Give an example of omitted variable bias in a multiple linear regression model. Explain how you...

Give an example of omitted variable bias in a multiple linear regression model. Explain how you would figure out the probable direction of the bias even without collecting data on this omitted variable. [3 marks]

Solutions

Expert Solution

Suppose you want to find out what factors determine the price of homes in your area. What could you set up to monitor all the variables? You decide to run a multiple regression to estimate the price of houses. For this, you thought of all the factors you want to include in your regression. You included variables like number of rooms in the house, the number of bathrooms, whether the house is furnished or not, and how old the house is. However, you forgot to include a very important variable – the size of the house in square feet. Your regression is likely to give you biased results. Think it over, and the reason is simple! Two houses with exactly similar values of the variables you have taken can have drastically different prices if the size of the house (or say the size of the room) is different. In missing this important variable, your regression suffers from Omitted Variable Bias.

The problem of omitted variables occurs due to misspecification of a linear regression model, which may be because either the effect of the omitted variable on the dependent variable is unknown or because the data is not available. This forces you to omit that variable from your regression, which results in over-estimating (upward bias) or under-estimating (downward) the effect of one of more other explanatory variables.

Two conditions must hold for omitted variable bias to exist.

a) The omitted variable must be correlated with the dependent variable.

b) The omitted variable must be correlated with one or more other explanatory/ independent variables.

In the example above, the size of the house in square feet is correlated with the price of the house as well as the number of rooms. Hence, omitting the size of house variable results in omitted variable bias.

milcah answered 10 months ago

Next > < Previous

Related Solutions

Multiple Linear Regression - Omitted Variable bias. Can someone provide me with an intuitive explanation of...

Multiple Linear Regression - Omitted Variable bias. Can someone provide me with an intuitive explanation of ommitted variable bias.

Give an example of an endogenous variable in a multiple regression model. Explain

How do you fix omitted variable bias? Please give a simple, and elaborate response. An example...

How do you fix omitted variable bias? Please give a simple, and elaborate response. An example in addition will help. Thank you!

for stat students, model ( linear regression, multiple regression,factorial experiments,liner model) For one statistical method, give...

for stat students, model ( linear regression, multiple regression,factorial experiments,liner model) For one statistical method, give at least three reasons why the underlying statistical model is important. three reasons for each one

When we estimate a linear multiple regression model (including a linear simple regression model), it appears...

When we estimate a linear multiple regression model (including a linear simple regression model), it appears that the calculation of the coefficient of determination, R2, for this model can be accomplished by using the squared sample correlation coefficient between the original values and the predicted values of the dependent variable of this model. Is this statement true? If yes, why? If not, why not? Please use either matrix algebra or algebra to support your reasoning.

Explain why you choose multiple regression with dummy variables but not linear trend model and why...

Explain why you choose multiple regression with dummy variables but not linear trend model and why do you believe this technique is appropriate to forecast your data?

Discuss the underlying assumptions of a simple linear regression model; multiple regression model; and polynomial regression.

Omitted variable bias: a. exists if the omitted variable is correlated with the included regressor but is not a determinant of the dependent variable. b. exists if the omitted variable is correlated

Omitted variable bias:a. exists if the omitted variable is correlated with the included regressor but is not a determinant of the dependent variable.b. exists if the omitted variable is correlated with the included regressor and is a determinant of the dependent variable.c. will always be present as long as the regression R2 < 1.d. is always there but is negligible in almost all economic examples.

4. How should a researcher specify a multiple linear regression model? In answering this question, you...

4. How should a researcher specify a multiple linear regression model? In answering this question, you should be sure to draw on your overall knowledge of econometrics in order to consider not only how a researcher should specify an initial model, but also how a researcher should assess the performance of an initial model and thereby iterate towards a final model.

Can you use linear regression framework to model non-linear relationships? Explain how.

Subjects