Questions
Single choice
Consider the following simple regression model: y = β0 + β1x1 + u (1) and the following multiple regression model: y = β0 + β1x1 + β2x2 + u (2), where x1 is the variable of primary interest to explain y. Which of the following statements is correct?
Options
A.All of the above.
B.When drawing ceteris paribus conclusions about how x1 affects y, with model (1), we must assume that x2, contained in u, is uncorrelated with x1.
C.If x2, contained in u, is correlated with x1, the OLS estimator of β1 in model (1) will be biased.
D.When drawing ceteris paribus conclusions about how x1 affects y, with model (2), because x2 is explicitly in the model equation, we are able to measure the effect of x1 on y, holding x2 fixed—assuming all other factors contained in u, are uncorrelated with x1 and x2.
View Explanation
Verified Answer
Please login to view
Step-by-Step Analysis
Let’s parse the question carefully and evaluate each statement in turn, keeping in mind what each model implies about ceteris paribus reasoning and omitted variable bias.
Option 2: 'When drawing ceteris paribus conclusions about how x1 affects y, with model (1), we must assume that x2, contained in u, is uncorrelated with x1.' This is accurate because in model (1) the term u contains β2 x2 and the error term. If x2 is correlated with x1, an endogeneity problem arises and E[x1 u......Login to view full explanationLog in for full answers
We've collected over 50,000 authentic exam questions and detailed explanations from around the globe. Log in now and get instant access to the answers!
Similar Questions
Question45 Consider the following regression of the logarithm of wages on age and an IQ score for a sample of US households: The estimated marginal effect of age on wages would suffer from omitted variables bias if there exists an additional excluded variable, say for instance productivity, which has an effect on wages there exists an additional excluded variable, say for instance productivity, which is correlated with age All of the above Any of the above None of the above ResetMaximum marks: 1 Flag question undefined
Consider the following general model specification: 𝑌 = 𝑓 ( 𝑋 1 , 𝑋 2 , 𝑋 3 𝑋 4 , 𝑋 5 , 𝑋 6 , 𝑋 7 , 𝑋 8 ) . Where the 9 variables are: Y = Test score of the mid-term exam X1 = Time spent studying for the exam X2 = Degree of course difficulty (measured by an index #) X3 = Class size (number of students) X4 = Experience/knowledge of the teacher X5 = Student’s college experience (college credits previously earned) X6 = Quantity of the student’s outside (non-academic) activities X7 = Level of the student’s determination to succeed (measured by an index #) X8 = Dummy (=1 if the course is face-to-face; 0 otherwise (online)) For each of the 2 cases of omitted variables below, determine the sign (positive/negative) of the bias – you must complete columns 3, 4, and 5 in the table below. Case OM IN 𝛽 𝑂 𝑀 𝛼 1 − ℎ 𝑎 𝑡 BIAS 1 X5 X6 Sign is [ Select ] negative positive Sign is [ Select ] negative positive Bias is [ Select ] negative positive 2 X4 X1 Sign is [ Select ] negative positive Sign is [ Select ] negative positive Bias is [ Select ] negative positive
The bias caused by leaving a variable out of an equation is called
Consider the following specification "situation:" In an equation explaining student GPAs at HUTB, the bias imparted to the coefficient of hours devoted to study (per course) of omitting the number of campus social organizations is
More Practical Tools for Students Powered by AI Study Helper
Making Your Study Simpler
Join us and instantly unlock extensive past papers & exclusive solutions to get a head start on your studies!