Questions tagged [omitted-variable-bias]
The omitted-variable-bias tag has no usage guidance.
15
questions
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22 views
Homoskedasticity
Suppose we have the following: $$Y=X_1'\beta_1+X_2'\beta_2+e$$ $$E[e|X_1,X_2]=0$$ $$E[e^2|X_1,X_2]=\sigma^2$$ $$E[X_2|X_1]=\Gamma X_1$$
We will assume $\Gamma\neq0$ and $\beta_1$ is of interest. We ...
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19 views
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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2answers
24 views
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 ...
2
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1answer
87 views
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 {(π , π , π ): π = 1, ... , π}. You want to estimate the causal effect
of π1 on π. You first run a regression π = πΌ0 + πΌ1πi + π’i and get the following ...
5
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1answer
109 views
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 ...
0
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1answer
21 views
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 ...
7
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1answer
285 views
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
$$
Y = \beta_{0} + \beta_{1}X_{1} + \...
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Variable significance very sensitive to specification of non-correlated second variable
IΒ΄m doing research on a political science topic and my models leave me behind with a big questionmark at this point. I have a dataset containing 79 observations on a number of variables and trying and ...
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13 views
Question about regression and deriving omitted variables
usually when I see derivations of ommited variable bias, I see something of the sort:
from y=xb + $\eta$, and looking at the for formula for the slope estimate:
$cov(x,y)$$/var(x)$
$cov(x,xb+\eta )$$/...
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0answers
40 views
Using an IV when there is more than one omitted variable
I am trying to estimate the following model:
$$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 (...
26
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3answers
2k views
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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1answer
75 views
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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32 views
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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50 views
omitted variable bias
I have a question about omitted variables and there is a big problem about omitted issues for me. Please can you help me.
Suppose that the true model is
$π_{t} = \beta_{0} + \beta_{1}π_{1,t} + \...
6
votes
2answers
612 views
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 ...
