# 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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### Variance ratio when including irrelevant variables in a regression model

I am interested to know if there is a general formula for the ratio of the variance of regression coefficient for a predictor in a correctly specified model and a misspecified model. Specifically, let'...
37 views

### How do I interpret a second-order multi-variate growth model?

I am running a multi-variate second order growth model. I have two factors, which are conceptually related to each other measured on 7 different occasions. Wanting to know how the two factors ...
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1 vote
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### How do I prioritise model diagnostics while considering model selection and parameter uncertainty?

I have fitted a generalized linear mixed model using glmmTMB on the data (110 observations, balanced data) collected from an observational study to understand the ...
95 views

### What happens when you perform MLE on the wrong Probability Distribution Function?

Suppose we have some data sample $x_1, x_2, ... x_n$ that came from some true probability distribution function $f(x; \theta)$. Based on this sample, i am interested in estimating $\theta$ using ...
1 vote
35 views

### OLS when X1 X2 and Y have unit roots: I(1), but not cointegrated

I have three log-transformed time series variables, which are: I(1) and not cointegrated. I read in a lecture slide that I can rethink my model by adding an LDV. This solves my autocorrelation problem ...
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### Do you need to correlate factor residuals of factors that are measured at the same time point

I am running a second-order (multiple indicator) latent growth curve. The model has three latent factors (excluding the growth intercept and slope factors) that each have 4-5 indicators. Two of them ...
• 315
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### How to interpret a confusingly large discrepancy between True Negative Rate and the number of Overspecified Models Selected by LASSO

Here it the link to the GitHub Repository for this project. I am going to preface my question by saying that this problem of interpretation I have run into is in the context of me doing my part as a ...
• 147
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### AR model with white noise model, DGP is T(5), why no misspecification?

Let $x_t$ =ϕ$x_{t−1}$+$ε _t$ be the model with errors being white noise. ​ If DGP is $X_t$=$e_t$ which is T(5) distributed. Why no misspecification?
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### No more than $n$ moose, but how many?

Introduction I am thinking about how to estimate the number of individual moose from wildlife camera photos. I have the latitude and longitude position of each observation, along with a datetime of ...
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### Link test for pooled logistic regression

After logistic regression of the cross-sectional data sets, link test _hatsq shows insignificant. However, when I pool the same two data sets, the link test using the same set of variables regression ...
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### Ramsey RESET Test

The Ramsey RESET test uses the fitted value of y to test nonlinearity, for example: $$y_i=x_i\beta+\epsilon$$ $$\hat{y_i}=x_ib$$ $$y_i=x_i\beta+\gamma\hat{y}^2_i+u_i$$ Test if $\gamma=0$ Why do ...
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### Priors that do not become irrelevant with large sample sizes

This may be a weird question. My colleagues and I are working on a medical estimation problem, where relevant prior knowledge regarding plausible values of some physiological parameters exists. In ...
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1 vote
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### The concept of a specification test

There is a confusing facet of the concept of a specification test. According to many textbooks, a specification test is roughly a statistical test investigating whether assumptions in a statistical ...
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### Equation for a DiD/Event Study Combining Several Simple DiD

I am trying to do a Difference in Differences/Dynamic Event Study but my data doens't easily lend itself to a classic staggered treatment DiD nor a simple 2X2 DiD. My dataset has 1-min readings from ...
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### What is the consequence of misspecification in logistic regression

In linear regression, the Ramsey RESET test can be used to test if the model is misspecified. The Gauss-Markov theorem allows us to understand the consequences of misspecification in linear regression....
• 713
1 vote
122 views

### Is traditional negative binomial regression robust to model misspecification or not?

By "traditional" NBR I mean NB2, i.e. the one modeling variance as a quadratic function of the mean, with the formula: $Var(Y)=E[Y]*(1+\alpha*E[Y])$. I have found contrasting statements in ...
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### Does the problem of multiple testing also apply to the testing of assumptions?

When dealing with statistical tests, sometimes we can run into cases where many assumptions would apply and consequently would need to be tested. In complex models, testing many assumptions at 5% may ...
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624 views

### Simple explanation of Takeuchi’s information criterion?

Takeuchi’s information criterion is said to be the generalization of AIC to misspecified models. That publication presents DEGREES OF FREEDOM FOR NONLINEAR LEAST SQUARES ESTIMATION. From that source: ...
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507 views

### ARMA/GARCH statistical significance of estimated parameters

My question is general and is concerned with ARMA-GARCH modeling. When performing the joint estimation of the ARMA and GARCH parts, some works tend to not be concerned with the statistical ...
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1 vote
229 views

### Wald Statistic Simplification - 2 Restrictions?

(Specify the wald statistic as: W=$(R\hat{\theta}-r)'[R\hat{V}(\hat{\theta})R']^{-1}(R\hat{\theta}-r)$ Where r is a vector of restrictions, R imposes the restrictions on $\hat{\beta}$, $\hat{\theta}$ ...
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### Issues in testing a simple linear relationship. Collinearity? Misspecification? Any other insight?

I have a theoretical model saying that Y should be equal to: Y = X + c * (W - X) + (Z1 - Z2), where c is a given constant. Here, it may be important to say that X is measured with error. Someone ...
1 vote
122 views

### Misspecification in the outcome model with regression adjustment for propensity score?

The propensity score is a popular tool used to control for confounding by covariates $C$ on the effect of an exposure $A$ on an outcome $Y$. There are several ways to incorporate the propensity score ...
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