# Tag Info

11

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 ...

6

"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. ...

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 ...

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 ...

4

What does industry * year fixed effect mean? $Industry \cdot year$ fixed effect is just an interaction term between industry and dummy year variables. For example, you can have dummy particular industry, let us say finance where $D=1$ if firm is a finance firm and $0$ otherwise, then you can have a year dummy which will be set to equal $1$ for particular ...

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. ...

4

This question is related to a post I addressed on CrossValidated. The "generalized" difference-in-differences (DiD) estimator is amenable to settings with multiple groups and multiple exposure periods. Take the following specification: $$y_{it} = \gamma_{i} + \lambda_{t} + \delta T_{it} + \epsilon_{it},$$ where $\gamma_{i}$ and $\lambda_{t}$ ...

3

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 ...

3

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

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

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

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

The Neymen-Rubin potential outcomes terminology is is not typically used in economics outside policy evaluation where your policy will be binary. This being said there are still counterfactuals. For example, if you are regressing interested rate on car purchase, if at time $t$ interest rate $i$ was 6% and associated car sales $s$ were 500 of cars at ...

2

Short answer: No. Your model is $Y=\alpha + \beta X + \varepsilon$. Even when $X$ is exogenous, if you regress $Y$ on $X$, $W_1$ and $W_2$, then the OLS estimator is inconsistent (for $\beta$) unless $W_1$ and $W_2$ do not affect $Y$ on average (after controlling for $X$) or $X$ is uncorrelated with $W_1$ and $W_2$. When $X$ is endogenous, there is no reason ...

2

You might be inspired by Joshua Angrist (MIT) who talks in this podcast about the craft of econometrics--how to use economic thinking and statistical methods to make sense of data and uncover causation. Using natural experiments is a good way to establish causation. A natural experiment is an empirical setting in which individuals are exposed to 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

I think the essence of this question is actually asking the difference between statistics and econometrics. You can find some good answers here. Here is my try on a simple - and maybe abstract, but I think useful way of - classification of these things. ML are statistics models. Econometrics are often the combinations of economics models and statistics ...

1

Here is a basic answer for anyone too uninterested to read the long answers: 1) ML focuses on prediction and not on causality (as does metrics) 2) ML is powerful for parameter selection and model validation 3) Many ML algs. are incredibly similar to basic metrics approaches. For example, ridge regressions and LASO are both just small extensions of OLS.

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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