Questions tagged [omitted-variable-bias]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
0 votes
0 answers
36 views

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). ...
3 votes
2 answers
105 views

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 ...
  • 46.5k
1 vote
1 answer
55 views

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+\...
  • 87
0 votes
0 answers
11 views

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 ...
2 votes
1 answer
72 views

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 ...
1 vote
1 answer
26 views

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 $y$...
0 votes
0 answers
64 views

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,...
  • 150
3 votes
0 answers
63 views

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 ...
  • 272
0 votes
0 answers
92 views

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 + ...
  • 306
2 votes
2 answers
243 views

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 ...
  • 21
0 votes
0 answers
32 views

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....
0 votes
0 answers
9 views

Distinguishing between effects of two variables on y

Assume that we have the linear regression model: $$ y=\beta+\beta_{1}x+\epsilon $$ We estimate the model by OLS, and we get $\hat{\beta}_{1},$ However, there is another variable $z,$ which is both ...
1 vote
1 answer
36 views

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 ...
0 votes
0 answers
42 views

Can you use GMM to overcome time-variant omitted variable bias resulting from FE?

I am looking at a FE model on the effects of R&D expenditure on labour productivity but not sure how to address the possible endogeneity resulting from time varying omitted variable bias. I cannot ...
1 vote
1 answer
47 views

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 ...
3 votes
0 answers
172 views

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 ...
  • 591
1 vote
0 answers
23 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 ...
  • 59
3 votes
2 answers
233 views

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 ...
  • 31
1 vote
1 answer
73 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 ...
  • 11
2 votes
1 answer
40 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
1 answer
141 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 ...
  • 23
0 votes
0 answers
205 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
2 answers
101 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 ...
  • 474
2 votes
2 answers
470 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 ${(π‘Œ_i , 𝑋_{1,i} , 𝑋_{2,i} ): 𝑖 = 1, ... , 𝑛}$. You want to estimate the causal effect of $𝑋_1$ on $π‘Œ$. You first run a regression $π‘Œ_i = 𝛼_0 + 𝛼_1𝑋_{1,i} +...
  • 21
7 votes
1 answer
2k 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 ...
  • 247
0 votes
1 answer
467 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 ...
  • 15
9 votes
1 answer
1k 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} + \...
  • 235
0 votes
0 answers
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)$$/var(x)$ $cov(x,xb+\eta )$$/...
  • 591
2 votes
0 answers
54 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 (...
  • 359
1 vote
1 answer
265 views

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 ...
user avatar
25 votes
3 answers
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 ...
  • 393
3 votes
1 answer
1k 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?
  • 31
0 votes
0 answers
151 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 ...
  • 109
9 votes
2 answers
4k 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 ...
2 votes
0 answers
120 views

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 ...
  • 171