11 votes

When can economists claim to "understand" an event or phenomenon?

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 ...
  • 16.6k
9 votes

The relation between econometrics and machine learning

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 ...
6 votes

Identifying assumption meaning

"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)...
  • 2,579
6 votes
Accepted

How time zones affects causality?

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 ...
  • 16k
5 votes
Accepted

Does mathematical education imply a better salary?

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 ...
  • 16k
4 votes
Accepted

Difference-in-differences with long time horizon and repeated treatments

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 ...
4 votes

Looking for discussion on equilibrium vs dynamic models in econometrics

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 ...
4 votes

When can economists claim to "understand" an event or phenomenon?

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"...
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3 votes

Identifying assumption meaning

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 ...
  • 45.1k
3 votes
Accepted

What are the difference between industry fixed effects and industry*year fixed effects?

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 ...
  • 45.1k
3 votes

The relation between econometrics and machine learning

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 ...
2 votes

Estimating causal effect for educated people on wages

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 ...
  • 10.5k
2 votes

Group contamination in Regression Discontinuity

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. ...
  • 6,672
2 votes

Stock return predictability. Why do papers continue to use Granger Causality tests

Because the papers which use these methods are not properly refereed. That's why you should read papers published in high impact factor journals.
  • 1,960
2 votes
Accepted

Justification for my Random Effects estimation

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 ...
2 votes
Accepted

How does counterfactual for continuous variables work?

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 ...
  • 45.1k
2 votes
Accepted

Interacting covariates with the instrument in the first stage

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 $...
  • 2,039
2 votes

Where to find information to establish causation between economic policies and economic data?

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 ...
  • 6,672
1 vote

Identifying assumption meaning

"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"...
1 vote

causal time series analysis economics

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 ...
  • 56
1 vote

How to estimate loss of customers due to customer support inefficiency?

Maybe you can do a Probit regression? Let Y be a binary indicator. If the customer continues to use the website frequently (you can determine the threshold for that) after leaving the comment, then Y ...
  • 316
1 vote

The relation between econometrics and machine learning

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 ...
  • 2,239
1 vote

The relation between econometrics and machine learning

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 ...
  • 2,901
1 vote

The relation between econometrics and machine learning

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 "...
1 vote

I'm studying the effect of a law on a sector: how do I select the control group?

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 ...
  • 2,155

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