A key assumption for the identification of the ceteris paribus effect in a multiple linear regression model, Yt = XtB + Et Homoskedasticity: Zero unconditional mean: Zero conditional mean: Simple random samplin: V (et) = 0² E(et |Xt) = 0 E(et | Xt) = 0 Cov(Et, Es) = 0

A key assumption for the identification of the ceteris paribus effect in a multiple linear regression model, Yt = XtB + Et Homoskedasticity: Zero unconditional mean: Zero conditional mean: Simple random samplin: V (et) = 0² E(et |Xt) = 0 E(et | Xt) = 0 Cov(Et, Es) = 0

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

Consider a simple linear regression model, y = Bo + Bix+u.. What does the zero conditional mean assumption imply? The estimated average value of 31 is zero The expected value of the explained variable, y, is zero, regardless of what the value of the explanatory variable, x, is The expected value of the error term, u, is zero, regardless of what the value of the explanatory variable, x, is The estimated average value of 30 is zero
a) The R? should not be used to choose the best econometric model specification in multiple regression models. True or False
When Y is regressed on X, B, > 0, sx+ 0, sy # 0, and the fraction of the variation in Y explained by the least-squares regression of Y on X does not equal 1. In this example, the sum of the squares of the deviations of the actual Y-values from their mean would definitely be greater than the sum of the squares of the deviations of the predicted Y-values from their mean. 1 A) B) true false 4. When Y is regressed on X, the sample slope is negative, sx + 0, sy + 0, and Y; = Y; for every observation. In this example, the sum of the squares of the deviations of the predicted Y-values from their mean would definitely be less than the sum of the squares of the deviations of the actual Y-values from their mean. A) В) true false 3.
46) The following model is a simplified version of the multiple regression model used by Biddle and Hamermesh (1990) to study the trade-off between time spent sleeping and working and to look at other factors affecting sleep: sleep = Bo + B₁totwrk + ß₂educ + ß3age +u, where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. (i) If adults trade off sleep for work, what is the sign of f₁? (ii) What signs do you think ₂ and 3 will have? (iii) Using the data in SLEEP75, the estimated equation is sleep = 6,241.15 + 0.211totwrk + 9.22educ + 1.67age n = 211, R² = 0.981 If someone works five more hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ. (v) Would you say totwrk, educ, and age explain much of the variation in sleep? What other factors might affect the time spent sleeping? Are these likely to be correlated with totwrk?
In regression analysis, a common metric used in assessing the quality of the model being used to fit the data is known as the R-squared coefficient. Explain the R-squared coefficient. What is the difference between the R-squared and adjusted R-squared coefficients?
Using a sample of recent university graduates, you estimate a simple linear regression using initial annual salary as the dependent variable and the graduate's weighted average mark (WAM) as the explanatory variable. If the regression model has an estimated intercept of 3200 and an estimated slope coefficient of 550, what is the predicted starting salary of a student with a WAM of 69? Answer:
Question 6 Are rental rates influenced by the student population in a college town? Let rent be the average monthly rent paid on rental units in a college town in the United States. Let pop denote the total city population An econometric model to test for this relationship is: = rent Bo+Bipop+u Esimate regression equation is as follows rent = 412+ 0.00013pop (18.17) (0.00015) The numeric value in the parenthesis represents standard error. (For example, Var(B1)= 0.00015) Calculate Tstatistics of B1 Round to two decimal places if needed Question 7 4 pts 4 pts
Consider the polynomial regression model Yį = ßo + B₁X; + ß₂X² + uį, which of the following is true? This is a second-degree polynomial regression model This is a simple regression model because there is only one explanatory variable OLS is problematic in estimating the parameters due to the multicollinearity problem All the parameters (B's) must have the same sign