Question

Does the omitted variable bias still exist if the omitted variable is not related to any...

Does the omitted variable bias still exist if the omitted variable is not related to any explanatory variable but is impacting the outcome variable

Homework Answers

Answer #1

yes, because In statistics, omitted-variable bias (OVB) occurs when a statistical model incorrectly leaves out one or more relevant variables. The bias results in the model attributing the effect of the missing variables to the estimated effects of the included variables.

OVB is the bias that appears in the estimates of parameters in a regression analysis when the assumed specification is incorrect in that it omits an independent variable that is correlated with both the dependent variable and one or more of the included independent variables.

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
How can omitted variable bias be addressed?
How can omitted variable bias be addressed?
explain how omitted variable bias can be detected (if it can):
explain how omitted variable bias can be detected (if it can):
In which of the following situations would one be most comfortable that omitted variable bias is...
In which of the following situations would one be most comfortable that omitted variable bias is not a concern? a. When all of the variables in a model are statistically significant b. When an estimated model has a high R-squared. c. When an estimated model has a high adjusted R-squared. d. When no one in a room full of experts can think of a variable that was not included in the model that could be causing omitted variable bias
Can the use of proxy variables solve the omitted variable bias in cases where unobservable characteristics...
Can the use of proxy variables solve the omitted variable bias in cases where unobservable characteristics might be important?
Why we should not worry about omitted variable bias in forecasting using time-series analysis?
Why we should not worry about omitted variable bias in forecasting using time-series analysis?
(a) In regression analysis, often the researcher will encounter issues of omitted variable bias (OVB) or...
(a) In regression analysis, often the researcher will encounter issues of omitted variable bias (OVB) or their included variables are too closely related (multicollinearity). In you own words, (i) explain what is meant by OVB? (ii) what is multicollinearity (iii) How do these problems lead to type1/type 2 errors? (b) In your own words, describe your understanding of linear regression analysis. What is the causal fallacy? (c) How is the model fit measured? In your answer describe both the R-squared...
Regression: It DOES NOT matter which is the explanatory/independent variable and which is the response/dependent variable...
Regression: It DOES NOT matter which is the explanatory/independent variable and which is the response/dependent variable for ____, but it DOES matter for ____.
Find all horizontal and vertical asymptotes (if any). (If an answer does not exist, enter DNE....
Find all horizontal and vertical asymptotes (if any). (If an answer does not exist, enter DNE. Enter your answers as a comma-separated list of equations.) r(x) = 5x + 1 4x2 + 1 vertical asymptote(s) horizontal asymptote
1. Suppose the variable x2 has been omitted from the following regression equation, y = β0...
1. Suppose the variable x2 has been omitted from the following regression equation, y = β0 + β1x1 +β2x2 + u. b1 is the estimator obtained when x2 is omitted from the equation. The bias in b1 is positive if A. β2<0 and x1 and x2 are positive correlated B. β2=0 and x1 and x2 are negative correlated C. β2>0 and x1 and x2 are negative correlated D. β2>0 and x1 and x2 are positive correlated 2. Suppose the true...
Next, suppose you add the unemployment rate, as a variable, to the regression model above and...
Next, suppose you add the unemployment rate, as a variable, to the regression model above and obtain the following estimates: SALES = 5.987-0.876(PRICE)-0.045(UNEMPLOYMENT RATE) B) What does this imply about the omission of the unemployment rate from the model above?  In other words, given the coefficient on PRICE went from -1.034 to -0.876, what was the nature of the omitted variable bias?