytically. The limiting factor in reading them will be your mathematical maturity: here is where the real analysis and topology come into play. A typical theory paper (that you cannot probably read comfortably) might look like Mas-Colell (1975).
   
   The game here is: write down a model; derive some mathematical results (typically about certain partial derivatives, cross-elasticities, or existence of equilibria); tell me why I should care. The trick is to write down a model that makes sense and bring results that are applicable, either to other theoretical problems or that can be applied to practical problems.
   
   Resources: MWG is probably good preparation for these papers, at least in form.
   
   
3. Third, there are computational papers. These tend to be macro, and within macro tend to be oriented around business cycle analysis. Here there is a lot of assumed background knowledge: macroeconomists expect to see models written down in particular ways and have certain preconceived expectations of what your "results" should look like. Again, I don't think these are approachable right out of the undergrad curriculum. I certainly could not follow the arguments of Ireland (2004) when I was an undergrad. I could barely work through the first few pages of Clarida, Gali and Gertler (1999).
   
   The game here is: write down a model; solve it approximately near the steady-state; simulate the model; show me some dynamics around the steady-state; show me how the model reacts to shocks; tell me why I should care. The trick is: write down a sensible model that captures the phenomena of interest, and show how the economy reacts to the shocks that you hit the economy with. Sometimes you want to show how policy can counteract those shocks.
   
   Resources: while reading a few chapters of Sargent & Ljungqvist is nice, there is still a lot of assumed background when reading macro that makes these papers a bit forbidding if you haven't had graduate training in the subject. Yes, I find it deplorable, but that's how the profession has evolved.
   
   A fantastic place to start in macro is Models of Business Cycles. You can probably read it if you know a little calculus and have taken a course in intermediate macro.
   
   By the way, all of the papers I've linked to are "classics" and are worth perusing, even if you don't fully grasp what's going on in them. I'm biased, so you've gotten a sampling of macro papers. Let me know if you want details/context on any of the papers I linked to.
   
   My deepest apologies if, in these summaries, I have offended the sensibilities of my applied and theoretical brethren. I'm stepping a bit out of the bounds of my field (money & macro). :)
   
   (Anyone know of good "classics" in applied micro that are readable? Card-Krueger (1994) is probably readable, as is Angrist (1990). But I'm not familiar with the classics in labor and IO.)

> Or that communism creates starvation (joke)

I don't think this is a joke. While causal designs would be difficult to apply, the spatio-temporal correlation is hard to ignore.


>Regarding causality- as you know that’s nearly impossible to prove in the social sciences.

Actually, these days the application of designs and approaches that provide strong support for causal claims have become quite prevalent. Some standard references-



1

2

3

4

good framework reference or a slightly heavier read

and the old classic


In fact, the Nobel prize in economics this year went to some people who have built their careers doing exactly that

It's actually become quite hard to publish in ranking journals in some fields without a convincing (causal) identification strategy.


But we digress.


>We will never be able to do an apples to apples study between heterosexual and homosexual child rearing for some of the reasons you mentioned above. (Diversity of relationship styles, not both biological parents within gay/lesbian couples)

In this case it isn't far fetched at all. The data collection for the survey data used in the study you linked could just as easily have disagregated the parents involved in same sex romantic relationships instead of pooling them. If I understood correctly, the researcher had obtained the data as a secondary source, so they didn't have control over this.

Outcomes for children in the foster care system are well studied, so one could in principal easily replicate the study comparing outcomes between children in the foster care system and those adopted into homes shared by stable same sex couples (you couldn't likely restrict it to married same sex couples, though, because laws permitting same sex civil marriage are too recent to observe outcomes).

>My bottom line-that I don’t see many disagree with if they are being intellectually honest, is a stable monogamous heterosexual family structure is the best model for immediate families. Or would you disagree?

But that's not the question at hand, is it? What we are interested in here is comparing kids bouncing around the state care system to those adopted into homes with two same-sex parents in a stable relationship.

That is exactly my point. The comparison you propose is uninformative relative to the question of permitting same sex couples to "foster to adopt". Because the counterfactual for those children is not likely to be a "stable monogamous heterosexual family". It is bouncing around the foster care system.

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

I really like applied stats but think a good understanding of stats theory is important for any researcher. A good "litmus" test, I think, would be reading Mostly Harmless Econometrics (you can probably find a place to check the book out for free). It isn't about health statistics at all but it is a very good "applied" theory book. If you get into the first bit and go "this isn't for me," that is fine and epi probably won't be a problem. If you go "this is interesting," then you might be worth looking at doing the required pre-reqs for the MS biostats program.

Relatedly, check out this free biostatistics bootcamp on Coursera. Check out the first few weeks of lectures and if you decide that the stat theory is more than you care for, epi is a good place.

Epi is a good field, don't get me wrong, but if you are interested in statistics, then it might not be a great fit. MHE and a few of those lectures might be very helpful in deciding if you are at all unsure of how you lean.

I've been using Excel since the day it came out. I've always used it casually as a classic spreadsheet tool. I didn't use it much for a few years but recently came back to it for job-related reasons. It has evolved into quite something. The current version (2019/O365) is a numerical wonder. Its data analysis tools/features are fantastic. As others have commented, the query, data modeling, data transformation and visualisation features are eye-popping. It's data science for the masses. I assume this is no accident. Microsoft realises the data revolution that is coming and not everyone wants to (or can) learn R or Python.

It goes without saying that you need to learn the basics before progressing to the more advanced stuff. But the more advanced stuff isn't very hard to learn and, as Nyct0phile pointed out, you can appear to be a data magician using some of the relatively easy to use tools. Youtube is good for the basics and an introduction to analytics.

One other thing. It really is helpful if you actually understand what Excel is doing (and you're not simply invoking commands). This book is a really great introduction to statistics, data analytics and machine learning. https://www.amazon.co.uk/gp/product/0241398630/ref=ppx_yo_dt_b_asin_title_o01_s00?ie=UTF8&psc=1

If you learn to use Excel's data analysis tools and read this book you too will be able to say: "It's sometimes better to apply a simple linear regression model for predictive analytics than to over-fit a complex Machine Learning algorithm".

For me:

Freakonomics <- Showed me a different level of problem solving, via thinking about the motivation behind things.

The Icarus Deception & Linchpin <- Helped me realize I hate doing the work of a cog in a machine and that I enjoy my work if I get to express myself via creativity.

Currently reading:

How To Win Friends And Influence People <- It may be old, but it's still a great resource for human relations, even today. I don't know about most people around here, but I don't like only staring at my monitor 24-7. You can kind of think of it as the start to social engineering. You learn the correct inputs so that you may get the outputs you desire.


Bonus: Not sure if this counts, since it could be considered "technical":

The Phoenix Project <- If you ever interact with non-IT folks, you should read this book. If you are a non-IT person and interact with them, you should read this book. It shows you there are more ways then simply supporting a business that IT should be utilized. I read this after I'd been "doing devops" for a couple years already, and it really solidified a number of points. It's also a great talking point if you ever interview with someone who HAS read it. The only feedback I've received has been positive when I mention this book (to someone who has read it).

edit: words

>Empirical methodology is about running regressions in order to establish causal or at least predictive relationships within the dataset.

Perhaps this is what empirical rigor means in practice, but the view that this is what empirical rigor should mean is ultimately untenable.

Josh Angrist might re-assert /u/OMG_TRIGGER_WARNING's question like this: it doesn't matter if X predicts Y almost with certainty, if tomorrow some policy change will cause the relationship to fall apart entirely. Causality is more important than correlation, because causality is the only true test of an actual economic model. Moreover, causality isn't something that you get from matching your data with some DSGE equations, finding p<.00001 with Newey-West standard errors, then passing a Hausman test. Unless you have a plausible quasi-experiment with a tight chain of causality, you have nothing except a statistical relationship. You can't even identify a diagram like X -> Y -> Z -> X.

There is a sort of nihilism in that worldview. If someone makes a valid criticism that breaks your chain of causality, there's no honest response except to ask for a suspension of disbelief. When all's said and done, you're not allowed to believe anything except local average treatment effects (LATEs) from randomized experiments. I don't see this as a useful standard to hold every single piece of empirical research to, because it's unreasonably demanding.

That's why I would agree with your general response, since I think macro is useful. This is because of one of the other reasons you've mentioned - there seems to be a sort of stationarity in the data where predictive relationships remain stable for a while. That's where I permit some suspension of disbelief. I think that makes me relatively lax, but I don't see a better alternative to answering the kinds of questions macroeconomists and policymakers need to ask. I might rephrase your answer to OP's question like this: macro is useful if we're OK accepting a lower standard for what constitutes useful information. There is use for statistical relationships which we hope will continue into the future but which aren't, currently, causally founded.

I'm a Political Methodologist; I'm happy to give you some help. It would be useful to know what your mathematical background is, and what sort of things you're interested in doing. You have to understand, to me, this question is a little bit like "I'm interested in American Politics; suggest an introductory text, please." There's a huge variety of stuff going on here, it's hard to know where to start.

Do you want to be able to read statistics wrt PoliSci? Or are you interested in figuring out how everything works, so that you can create / replicate?

If you want something very undergraduate centric, my undergrad research methods class used the Kellstedt and Whitten book. It was fine, but obviously very rudimentary. It will get you to understand some of the big picture type stuff, as well as some of the simple statistical nuts and bolts you'd want to understand. This class also used the everpresent King, Keohane and Verba text, which is oriented around qualitative work, but Gary King is the foremost quantitative methodologist in the discipline, so it's still pretty good (and "qualitative" certainly doesn't mean "non-rigorous" -- it's cited a lot because it really delves into deeply into research design). That said, I don't remember a whole lot about this class anymore, and I haven't looked in these books for ages. My feeling is that both of these books will probably be close to what you're looking for -- they're oriented around intuition and identifying the main issues in inference in the social sciences, without getting too bogged down in all of the math.

That said, if you have more math background, I'd suggest Mostly Harmless Econometrics which is often used as a first year graduate level quant methods book. It's absolutely fantastic, but it isn't easy if you don't have the math background. It may also assume some preexisting rudimentary probability or statistical knowledge. I'd also suggest the Morgan and Winship. These two books are structured more around causal inference, which is a subtle reframing of the whole "statistics in the social sciences".

For more nuts and bolts econometrics, Baby Wooldridge is one of the standards. I think it's pretty often used in undergrad econ classes.

In general, though, statistics is statistics, so if you want to learn it, find an appropriate level of statistics/econometrics book.

Take a look at those books in your library/online/etc and see if any of them are what you're looking for.

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

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.

I did well in both Econometrics courses (399,497), and would say definitely check this text book out. You should be able to find a PDF somewhere. In my section with Fossati there was some really basic coding (Shazam), and the stats/maths were very introductory. Understanding basic calculus (multivariate inc.) as well as introductory stats (distributions, expected values, hypothesis testing) is most the course imo.

They're also both a lot of fun. :) If you're looking for interesting Econ courses, those specific 400 level ones are pretty interesting, since you typically get directed to read papers with substantial findings and get a good taste of that particular field. Labor Economics and Urban Economics are both great.

Sure no problem. I went thought the first few chapters in this book by Tsay:

 

https://www.amazon.com/Analysis-Financial-Time-Ruey-Tsay/dp/0470414359

 

Then I downloaded free finance data online where I could find it and also from this site called Quandl:

 

https://www.quandl.com/

 

I then loaded the data into a database and wrote a small application (using python, R, and java) to pull the time series data from the database and implement the calculations that I understood in that book.

 

I talked about this project in my cover letter for any job I applied to. It was definitely helpful in job interviews to be able to discuss what I had done. I also learned a lot because when I started I didn't have very much experience with programming so it was definitely a good learning experience.

If you want help getting started, let me know what questions you have and i'll send you some notes.

 

Edit: The book below (by Makridakis/Hyndman) is much simpler book and is a better introduction to time series than the book in the first link above. I read this first got a basic understanding of time series and forecasting. After getting through a few chapters in Hyndman, the Tsay book is much easier to understand. But in either case building small application that implement these methods in either of these books is a great exercise.

 

https://www.amazon.com/Forecasting-principles-practice-Rob-Hyndman/dp/0987507109/

 

You can also get the contents of that book for free at this link:

 

https://www.otexts.org/fpp

Where are you in economics right now? Undergraduate? Graduate?

Advanced mathematics appears everywhere in economics, though your mileage may vary depending on your definition of "advanced". As a mathematician, I suspect that quantitative finance contains the most advanced mathematics, since in modern mathematics research the majority of interaction with economics is through quantitative finance. But unless you plan on doing the most advanced math, there's more than enough advanced math in non-finance economics to keep you interested.

Generally speaking, professional economists build up some skill in real and functional analysis, as well as a variety of other skills like optimization, stochastics, and PDEs, depending on their specific research interests. These are all graduate-level math topics, so I'd consider them reasonably advanced. Take a look into econ PhD prelim coursework. When I took the sequence, we used the texts Microeconomic Theory by Mas-Colell-Whinston-Green, Recursive Macroeconomic Theory by Ljungqvist and Sargent, and Econometrics by Hayashi. I think they're good springboards for you to evaluate the math in higher economics.

In quantitative finance, I'd maybe start by checking out Portfolio Risk Analysis by Connor, Goldberg, and Korajczyk, then if you're still interested, I'd pick up measure-theoretic probability. I recommend Probability with Martingales by Williams. Once you're comfortable with measure theory, look through Stochastic Calculus and Financial Applications by Steele. You'll very quickly enter the area of research mathematics while studying quantitative finance, e.g. jump-diffusion models and Levy processes appear in the pricing of exotic derivatives, and they're heavily studied by even pure mathematicians.

I agree with most that doing some practice with it is probably the easiest, especially if you've already got a lot of knowledge of programming (ie it'll be easy to pick up). Grab something like Cameron and Trivedi as a nice reference, though I don't know if it's the most up to date. I don't know that it's the best text for learning, but will have a wide range of topics that you can easily reference as you approach problems.

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Have you taken a bunch of stats and/or econometrics? If so then I'd just do some replications of papers Joshua Angrist's site has quite a few that are fairly intuitive and approachable. Also William Evan's has a nice teaching page for learning to use Stata. I link the undergraduate one, but the graduate one is also useful as well and will be pretty approachable if you've had a solid background in stats and econometrics.

Hey, so my favorite, whenever I want to go back and refresh myself on metrics is Fumio Hayashi. He writes it a bit like a novel and the flow is pretty good for the proofs. It can be a bit dense sometimes but I generally like his writing style. Also, he has you derive Nerlove's 1963 paper which is a good exercise in itself. Best of luck and go forth!

I would highly recommend the following book: Mostly harmless econometrics

http://www.amazon.com/Mostly-Harmless-Econometrics-Empiricists-Companion/dp/0691120358/ref=wl_it_dp_o?ie=UTF8&coliid=IVHBGLQH4VJ3I&colid=21QCK3AR703JR

It is very problem driven book and will help build up your knowledge base to know what models are appropriate for a given situation or dataset.

You will then need to start practicing in a statistical program to gain the practical skills of applying those models to real data. Excel works, but I don't know a good book to recommend to guide you through using excel on real problems.

I recommend Stata to new data analysts and have them pick up "microeconomics using stata"; once they've worked through these two books they get excited and start grabbing data all over and begin running models, its exciting to watch new data modellers apply tools they're learning. R is free and open source but is more difficult to learn, if you're willing to ask there are tons of people willing to help you through R.

I think @omeow gives a good answer. Not less calculus as Calculus is the bedrock of so many different areas of maths and science. If you want a good book on this Steve Strogatz's lastes "infinite powers" is awesome: https://www.amazon.co.uk/Infinite-Powers-Calculus-Reveals-Universe/dp/1328879984

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That said, statistics is becoming increasingly important. We need to train everyone, not just Maths grads in more stats. I think if you want to guaruntee a job coming out of an undergraduate degree then Stats is a pretty good bet. Also if you're looking for a primer on stats then David Spiegelhalter's book "The art of statistics" iss a great one: https://www.amazon.co.uk/Art-Statistics-Learning-Pelican-Books/dp/0241398630/ref=sr_1_1?keywords=the+art+of+statistics&qid=1569697929&s=books&sr=1-1

Uhm... in econometrics, I'd go with Woolridge (he has another book mostly on panel data but that is a graduate level textbook). I used Gujarati in my undergrad classes and didn't like it very much.

If you are more into macro-oriented time series econometrics, Enders is a great book, very practical and with a lot of examples. If you end up doing applied microeconometrics in Stata, Cameron and Trivedi have a great book for that.

I just stumbled upon this website that seems to have good info on econometrics texts which, as you can see, can be pretty pricey.

> It would be like a game of sorts. Learn all the handshakes, play golf with important people, all the while my end goal would be to learn their game and use it to favor the less fortunate.

You sound very young! Unfortunately it is not just a matter of secret handshakes..

Can I recommend another book which will probably give you lots of inspirations? The author is an admirer of Thomas Schelling's work, so it ties nicely with the second book I suggested before:

The Undercover Economist: Exposing Why the Rich Are Rich, the Poor Are Poor--and Why You Can Never Buy a Decent Used Car!

Putting in a vote for Econometrics.

I was a math undergrad and took Econometrics as an elective and I loved it. If you're into applied statistics, then I definitely recommend it. I've heard good things about this book if you're looking for an undergrad level book to read through in your free time (there should be a free pdf online if you do it a Google). You should be able to do all of the required computations in Excel, so no other software is needed.

Also, I'm not sure what your stats background is, so I'll just leave you a link to a fantastic source provided by PSU. It has online notes for many stats courses starting from the basics, if you're interested.

• The Undercover Economist - Has a pretty similar feel to Freakonomics. Basically economic concepts applied to every day things.
  
  
• Spin-Free Economics - More like a textbook in format. There are a bunch of short chapters on various topics which he analyzes using economic theory. Some of the Amazon reviews say it's a pretty standard free-market capitalist style thinking and not "spin-free" but it's a good primer on basic concepts imo.
  
  
• Predictably Irrational - Behavioral economics, which is basically taking psychology and applying it to econ. Basically economic models rely on humans being rational and the Ariely's research is all about how humans aren't a lot of the time. This area of econ interests me most, so if you like this book, the Upside of Irrationality, Sway, and Nudge are all pretty interesting too.
  
  
• Malcolm Gladwell's books (Outliers, Tipping Point, Blink) aren't about econ but they're in the realm of "dumbed down" interesting things.

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.

If you want to know just the practical use:

The closest the R2 goes to 1, the better the model. For example, an R2 of, say, 0.82 means: "82% of the variation of the dependent variable is explained by the variation of the dependent variables". Conversely, the less the R2, the less the predictive power of the dependent variables.

In order to evaluate the significance of each variable, you have to check the P values. The practical meaning is: the lower the p-value, the more useful is the variable. For example, a p-value of, say, 0.01 means the variable is very significant, and therefore should be implemented in the model. The level of significance of course depends on your work.

edit: If you want to know the theory, take a look at this. It is one of the best books. https://www.amazon.com/Basic-Econometrics-Damodar-Gujarati/dp/0073375772

Definitely read both books, although you may appreciate Cochrane's more if you like asset pricing. Keep an open mind -- in my experience many students enter PhD programs planning on doing asset pricing, then move to corporate finance. That is what I did.

I'm not aware of any recent machine learning asset pricing papers in the top journals, so I can't point you in that direction (although, to be honest, I don't do asset pricing, so I'm not the best resource). You may like the Santa Fe market -- which is a simulated financial market. It's a little old, but you may find value in it.

If you really like econometrics, I'd suggest taking a look at Hamilton's Time Series Analysis or Campbell, Lo, and MacKinlay's The Econometrics of Financial Markets. They're both very mathy, as they are more about the econometric techniques used in empirical research than they are about the research itself. You'll likely run into one or the other in your grad program.

> Oh, and the different between men and women for the De-Individuated is 0.04375, or 1.6 - 1.55625 = 0.04375. Sooo... < 0.05 is significant, but < 0.04 is not?

Not sure if you're serious here, but the question is not whether a difference of 0.04 is meaningful, but whether the difference in sample means was statistically significant. This means, is it different enough that you would not find such a difference in means just by virtue of noisy data. The 0.05 number that people talk about is not just a difference in means of 0.05 (whatever the unit of measure is), but rather, a p-value of less than 0.05, indicating that the results of the study would only be expected to be wrong 5% of the time. The people you're responding to are using significant in the technical sense. Their point is that, from the sample size and the difference in means, the original authors correctly noted that we cannot claim statistical significance. Given the generally accepted cutoff of 0.05, this means that you're likely to get results like these by random noise in the data, and be wrong more than 1/20th of the time.

In order to do the appropriate calculations, you would need to know the sample sizes (among other things). You can't just find the difference in means and make inferences based on whether the number seems important. If you're interested in sharpening your statistical skills, I highly recommend A Modern Approach to Regression with R, and for more on this kind of problem, Nonparametric Statistical Methods may prove appropriate, given that this can be reduced to comparing pre- and post-treatment means (where individuation is the 'treatment').

The Card paper is old but the Peri and Yasenov paper does have clean data and code available here.

Also has the raw CPS mariel data and codes on how to construct the synthetic controls. My guess is that with the raw CPS data you should be able to get at Card's results too.

Knock yourself out jan. Pick up an econometrics textbook while you're at it too. You might actually learn something in the panel data section (if indeed you are capable of such a thing at all).

Please report the findings of your "investigation" for our viewing pleasure.

Thanks!

The matching with women proposing would be: m1/w3, m2/w1, m3/w4, m4/w2

This is because in the first round, women will choose different men than vice-versa. You're totally correct that there will also be only one round.

Honestly, this example is really shitty/construed. There are no further rounds/rejections because

• There's no outside option. Every man is acceptable to every woman and vice-versa.
  
• In the first round, every proposer chooses a different man/woman.
  
  An interesting fact of stable two-sided matchings is the following: When men (women) propose, from all possible stable matchings, the one that is best for men (women) materializes. So the interests of men and women are exactly opposing (i.e. what's good for one men is also good for all other men, and bad for all women).
  
  Armed with this fact, we can conclude something else about this problem: Since m4/w2 are matched in both matchings (i.e. best for men and best for women), they are matched in every stable matching. You could also deduce this from the fact that they are each other's first choice, but that statement would hold even if they weren't.
  
  If you want to study this in detail, this is the book to read: https://www.amazon.com/Two-Sided-Matching-Econometric-Society-Monographs/dp/0521437881

Actually scratch the macro/micro difference. The best layperson Econ books are:

-Freakonomics (Revised Edition) https://www.amazon.ca/dp/0061234001/ref=cm_sw_r_cp_api_p6WfAbTQ50ZGZ (it has a follow up, SuperFreakonomics)

And

-The Armchair Economist: Economics and Everyday Life https://www.amazon.ca/dp/1451651732/ref=cm_sw_r_cp_api_Q5WfAbR3TEZTP (or anything by Steven Landsburg really)

If you like podcasts, check out Planet Money, Freakonomics Radio, and EconTalk.

Freakonomics and SuperFreakonomics are both available as audiobooks and done really well if that’s a thing you like.

There is some material in MWG that is considered mechanism design. Chapters 13, 14, 21, 23, maybe some elements in others.

For Contract Theory, this is a great introduction and this is a slightly more advanced followup.

For Matching, this is a good introduction to the basics.

I don't have a good idea for a text for Voting. It's mentioned in MWG to some extent.

If you want seminal papers, these books provide citations of those, as well as many, many others.

First, I need to make a disclaimer that I am not an expect. I am unsure if you are using a text to reference your question, but I would recommend [Basic Econometrics] (http://www.amazon.com/Basic-Econometrics-Damodar-Gujarati/dp/0073375772). That text will be much more helpful than I will be.

I'll start will the question you asked in your other post.

> I have read that Multicollinearity is different from Endogeneity, because in the former case, there is not supposedly a causal relationship between the multicollinear variables. But in my model I know for sure that there is a causal relationship between my variable of interest and a control variable, but both are also related to my dependent that I really can't leave the control variable out. Does this 'difference' between multicollinearity and endogeneity make sense?

Your first statement that there is not supposedly a causal relationship between collinear variable is correct. With endogenity, we are concerned about causal relationships, because it is the existence of the causal relationship that is causing our variable to be correlated with the error term. However, with multicollinearity we are not concerned about the causality between the two variables. It is the fact that these two variables are correlated together, and not that one causes the other, that is going to cause the issues of multicollinearity that I described before. We could have two completely unrelated variables in our analysis, but it they by chance are correlated together we are going to get the problems of multicollinearity. Does that make sense?

So, if I understand this correctly, you are concerned that your variables representing fiscal consolidations and the economy may be correlated together and cause collinear issues? What does your correlation matrix say? Also, if you are not trying to determine causal affects, then why are you concerned about using an IV approach? I thought the purpose of an IV was to try to determine causal relationships. Rather than looking for an IV variable, I think what you might be looking for is a [proxy variable] (https://en.wikipedia.org/wiki/Proxy_(statistics). A variable that can stand in your control variables place and represent it, but is not correlated with your key variable. IV and proxy are closely related, but there are less requirements for finding a suitable proxy variable.

Trading and Exchanges by Larry Harris is probably the best. It tends to lean toward Equities, but many of the concepts (market participants; economics; etc) are universal to all assets. The market structure itself tends to deviate for other assets, but this should give you enough of a baseline to know what else to search for if you want to go deeper down the rabbit hole.

Second may be Empirical Market Microstructure by Joel Hasbrouck.

If you want something on more exotic asset types (STIRs or such) let me know.

The linear part of linear regression refers to the coefficients, not the variables. For example Y = aX + bX^2 is a linear model because it is a linear combination of X and X^2 involving a and b. Y = abX is not a linear model. You can fit a lot of models that are not linear using linear regression. The name is kind of misleading, I think.

Without knowing it, you're asking a gigantic question. You want to know how to fit regression models. That can take up two graduate level courses, if you're learning all the details. A good introduction is by Simon Sheather (Amazon Link). If you're a student, you can read that book for free from SpringerLink. There should be courses on regression modeling from Coursera and MIT Open Courseware, if you'd prefer that. Linear regression and generalized linear regression are fundamental tools that you just need to know if you're going to do any kind of statistics.

I'm sorry I can't answer your question directly. You really need to understand a little more about regression to build good models. For any given datasets, there's a handful of different ways, with varying degrees of validity, to model relationships among variables.

If you're interested in all of that, you should start by reading up on mechanism design, which you can find in any good microeconomics or game theory textbook. I like Fudenberg and Tirole.

• Fudeberg and Tirole - Game Theory
  
  I mainly see Krishna's book used for auctions. Menezes and Monteiro is also good. There are also books by Paul Klemperer and Paul Milgrom which are less textbook like.
  
  
• Auction Theory - Vijay Krishna
  
  
• Menezes and Monteiro - An Introduction to Auction Theory
  
  For matching, the guide to the "classical" theory is Roth & Sotomayor, but it's getting pretty old. There's a new "Handbook of Market Design" but I haven't read it and can't vouch for it.
  
  
• Roth and Sotomayor - Two-Sided Matching
  
  For bargaining, you can't go wrong with Osbourne and Rubinstein.
  
  
• Osbourne and Rubinstein - Bargaining and Markets
  
  Sadly, I don't know of a good voting textbook. Much of the recent work in voting has moved out of economics into computer science, political science, etc., so I would look there for current work on voting.

What part of finance are you interested in?

Asset pricing?

• Asset Pricing - Cochrane
  
• Dynamic Asset Pricing Theory - Duffie
  
  Corporate Finance?
  
  
• Theory of Corporate Finance - Tirole
  
• Theoretical Foundations of Corporate Finance - Matos
  
  Behavioral Finance?
  
  
• Inefficient Markets - Schleifer
  
  Financial Econometrics?
  
  
• The Econometrics of Financial Markets - Campbell, Lo, Mackinlay
  
• Analysis of Financial Time Series - Tsay
  
  Market Microstructure?
  
  
• Market Microstructure Theory - O'Hara
  
• Empirical Market Microstructure - Hasbrouck

I found this book and going by it, they say it is a classic and a good thing is that it does not start from the Gaussian distribution of returns assumption. I hope it helps you.

https://www.amazon.de/Analysis-Financial-Wiley-Probability-Statistics/dp/0470414359

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.

There are lots of good recommendations on the technical side, (also, this for game theory and this is excellent intro into econometrics), but most economic insights are really very simple, and if you want to practice these ideas, I recommend blogs. These two are my favourite general blogs:

Marginal Revolution

Modeled Behaviour

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
  
  

I'll second the recommendation of Mayo's book. It's really great. In a similar vein, you might like The Cult of Statistical Significance by Ziliak and McCloskey.

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.

If you are interested in financial applications, this book is great: http://www.amazon.com/Analysis-Financial-Wiley-Probability-Statistics/dp/0470414359/ref=sr_1_1?s=books&ie=UTF8&qid=1345211947&sr=1-1&keywords=financial+time+series

I haven't read Pearl, but I have read Mostly Harmless Econometrics a quick amazon comparison looks like they're covering similar material for different audience.

In my graduate econometrics course we used Mostly Harmless Econometrics. It's focused on the question of causal inference, and specifically how to do empirically rigorous studies when variables aren't exogenous. It covers a bunch of best practices in design experiments. It's not focused on networks, which is a rapidly emerging field of study in the social sciences. However, it does a very good job of explaining possible sources of error in statistical inference and research design.

Mostly Harmless Econometrics

and

The Signal and the Noise

are my recommendations for an introduction into more advanced topics in econometrics. If you want more of a textbook Th3Plot_inYou's suggestion is good (I still have mine from my class).

Edit: Signal and the Noise is more theoretical about forecasting in general.

I really enjoy using this book for reference material. It depends on what you are trying to learn, though. You won't find code examples in it.

What you will find, however, is a ton of different methods and examples of how to apply them (contextually). You'd probably need to have a least a basic idea of how to begin the analysis to make it useful. (For instance, I'm modeling a binary event, I need a logit or probit, start w/ that chapter; or I'm modeling a rank, Tobit).

for an undergrad introduction, see wooldridge. It's reads pretty nice, you can find a digital copy in the web and you can buy a much cheaper international version by mail too!

I really found How to Fight the President entertaining, it is more history of course. For meditation, check out Dan Harris' 10% Happier... psychology, anything by Oliver Sacks is good... economics, Freakonomics...

Check with your library, mine has a great selection of digital audiobooks, just download using the Overdrive app...

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.

I don't understand how my example of spurious correlation among randomly generated numbers doesn't already meet that burden. That's a data generating process that is not causal by design but produces your preferred observed signal.

Your additions of "repeated", "different times" and "different places" only reduce likelihood of finding a set with your preferred signal (or similarly require checking more pairs). There's literally a cottage industry around finding these funny noncausal relationships http://tylervigen.com/page?page=1

If you're imagining something more elaborate about what it means to move "reliably" together, Mostly Harmless Econometrics walks through how every single thing you might be thinking of is really just trying to get back to Rubin style randomized treatment assignment
https://www.amazon.com/Mostly-Harmless-Econometrics-Empiricists-Companion/dp/0691120358

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.)

Sounds like market microstructure. I don't know anyone working on the topics you mention specifically, but Maureen O'Hara at Cornell is a guru on MM. There is also this book on Empirical Market Microstructure and a survey paper by Biais, Glosten, and Spatt that might provide references for some of the areas you're interested in.

The "Also Viewed" section is hilarious:

The 2009-2014 Outlook for Wood Toilet Seats in Greater China

UFO Detector

David Hasselhoff Albulm

A Million Random Digits THE SEQUEL

/u/sumant28 gave a solid piece of advice above. Of these two, normally I'd recommend Wooldridge over MHE anyday, but now considering that a lot of health economics is treatment-effect stuff, you might want to pick up MHE.

e. that's Mostly Harmless Econometrics just in case you didn't know this.

A good bet would be "Mostly harmless Econometrics"

http://www.amazon.com/Mostly-Harmless-Econometrics-Empiricists-Companion/dp/0691120358

Not overly theoretic and very focussed on practical applications.

I thought that The Undercover Economist was far superior to Freakonomics

For time series analysis, which you would use to do forecasting, you can't beat Hamilton's Time Series Analysis if you really want to do it right. Supplementing it with a less technical text like Nate Silver's book, as /u/granito suggested is a good idea. For econometrics outside of time series I second Angrist and Pischke's Mostly Harmless Econometrics. Unfortunately, I don't know of a great time series book with a similar style to MHE.

my whoel problem is that 95% certainty. I'm a statistician by trade, and even most statisticians get the idea of statistical certainty wrong.

There is NOOOO way you can claim "we are X% certain that humans are causing climate change". the data is way to variable and we don't know the cause and affect relationship.

many times, when you hear a scientist say I'm X% certain you should run. and run quickly.

Here's a good book on the subject

http://www.amazon.com/Cult-Statistical-Significance-Economics-Cognition/dp/0472050079/ref=sr_1_1?ie=UTF8&qid=1422292380&sr=8-1&keywords=cult+of+statistical+significance

This is actually from The Undercover Economist. Good read.

In addition to linear regression, do you need a reference for future use/other topics? Casella/Berger is a good one.

For linear regression, I really enjoyed A Modern Approach to Regression with R.

The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives tend to show that statisticians actually don't really understand their field either.

Edit: Why the downmod? If you've read the book at least explain and if you haven't... well on what ground do you downvote?

Free pdf of this book. We used it in our grad class and I thought it did a good job in most instances.

A ton of people seem to like the one by Hamilton. I have never read it but it got high reviews. It seems to be written for Econometricians more so than statisticians.

Why are you on this sub? You don't have any idea what you're talking about. Tell you what smart guy, go to your library and read Hayashi's seminal treatment of econometrics (Econometrics) and report back to us how it's political.

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

My 2009-2014 Outlook for Wood Toilet Seats in Greater China says this is true.

You seem to have a similar taste in non-fiction to me. Here are a few more that I've either read or have been recommended to me.

The Undercover Economist

I.O.U Why Everyone Owes Everyone and No Once Can Pay


I heard this guy give a fascinating interview but his book is getting mixed reviews, I'm still tempted though...

Predictioneers-Game

You could look at The Cult of Statistical Significance which discusses what the authors see as the widespread misuse of significance tests and lists large numbers of problematic articles, mostly in econometrics but also in other fields such as psychology. If you do read the book, prepare to be ranted at; the authors' style is also pretty repetitive, so prepare to be selective in yr reading.

Don't worry, the up votes have spoken for who's post is ludicrous, and it is yours.

Here are some reading resources for you:

http://www.amazon.com/Stochastic-Methods-Economics-Advanced-Textbooks/dp/0444862013/ref=sr_1_6?s=books&ie=UTF8&qid=1309949494&sr=1-6

http://www.amazon.com/Econometrics-Financial-Markets-John-Campbell/dp/0691043019/ref=pd_sim_b_2

and, to ensure you can understand one of these:

http://www.amazon.com/Economics-Dummies-Business-Personal-Finance/dp/0470879483/ref=sr_1_1?s=books&ie=UTF8&qid=1309949559&sr=1-1

You could try "Mostly Harmless Econometrics" by Joshua Angrist

Edit: https://www.amazon.com/dp/0691120358/ref=cm_sw_r_cp_apa_R1VFAbRYNTEV9

Mostly Harmless Econometrics is a classic. It gets into regression, but also design with inference in mind. Combine that with ISLR and you should be set!

Yeah, it's because they're all obsessed with statistical significance and nothing else. There's an interesting book about this actually: "The Cult of Statistical Significance": http://www.amazon.com/Cult-Statistical-Significance-Economics-Cognition/dp/0472050079

And it's sad, really. Because things are far more complex than that. It's entirely possible that a drug can increase the risk ON AVERAGE, even though it could increase the risk in SOME populations and decrease the risk in OTHER populations (although I presume that this doesn't happen very often for any specific [drug, risk] combo).

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/

Wooldridge's introductory text is the standard.

This e-book

Saw a Tim Harford link and remembered this. Also helped me understand more about human rationality and morality.

The undercover economist

Freakonomics

ORLY.

[It says 'introductory' right there in the title!] (https://www.amazon.co.uk/Introductory-Econometrics-Approach-Jeffrey-Wooldridge/dp/1111531048)

Hamilton

http://www.amazon.com/Freakonomics-Revised-Expanded-Economist-Everything/dp/0061234001/

http://www.amazon.com/Basic-Econometrics-Damodar-Gujarati/dp/0073375772

Read this and this and see how you go.

i'm not a economist, but from what i know, the intro books are Gujarati

and Woolridge

Try /r/algotrading they have an decent sidebar.

I would also suggest:

An Introduction to Analysis of Financial Data with R by Tsay

Analysis of Financial Time Series, again by Tsay

Algorithmic Trading and DMA by Johnson

Empirical Market Microstructure by Hasbrouck