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)...
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 ...
5
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 ...
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
How to correct selection bias in an econometric analysis?
The parallel trends assumption of Diff-in-diff is essentially an assumption that there is no selection bias. If you are curious about the parallel trends assumption, I encourage you to ask about it in ...
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 ...
3
votes
Simultaneous Causality and Variance within a supply and demand model
$$(1) P_i = \beta_0 +\beta_1Q_i + \beta_2X_i +u_i$$
$$(2) Q_i = \delta_0 +\delta_1P_i + \delta_2W_i +v_i$$
Plugging (2) into (1) and simplifying yields,
$$P_i = \beta_0 +\beta_1(\delta_0 +\...
3
votes
Accepted
Articles on the definition of causality in Economics
As 1muflon1 said, the concept of causality in economics is intertwined with the concept of causality in other sciences, in particular statistic and econometrics, and the concept of probabilistic ...
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 ...
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 ...
3
votes
Abadie's Kappa (2003) for continuous treatments / IVs
No. There is not. I tried working on deriving one for a while. I struggled for a month, failed, and then emailed some contacts at the University of Chicago to ask if they had any ideas. My buddy asked ...
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.
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 ...
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
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 ...
2
votes
Accepted
How to correct selection bias in an econometric analysis?
As the other answer mentioned, if the Diff-in-diff assumptions hold, it should not be a problem.
However, if you think selection might be invalidating your diff-in-diff assumptions, you have two ...
2
votes
Causal link between business expansion and profit
No you are not off.
Expanding first to eventually become profitable is what happens often with large projects, where there is the capital to cover the operational losses during the initial expansion.
...
1
vote
Granger-Sims causality and subtle differences
Let $\mathbf{X}$ be conditionally iid on $\{0,1\}$ with distribution $(P,1-P)$, with $P$ being a nontrivial random variable. Let $Y_t=\limsup_{T\to\infty} T^{-1}\sum_{i=1}^TX_{t-i}$.
Then $\textbf{X}\...
1
vote
Diff-in-diff with two control groups: compare parallel trends
Background
The typical thing to do is visually inspect the pre-treatment trends for the control and treatment groups. Whether you want it or not, you might be biased when looking at the visual ...
1
vote
Diff-in-diff with two control groups: compare parallel trends
I'm not understanding-- could you please talk a little about why both can't be used here? It's been a while since I've really worked with DD, but as far as I'm aware, there's not real metric you can ...
1
vote
Causal link between business expansion and profit
I agree with you that in some cases firms expand first and then expect to gain profit. For example, Coursera expanded quite quickly during the pandemic (in terms of course offerings, number of ...
1
vote
Causal link between business expansion and profit
As pointed by other answer your thinking is not totally wrong. This being said, I think what your professor is trying to warn you about is potential for reverse causality. There is a rich literature ...
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 ...
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 ...
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 ...
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 ...
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