Questions tagged [omitted-variable-bias]
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17
questions
2
votes
1answer
7 views
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 ...
1
vote
1answer
13 views
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 $X_2$ ...
2
votes
1answer
29 views
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 ...
0
votes
0answers
41 views
Homoskedasticity
From Bruce Hansen's book, chapter 2, ex. 2.22
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 $\...
0
votes
0answers
43 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.
...
1
vote
2answers
29 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
votes
1answer
118 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 {(𝑌 , 𝑋 , 𝑋 ): 𝑖 = 1, ... , 𝑛}. You want to estimate the causal effect
of 𝑋1 on 𝑌. You first run a regression 𝑌 = 𝛼0 + 𝛼1𝑋i + 𝑢i and get the following ...
6
votes
1answer
404 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
votes
1answer
65 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
votes
1answer
415 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
$$
Y = \beta_{0} + \beta_{1}X_{1} + \...
0
votes
0answers
9 views
Variable significance very sensitive to specification of non-correlated second variable
I´m doing research on a political science topic and my models leave me behind with a big questionmark at this point. I have a dataset containing 79 observations on a number of variables and trying and ...
0
votes
0answers
13 views
Question about regression and deriving omitted variables
usually when I see derivations of ommited variable bias, I see something of the sort:
from y=xb + $\eta$, and looking at the for formula for the slope estimate:
$cov(x,y)$$/var(x)$
$cov(x,xb+\eta )$$/...
2
votes
0answers
43 views
Using an IV when there is more than one omitted variable
I am trying to estimate the following model:
$$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 (...
25
votes
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 ...
2
votes
1answer
227 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?
0
votes
0answers
42 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 ...
6
votes
2answers
1k 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 ...
