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What does a data-generating process (DGP) actually mean? A DGP is a mathematical description of reality (in econometrics one seems to often abstract reality to a so called "true DGP") What I am saying is that stating a DGP seems to allow ambiguity about what statement about reality is actually being made
Population vs. Data-Generating Process - Cross Validated On the other hand, some, especially new papers in Causal Infrence, instead to population refer to Data-Generating Process (DGP) An example could be "The Identification Zoo: Meanings of Identification in Econometrics" by A Lewbel
A Rigorous Definition of Data Generating Process (DGP) I am trying to find a rigorous mathematical definition of a data generating process (DGP) under a well-defined probability space 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)
Relationship between distribution and data generating process In econometric theory we refer to the underlying common distribution F as the population Some authors prefer the label the data-generating-process (DGP) You can think of it as a theoretical concept or an infinitely-large potential population
Under which assumptions a regression can be interpreted causally? The question is: 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:
OLS estimator of ARMA(1,1) process - Cross Validated When I solved the DGP in the picture, I got an ARMA(1,1) process with intercept term (1-a)*mu To solve my problem I need the (X'X)^-1(X'Y) form equation of "mu hat" How can I derive the equation in
Classical Linear Model Assumptions: Stationarity So, we have the following logical link: Assumptions about DGP $\to$ Set of appropriate DGPs $\to$ Set of appropriate joint distributions $ (y, X)$ My question: which assumption (s) in Ch 1 about joint distributions $ (y, X)$ was induced by ergodic stationarity assumption about DGP in Ch 2?
hypothesis testing - Understanding different versions of Augmented . . . 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): under the null, the process becomes a unit root with drift, while under the alternative, it is a linear time trend