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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'...
librus's user avatar
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3 votes
1 answer
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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 ...
EmH's user avatar
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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 ...
medium-dimensional's user avatar
2 votes
1 answer
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 ...
Uk rain troll's user avatar
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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 ...
grain's user avatar
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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 ...
EmH's user avatar
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1 answer
43 views

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 ...
Marlen's user avatar
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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?
MSKO's user avatar
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6 votes
1 answer
177 views

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 ...
Galen's user avatar
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28 views

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 ...
Arya's user avatar
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3 votes
1 answer
146 views

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 ...
jasmine's user avatar
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16 votes
4 answers
1k views

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 ...
Eike P.'s user avatar
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1 vote
1 answer
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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 ...
M.C. Park's user avatar
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2 votes
0 answers
68 views

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 ...
Morire Adeyemi's user avatar
2 votes
1 answer
54 views

Is this a valid model specification?

I am working with weekly, highly seasonal data (period=52) and fitting the following regression using lm: $$ y_t=\rho_1y_{t-1}+\rho_2y_{t-52}+\beta x_t+\...
steinchen's user avatar
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1 answer
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Linktest results interpretation

linktest's rule of thumb is that _hat should be statistically significant while _hatsq ...
Sue's user avatar
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3 votes
0 answers
107 views

A misspecification error with linear models that can complete reverse the direction of an effect, has this been described, has this a name?

Linear models are ubiquitous in economic, social, health and nutritional sciences and the starting point for much research and many articles. However, there is a problem with linear models. When the ...
Sextus Empiricus's user avatar
2 votes
0 answers
58 views

Why are parametric models usually robust to mild misspecification?

Statisticians often use parametric families of models, for example normal distributions with unknown mean and variance. However, nothing in real life is perfectly normally distributed (or distributed ...
alligator's user avatar
8 votes
4 answers
2k views

What is the benefit of regression with student-t residuals over OLS regression? [duplicate]

Sometimes I see advice to fit regressions with student-t residuals rather than using OLS (which is equivalent to assuming normally distributed residuals) if the distribution of the residuals is heavy-...
frelk's user avatar
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Not sure of model choice - significant p-values but very low R2 adj

I am writing a thesis in which I'm trying to see what the relationship is between how innovative a patent is (as determined by a series of measures based on NLP that) and the financial value of that ...
momaatala's user avatar
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Changing relation between independent and dependent variable over time

I am currently trying to run a linear regression model where the effect of one of my independent variables (Xit) on my dependent variable (Yit) changes over time. Specifically, I suspect the ...
Thomas Boerman's user avatar
1 vote
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24 views

Do insignificant variables result in a specification error?

I am trying to understand omitted variable bais better. I know that it detects irrelevant variables, but are irrelevant variables and insignificant variables synonymous here? If I have a regression ...
rabito's user avatar
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2 votes
1 answer
462 views

The exact meaning of "correctly specified"

I will cite a paper I am reading: (Bugni, Federico A., Ivan A. Canay, and Xiaoxia Shi. "Specification tests for partially identified models defined by moment inequalities." Journal of ...
M.C. Park's user avatar
  • 935
3 votes
1 answer
96 views

Concerns about pre-trend stability testing

In this discussion, @Thomas Bilach well explains the equation to test pre-trend stability. $$ y_{kt} = \alpha_k + \lambda_t + \delta_{-2} d_{k,t-2} + \delta_{-1} d_{k,t-1} + \delta d_{kt} + \delta_{+1}...
Phil Nguyen's user avatar
3 votes
1 answer
991 views

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....
Master Shi's user avatar
1 vote
0 answers
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 ...
Federico Tedeschi's user avatar
2 votes
1 answer
151 views

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 ...
Firona's user avatar
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0 votes
1 answer
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: ...
Carl's user avatar
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4 votes
1 answer
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 ...
peter5's user avatar
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1 vote
1 answer
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 $\hat{\beta}$, $\hat{\theta}$ ...
CorporateNationalism's user avatar
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1 answer
29 views

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 ...
user300602's user avatar
1 vote
0 answers
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 ...
Breaking Waves's user avatar
2 votes
1 answer
1k views

Question about regression using a rate vs a count as the dependent variable

Let's say I wanted to run a regression of mortality on an x variable, so I could see 3 different possibilities I can do I define a rate: $y = \left(\frac{\#deaths}{population}\right)$ and regress $$...
Steve's user avatar
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0 answers
111 views

Bayes estimates and model misspecification

Consider a misspecified model: $$P \sim \text{SegmentedUniform}(0,1) \\ Y \mid P \sim \text{Binomial}(N,P).$$ Where SegmentedUniform has uniform density on intervals $$(0.1, 0.2), (0.3, 0.4), (0.5, 0....
fool's user avatar
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1 vote
1 answer
2k views

Event Study with two treatments

Lets say I wanted to include two different treatments in a diff in diff at the same time, so I could have: $y_{i,t} = \lambda_i +\tau_t + Treat1_i*post_t+Treat2_i*post_t + \eta_{i,t}$, where $\...
Steve's user avatar
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1 vote
1 answer
109 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 ...
synack's user avatar
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4 votes
1 answer
406 views

Consequences of choosing the wrong GLM response distribution

In Chapter 10 of McElreath's Statistical Rethinking (2nd edition), he argues that the response distribution for a GLM should be chosen to maximize entropy given a set of constraints on the response ...
Tomas Bencomo's user avatar
0 votes
1 answer
86 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?
user avatar
4 votes
3 answers
546 views

Bayesian inference with false models: to what does it converge?

This is the second follow up question from these two previous questions: Bayesian inference and testable implications How do I perform an actual "posterior predictive check" in this model? ...
user avatar
3 votes
0 answers
1k 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 ...
user avatar
1 vote
0 answers
160 views

How bad are short-term forecasts of ARIMA models of ARFIMA processes?

Suppose I have a set of economic time series that appear to be a unit root process. I difference them, and fit an ARMA model to the differenced series. Suppose however that the true data generating ...
andrewH's user avatar
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1 vote
0 answers
56 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 ...
An old man in the sea.'s user avatar
2 votes
0 answers
43 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 ...
user1205901 - Слава Україні's user avatar
1 vote
0 answers
61 views

Are there any practical differences in fitting a random slope vs. fitting an interaction term in the intercept in lmer

I am trying to fit a few models with the form ...
Myriad's user avatar
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1 vote
0 answers
174 views

Buld prediction intervals for glmmTMB

I'm using glmmTMB to build a mixed effect logistic model from which I want to draw predictions. The predict() function applied to a glmmTMB model allows extracting the ML prediction and the relative ...
Bakaburg's user avatar
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0 votes
1 answer
2k views

Path analysis model fit statistics. RMSEA = 0.106

Hi folks, My path analysis is using primary data that I've collected for a uni project. After removing the very non-signif variables, I have a model (which I hypothesised) that seems to work pretty ...
Magenta Sunrise's user avatar
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0 answers
613 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'...
Anne Ng's user avatar
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3 votes
0 answers
324 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 ...
High GPA's user avatar
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5 votes
1 answer
234 views

Non-Linear regression and variance misspecification

Given a non-linear regression model for cross-section data $$y_i = f(x_i,\theta_0) + \epsilon_i,$$ where it is assumed that $\mathbb E[y_i\lvert x_i] = f(x_i,\theta_0)$, I understand that it is a ...
Jesper for President's user avatar
2 votes
2 answers
2k views

$R^2$ is too high- reasons? [closed]

What are the reasons of too high values of $R^2$? I only know that its value increases when the number of independent variables increases. What are the other factors?
los6996's user avatar
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