# Questions tagged [misspecification]

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

41 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
0answers
103 views

### When does the prediction of random effects matter?

In linear or generalized linear mixed effects models, random effects are incorporated to explain the within-unit correlation for repeated measures over time. In Bayesian modeling, conventional prior ...
0answers
77 views

### literature on small samples and parametric survival models

I have an abundance of small data sets with right-censored data. There are different groups in each data set and I'd like to get confidence intervals for the regression parameters. Each data set has 3-...
0answers
222 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 ...
0answers
76 views

### Over “specification” of a statistics model?

For a simple example, I am fitting data with a likelihood function generated by a normal distribution. The first model is the normal distribution with two-parameters. The second competing model is the ...
0answers
76 views

### Does a high Chi square p-value for a whole model mean it is insignificant if likelihood ratio tests indicated variables should be added?

I've been estimating lots of versions of the same model by incrementally adding variables. With some variables, if I add them to the model, the likelihood ratio test indicates that they are ...
0answers
5k views

### What to do when ovtest and linktest in Stata suggest model misspecification?

I have a sample that consists of 50 observations. The base model of the OLS-Regression with three control variables, two of them significant, has a $R^2=0.50$ and its F-Value is 7. Both ...
0answers
17 views

### SEM: Why is there no dependable connection between the size of SEM residuals and the type or degree of model misspecification?

Kline (2016) p. 278 writes [Y]ou should know that there is actually no dependable or trustworthy connection between the size of the residuals and the type or degree of model misspecification. For ...
0answers
28 views

### Random effects for mutually inclusive grouping factors

I am trying to fit a model on a set of data (e.g. 10,000 observations with 20 explanatory variables). The observations belong to 30 groups, G1, G2, G3, ... G30, so I need to account for the grouping ...
0answers
83 views

### Relative importance different model diagnostics in LMM/GLMM

I may be over thinking things but I have been stuck on this issue for the last few days. I am currently modelling data with a large number of clusters but cluster size is limited to 2, with a skewed ...
0answers
223 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 ...
0answers
58 views

### How to compute the likelihood when uncertain about underlying distribution

In a Bayesian approach, when I have no previous idea about the likelihood of a given event, can I simply gather a lot of data and use that distribution as my likelihood (1) and as I gather more and ...
0answers
569 views

### Comparing Classical and Robust (Huber-White/sandwich/heteroscedasticity consistent) Standard Errors in Linear Multiple Regression

I'm running a linear multiple regression model of the type $y_i = \beta_0 + \beta_1 X_{i1} + \beta_2 X_{i2} + \beta_3 X_{i3} + u_i$. I came across King and Roberts' 2015 paper called "How Robust ...
0answers
101 views

### Is there a way to correct standard errors and/or prediction intervals for multiple comparison after doing backwards selection?

It is well known that most model selection algorithms can easily fall into a multiple comparison trap. To quote Friedman: Consider developing a regression model in a context where substantive ...
0answers
15 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 ...
1answer
34 views

0answers
8 views

### Variable significance very sensitive to specification of non-correlated second variable

IĀ“m doing research on a political science topic and my models leave me behind with a big questionmark at this point. I have a dataset containing 79 observations on a number of variables and trying and ...
1answer
30 views

### Role of misspecification by biased data in the generalization error

I am confused with the role that model misspecification plays in the generalization error, in particular when the misspecification is due to a biased (non representative) training dataset. To clarify ...
0answers
29 views

### Bayesian model averaging when none of the models is well specified

Usually, from what I've read of Bayesian Model Averaging (BMA), a typical assumption is that one of the models is well specified... However, what will happen when all models are misspecified? What ...
0answers
11 views

### How does misspecification of a structural equation model impact the results or interpretation of the model?

Any ideas? I am new to SEM and am struggling to articulate why and how misspecification of the model affects results and interpretation of the SEM.
0answers
90 views

### how to interpret the smoothed effect of time in GAMM models

I have recently taken a plunge into the world of Generalized Additive Mixed Models (GAMMs) after learning that including the quadratic and cubic fixed and random effects of time to understand students'...
1answer
61 views

### Finding SSE when ignoring a parameter in estimation

The model is $Y=XB+Zy+e$ where $B$ and $y$ are unknown parameters and $e\sim \text{N}(0,Ļ^2 I)$. Using ordinary least squares and ignoring $Z$ in parameter estimation, I need to find the distribution ...
0answers
89 views

### How to solve a well fitted model - Model Misspecification

I am currently writing a paper, analysing the impact of goldprice movements on the capital structure of gold mining firms. My basic model is a simple OLS model with (y=leverage and x=ln(goldprice)). ...
0answers
2k views

### regression of a level vs a change variable

This question has likely been asked already but due to the lack of proper terminology I might not have been able to find google up the relevant questions. We have data for several years: 2000, 2001, ....