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A dgp is a mathematical description of reality (in econometrics one seems to often abstract reality to a so called true dgp) Under the null, the process becomes a unit root with drift, while under the alternative, it is a linear time trend. What i am saying is that stating a dgp seems to allow ambiguity about what statement about reality is actually being made.
What's the dgp in causal inference In particular, it shows which dgp we need to have in mind in the trend case (and the same idea applies in the constant case) Ask question asked 5 years, 1 month ago modified 5 years ago
An example could be the identification zoo
Meanings of identification in econometrics by a The dgp is the true model The model is what we have tried to, using our best skills, to represent the true state of nature The dgp is influenced by noise
Noise can be of many kinds One time interventions level shifts trends changes in seasonality changes in model parameters changes in variance if you don't control for these 6 items than your ability to identify the true dgp is reduced. In econometric theory we refer to the underlying common distribution f as the population The closest source i have found on cross validated is this one, and it seems to come from a evans and rosenthal textbook (see the post).
Under which assumptions of the dgp $\text {d}_x (\cdot)$ can we infer the regression (linear or not) represents a causal relationship
It is well known that experimental data does allow for such interpretation For what i can read elsewhere, it seems the condition required on the dgp is exogeneity: So, we have the following logical link Assumptions about dgp $\to$ set of appropriate dgps $\to$ set of appropriate joint distributions $ (y, x)$
Which assumption (s) in ch 1 about joint distributions $ (y, x)$ was induced by ergodic stationarity assumption about dgp in ch
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