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  • What does a data-generating process (DGP) actually mean?
    $\begingroup$ The hope is that you understand the DGP well enough that your observed result aligns closely with the proposed model of the DGP (agreement with expected result) The deviations between observed and expected indicate how well the inference model fits the outcome of the true underlying DGP and how completely it has been described
  • Whats the DGP in causal inference? - Cross Validated
    The linear true model is the more used object name in econometrics literature in place of DGP In econometric literature the role of causality is important even if many times is not properly treated (for example read: Under which assumptions a regression can be interpreted causally? and Is the linearity assumption in linear regression merely a
  • 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)
  • econometrics - In regression analysis whats the difference between . . .
    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
  • 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
  • How to set up a DGP for Monte Carlo simulation with non-independent . . .
    I want to set up a data generating process for two different estimations The idea is to show how bias is introduced when the models are not properly specified The first model should be a logit pr
  • causality - Under which assumptions a regression can be interpreted . . .
    So, DGP must be precisely the causal mechanism we are interested in, and our SCM encode all we know assume about the DGP Read here for more detail about DGP and SCM in causal inference: What's the DGP in causal inference? Now You, as most econometrics books, rightly invoke exogeneity, that is a causal concept:
  • What is the difference between errors and residuals?
    Errors pertain to the true data generating process (DGP), whereas residuals are what is left over after having estimated your model In truth, assumptions like normality, homoscedasticity, and independence apply to the errors of the DGP, not your model's residuals
  • When is it appropriate to use an improper scoring rule?
    In this more realistic setting, if our forecasting task is easier than to figure out the entire density of the true DGP we may actually do better This is especially true for classification For example the true DGP can be very complex but the classification task can be very easy Yaroslav Bulatov provided the following example in his blog:
  • What is an appropriate observation data points in time-series . . .
    $\begingroup$ More data from the same data generating process (DGP) is better for accuracy But DGPs in areas like macroeconomics and finance tend to evolve over time, so samples spanning longer time periods risk having the DGP at the beginning being quite different from the DGP at the end $\endgroup$ –





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