Questions tagged [misspecification]

Problems with model specification, such as missing variables/predictors, wrong functional form, wrong variance or covariance structure, etc.

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28
votes
6answers
75k views

Inclusion of lagged dependent variable in regression

I'm very confused about if it's legitimate to include a lagged dependent variable into a regression model. Basically I think if this model focuses on the relationship between the change in Y and other ...
9
votes
2answers
318 views

Statistical inference under model misspecification

I have a general methodological question. It might have been answered before, but I am not able to locate the relevant thread. I will appreciate pointers to possible duplicates. (Here is an excellent ...
7
votes
1answer
247 views

Effects of model selection and misspecification testing on inference: Probabilistic Reduction approach (Aris Spanos)

This question is about pre-test bias, inference after model selection and data snooping within the Probabilistic Reduction (PR) methodology by Aris Spanos (which is related to the Error Statistics ...
6
votes
2answers
4k views

Conditional vs. Marginal models

I have data with an outcome of 0 or 1 (binary) representing success or failure. I also have two comparison groups (Treatment vs. Control). Each subject in the study contributed 2 observations (the ...
10
votes
2answers
7k views

When to use (non)parametric test of homoscedasticity assumption?

If one is testing assumption of homoscedasticity, parametric (Bartlett Test of Homogeneity of Variances, bartlett.test) and non-parametric (Figner-Killeen Test of ...
5
votes
2answers
2k views

Robust standard errors in econometrics

I keep hearing my professor try to explain that we can use robust standard errors when we run a regression to confront the issue of heteroskedasticity. However I don't quite understand how telling ...
3
votes
0answers
201 views

How do I perform an actual “posterior predictive check”?

This question is the follow-up of this previous question: Bayesian inference and testable implications. For concreteness, consider the following bayesian model. This model is not to be taken ...
3
votes
1answer
3k views

Strictly positive response in regression: what should my “default” model be?

For unbounded continuous responses, Gaussian errors are the analyst's default model for many reasons, one of them being that their ML estimate coincides with the OLS estimate that has many desirable ...
5
votes
1answer
600 views

Statistical Significance or Unambiguous Direction of Influence?

TL; DR In the context of a linear regression model, we run a statistical test for whether an estimated coefficient is "statistically significant". We will say that it is if we reject the null of it ...
2
votes
1answer
556 views

How to calculate sandwich standard errors for generalized least squares models?

Dependent data can be modeled using covariance structures like compound symmetry, spherical, AR-1, and other. Using generalized least squares, inference can be made on the regression coefficients ...
26
votes
2answers
5k views

Is it true that Bayesian methods don't overfit?

Is it true that Bayesian methods don't overfit? (I saw some papers and tutorials making this claim) For example, if we apply a Gaussian Process to MNIST (handwritten digit classification), but only ...
23
votes
2answers
1k views

Why doesn't Wilks' 1938 proof work for misspecified models?

In the famous 1938 paper ("The large-sample distribution of the likelihood ratio for testing composite hypotheses", Annals of Mathematical Statistics, 9:60-62), Samuel Wilks derived the asymptotic ...
15
votes
2answers
284 views

Statistical Inference Under Misspecification

The classical treatment of statistical inference relies on the assumption that that a correctly specified statistical is used exists. That is, the distribution $\mathbb{P}^*(Y)$ that generated the ...
3
votes
1answer
122 views

Recommend monograph on statistical model misspecification

Is there a good book on statistical model misspecification in general? It should cover, for example, the behavior of estimators (e.g., maximum likelihood) when the specified parametric family does not ...
2
votes
1answer
295 views

Robust error estimation and hazard ratio with non-proportional hazards

I recall having heard that the hazard ratio, estimated in a Cox model, can be made robust against the parallel hazard functions assumption. The key to this is using a Huber-White, or Huber-Eicker-...
2
votes
0answers
212 views

Maximum likelihood estimation for incorrect distribution parameter

Supposing I have a random variable $X$ with distribution $$f(x | \boldsymbol{\theta}, \boldsymbol{\varphi}).$$ Here $\boldsymbol{\theta} = (\theta_1,\theta_2,...,\theta_r)$ are $r$ parameters which ...
6
votes
1answer
1k views

Fixed Regressor Conspiracy and Connection to Exchangeability

In simple regression model regressors are treated as fixed rather than stochastic. Whoever picks the experimental values for the regressors, decides in which frequency to include each value. This can ...
4
votes
1answer
1k views

Does the sandwich estimator in GEE protect against both correlation misspecification and heteroscedasticity?

The relative merits of GEE with exchangeable correlation or GEE with independence and the sandwich estimate have been discussed, but I couldn't find a post specifically addressing my question. I have ...
0
votes
1answer
62 views

Practical numerical example illustrating how bayesian p-values should be used for model checking?

Can you give a practical numerical example illustrating how bayesian p-values should be used for model checking?