Questions tagged [omitted-variable-bias]
The omitted-variable-bias tag has no usage guidance.
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Can a confounding factor hide a possible causal relationship? (as opposed to find a spurious one)
I'm a rookie with statistics, and I'm struggling to understand this:
it is well known that a confounding factor can cause a spurious association, leading to rejecting a true null hypothesis (i.e. due ...
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Omitted variable bias vs. Multicollinearity
There's seems to be a bit like catch 22: suppose I am doing linear regression, and I have 2 variables that are highly correlated. If I use both in my model, I will suffer from multicollinearity, but ...
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Omitted Variable Bias (OVB) and multicollinearity
In a linear regression model, the reason we control for variables is to prevent the omitted variable bias (OVB). That is, suppose we are trying to fit the model
$$
Y = \beta_{0} + \beta_{1}X_{1} + \...
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Difference Omitted Variable Bias and Confounding?
Is there a difference between omitted variable bias and confounding bias in linear models?
To my knowledge, when investigating the causal effect of $X$ on $Y$, a confounder is a variable $Z$ that is ...
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How is the omitted variable bias formula derived?
I see it is often quoted that the omitted variable bias formula is
$$
\text{Bias}\left(\widehat{\beta_1}\right) = \beta_2 \cdot \text{Corr}\left(X_2,X_1\right)
$$
where $\widehat{\beta_1}$ is the ...
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Using a DAG to understand omitted variable bias in OLS vs Binary Dependent Variable Regression
Suppose I have three variables. $A$ and $U$ are continuous variables but $U$ is unobserved. $Y$ is the binary outcome. $A$ and $U$ are independent.
Let the true model be from the typical probit or ...
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What would it take for the omitted-variable bias from multiple omitted variables to cancel out?
Let's stick to ordinary least squares linear regression for now, and assume the typical conditions for the Gauss-Markov theorem. If it is helpful to assume Gaussian errors, that's fine.
In such a ...
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Does confounding always imply endogeneity?
I'm a bit confused with the definitions regarding causal inference. My question is whether we can call measured confounding an endogeneity problem?
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Does Omitted Variable Bias Matter for Prediction? [duplicate]
In the context of linear models, I can see why omitted variable bias may matter, as often we are interested in causal effects.
In the context of time series models, we are often interested in ...
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Question about statement in Oster (2019): variation in a control
In Oster (2019), she discusses how authors typically include controls and examine coefficient stability as a way to test for presence of confounding, and points out that researchers should consider ...
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Adjusting for confounding in linear regression model
I am wondering how would the slope and intercept change after adjusting for a confounder factor. After adjustment, would the slope be lower, or higher, and the value for the intercept?
Is there any ...
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Difference between the concept of omitted variable bias in econ and epidemiology/social sciences (Elwert and Winship)
I am currently reading the article by Elwert and Winship's Endogenous Selection Bias: The Problem of Conditioning on a
Collider Variable.
However, I am however quite perplexed by the definition of ...
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Should I adjust for a confounder when it is colinear with a predictor?
Suppose the DAG in the population is as follows:
We observe both $X_1$ and $X_2$.
We are interested in the effect of $X_1$ on $Y$. We want to use OLS to estimate the relationship.
Now if I take $X_2$ ...
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Trade-off between omitting variables or dropping observations in multivariate logistic regression
Say you are selecting $n$ observations from a complex survey of $N$ individuals to create an analytical sample of relevant observations; and that you intend to fit a binomial multivariate logistic ...
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How to test whether OVB by examining two regressors (X_1, X_2) using hypothesis test with null hypothesis H0: corr(X_1,X_2) = 0
Suppose you have an i.i.d. sample ${(𝑌_i , 𝑋_{1,i} , 𝑋_{2,i} ): 𝑖 = 1, ... , 𝑛}$. You want to estimate the causal effect
of $𝑋_1$ on $𝑌$. You first run a regression $𝑌_i = 𝛼_0 + 𝛼_1𝑋_{1,i} +...
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Using an IV when there is more than one omitted variable
I am trying to estimate the following model:
$$y=B_0 + B_1x_1 + B_2x_2 + B_3x_3 + e$$
However, I have an omitted variable bias because $x_2$ and $x_3$ are not observed.
Situation 1
If I have an (...
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Can an omitted random variable cause "omitted variable bias"?
Suppose we have a linear regression:
Y = mx + b
where X is the independent variable of interest, in this case "scoops of ice cream per order" at an ice cream shop, b is the error term, and Y is the ...
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Omitted variable bias in ordered logistic regression query
Including too few variables in OLS regression means that the coefficient estimates can be biased, e.g. if we aren't controlling for a variable in a model that should be there, it is instead captured ...
user207543
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Omitted variable problem
I'm studying the cases in which the endogeneity problem arises in OLS regression.
Suppose we have the following population equation:
$y=\beta_0 +\beta_1 x_1 + ... + \beta_k x_k + \gamma q + \epsilon$
...
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My instrument (z) only affects y through x, but y affects z directly. Is my instrument valid?
I'm running a regression model to test whether unionisation rates have an impact on wages.
I've introduced an instrumental variable: public support for unions. As far as I can tell, this instrument ...
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Omitted variables problem
I'm studying the omitted variables problem.
My model is:
$E[y|x_1,x_2,...,x_k,q]=\beta_0+\beta_1x_1+...+\beta_k x_k + \gamma q$
From the first equation, I write the population model as
$y= \beta_0+\...
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2
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Is omitted variable bias possible with a perfectly correlated dependent and independent variable?
Suppose $X$ and $Y$ are perfectly correlated, and we fit a model $Y=a+bX+\epsilon$. Is it possible that there would be omitted variable bias in this situation?
Intuitively, I think so, but I'm ...
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control "for post-treatment" variables vs omitted variable bias
in chapter 9 of gelman's data analysis using regression and multilevel/hierarchical models, page 170 presents a simple example on the bias of an omitted variable $x$ from a regression of an outcome $y$...
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Does omitted variable bias affects coefficients for those variables that are not correlated with the error term? (When their is one variable that is)
Does omitted variable bias affects coefficients for those variables that are not correlated with the error term? (When there is one variable that is.) I found two answers, but they appear to be ...
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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 ...
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Instrument validity: does a positive and significant coefficient on Z in a regression of Y on X and Z pose a problem?
I have an initial regression of Y on X and Z. Both of my coefficients on X and Z are non-zero and strongly statistically significant. X and Z are correlated but I am told collinearity shouldn't be an ...
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Endogeneity coming from omitted variable vs measurement error
Can someone explain more clearly what is a measurement error and how is it different from omitted variable. I know the theoretical implications, but I don't really know how to tell which problem I'm ...
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Can I use DiD for a RDD design? (treatment determined by threshold)
is it possible to apply a Difference-in-Differences method for a quasi-experiment that determines treatment by a threshold? All schools below a certain API rank are treated the rest is not (control). ...
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Comparing IV and OLS results to get infomation about the omitted variable correlation
Very often in seminars people compare the (biased because of endogeneity) results of their OLS estimation with those (unbiased) from an IV strategy estimation. Assuming everything is ok with the IV ...
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Omitted Variables Bias and time-invariant variables
In empirical research, when working with panel data sets, it is common to include time fixed effects (e.g., year dummies) into your regression model to account for unobserved heterogeneity across time,...
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Variance of linear regression model with omitted variable bias
Suppose we have the following data generation process:
\begin{align*}
U &= N_{U}\\
X_{1} &= \alpha_{1}U + N_{1}\\
X_{2} &= \alpha_{2}U + N_{2}\\
X_{3} &= \alpha_{3}U + ...
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Should I include the lagged variable in the Pearson correlation?
I currently using a random effects model for my panel data and my regression is as follows:
Beta(t) = constant + Env Score(t-1) + Controls(t) + i.Industry + error
The main independent variable Env....
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VAR model variable selection
I'm required to use two time series models in my exam project.
I want to use a stock price of an energy company, and then explain it first using ARIMA, and then adding other variables and using VAR.
...
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Question about regression and deriving omitted variables
usually when I see derivations of ommited variable bias, I see something of the sort:
from y=xb + $\eta$, and looking at the for formula for the slope estimate:
$cov(x,y)$$/var(x)$
$cov(x,xb+\eta )$$/...
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Are coefficients that are zero omitted variable bias?
If a regression coefficient is essentially zero, doesn't that imply that there is (massive) omitted variable bias? That is, the change must then exist in the error term.
The classic definition of OVB ...
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