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I'm not sure there is a correct answer to this question (or if there is, we don't understand it yet!), but here's a first shot at an answer: Even if you look at the natural sciences, there is a process whereby ideas are refined over time. In the 17th century people 'understood' mechanics thanks primarily to Newton. But that didn't mean Einstein couldn't ...


9

I.) 2 Principles of econometrics can potentially be useful compared to Machine Learning. (see Hal R Varian 2014 Paper : https://pubs.aeaweb.org/doi/pdf/10.1257/jep.28.2.3) A.) As you suggest the search of causality is one advantage but unlike what you think, even if causality sometimes could be tricky to measure it remains very useful and functional. But ...


5

I'm skeptical that this will work. I'm concerned that for the sort of shock that is large enough to be interesting and large enough to study that stock prices will move before they open. You could potentially measure this by looking at the changes in stock price from close to open, but I'm not sure what you'd be measuring. Are changes in Tokyo stock prices ...


5

Math is easier if you are smarter. As such, math education is a costly and therefore credible signal of general intelligence. Below are two experiments that try to get around this selection issue by looking at exogenous variation in worker mathematical ability on labor market outcomes. However, a word of caution. They do not present evidence that ...


4

Economists (most of them) build their models assuming most of the time stochastic dynamic equilibrium. So Economics does not contrast "dynamic" with "equilibrium" - it synthesizes them. It is stochastic in the sense that random shocks are acknowledged. It is dynamic in the sense that it may revolve around a deterministic or stochastic trend. And it is an ...


4

Ubiquitous has provided a very good explanation for what constitutes understanding an economic problem. I'd like to address the second part of your question about what sort of "key questions" are solved in economics (if any). First, the obvious. We have to talk about what meaningful economic problems are, that economists are best suited to address. ...


3

"Identification" is the most loaded term in econometrics. There are multiple cheap talk equilibria with regard to its meaning. It is used with different intended (but related and overlapping) meanings, in different contexts, by people with different orientations, with different levels of precision. Therefore you will get a range of correct answers. ...


2

Yes, you have to worry about the difference between correlation and causality. In these situations, it helps to try to force a case of ommited variable bias. In this case "talent" or "effort" is unobserved. Both this might make people more likely to pick up additional education, and also be more successful at their jobs. Or family background: If your ...


2

Are you looking for general references on Diff-in-Diff (DiD) and Regression Discontinuity (RD)? If yes, a good starting reference at the undergraduate level is probably J. Angrist and J.S. Pischke, Mastering' Metrics: The Path from Cause to Effect, Princeton University Press, 2014. Look at chapters 4 and 5 for RD and DiD. If who want to dig deeper: ...


2

My view coincides with the introduction to your question. Namely, a) Econometrics is mostly concerned with causality b) Machine learning is mostly concerned with fit But for the remaining part, our views depart. Here is why: a) IV (and other quasi-experimental techniques) are not the only way to test for causality. The alternatives are i) experiments ii) ...


2

Because the papers which use these methods are not properly refereed. That's why you should read papers published in high impact factor journals.


2

Your first point is valid for not using fixed effects as you are interested in the entire border not just these 16 roads. Random effects can be advantageous when you have such a small sample compared to the population for your treatment group. Also note, if the unobserved effect has a large variance or T is very large then RE will be close to FE anyway. ...


2

I think the best way how to explain this is to first quickly explain what identification actually is. As mentioned in this thread: For example, in the John Stachurski "A Primer in Econometric Theory" the identification is a process of finding out if the parameters are identifiable and identifiability is defined as “Identifiability means that the ...


1

"Identification" is the professional jargon in econometrics for "asserting that the outputs from an econometric model do indeed estimate what we want and declare that they estimate". "Identification" does not include an assertion that a specific estimate coming from combining a specific estimation method with a data sample, will ...


1

If you run a vector autoregression you could also follow up by testing for Granger causality. I don't know whether this is implemented directly in SPSS, but once you have an estimated model it is easy to calculate the statistic as it is basically just an F-test. In R you can do it directly via for example the package vars.


1

So you are right. It is extremely difficult to prove causality in economics. Using an instrumental variable is a good way to do so. I think you might be a little confused about the difference between "machine learning" and Econometrics. Machine learning works in 2 ways: 1) You have a massive data set with the correct answers already keyed in. You split the ...


1

Ideally, but not always feasible, the first option would be to select a similar region where the law is not in place and compare them (e.g. different country / State / city) If it is not possible to compare with another region, then chose a similar industry. I would try to justify it well though, and comparability would be harder to prove. For example, if ...


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