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4 de jan. de 2020 · 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. It seems like maybe you have DGP coming up in a specific context.
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
1 de nov. de 2020 · Now, remaining simple and closest as possible to the econometrics literature the proper way for make causal inference is consider the true model as an linear SCM. So: y = X′θ + ϵ y = X ′ θ + ϵ. we can interpret all three objects [y, X, ϵ] [y, X, ϵ] as random variables (X X is a vector). Read here for more details: linear causal model.
15 de out. de 2020 · Therefore, the DGP can be simplified to the following MA (1) type process (ut ≡ Δet u t ≡ Δ e t): Zt ≡ ΔXt − ΔXt−1 = β + Δut Z t ≡ Δ X t − Δ X t − 1 = β + Δ u t. So the random variable Zt Z t has a particular distribution with mean value β β, which will be estimated from given observations. And while that is true, it ...
16 de ago. de 2021 · Then, the adversary (or the collection of all adversaries) represents the data generating process (DGP) that generates the distribution of spam. When the adversary changes its strategy, for instance, starts using the topic of coronavirus in spam emails in 2020, does the DGP change, or are new spam variants just observations from the distribution of spam that have never occurred until 2020?
20 de dez. de 2020 · 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.
17 de ago. de 2022 · 3. 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). Are there other rigorous definitions of a DGP or a reference textbook that ...
19 de dez. de 2014 · $\begingroup$ interesting: one idea I had which sounds similar is the following: take one of the data sets, estimate it blindly to get the estimated parameters. then back out the estimated variance using rss/n-k and use that as my sigmasquared hat and then set that sigmasquared hat = var(e_t) + gammasquared var(e_t) to back out var(e_t).
14 de jan. de 2015 · 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.
These assumption must stay outside, in a structural causal model. You need two objects, no just one. The structural causal model stand for theoretical-causal assumptions, exogeneity is among them and it is needed for identification. Regression stand for estimation (under other pure statistical assumption).
