If you included both time and entity fixed effects in the regression model which includes a constant, then:O a. you have perfect multicollinearity.O b. you can exclude the constant and one entity binary variable to estimate the model.you can exclude the constant and one time binary variable to estimate the model.O d. you can exclude one time binary variable and one entity binary variable to estimate the model.O e. All of the above.Of.None of the above.

Answered: If you included both time and entity…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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10. Suppose in our model, lifespans increased due to a productivity in- crease. What would likely happen to hours worked and leisure over the lifetime of a represent ative consumer. (a) Let's consider one of two common ways of implementing this change. Suppose the increase raised both lifespans and the amount of years consumers are healthy enough to work by the same amount, how would that likely affect the measured numbered of hours work by prime aged adults within a given year? What would likely hap- pen to the retirement age? (b) Let's consider the other way to implement it. Suppose the change in lifetimes came about changes in technology that delayed death but did not extend the amount of years consumers are healthy enough to work. What would likely happen to hours worked within a year? What would happen to the retirement age? (c) In the US, the generalized stylized fact is that the actual age of retirement is not increasing. Nor are hours worked per week. Why are both of the above…
Please answer all three sub-sections of this question
4- The manager of Collins Import Autos believes the number of cars sold in a day(Q) depends on two factors: (1) the number of hours the dealership is open (H) and (2) the number of salespersons working that day (S ). After collecting data for two months (53 days), the manager estimates the following log-linear model: Q = aHbSc ----- a. Explain how to transform this log-linear model into a linear form that can be estimated using multiple regression analysis. b. How do you interpret coefficients b and c? If the dealership increases the number of salespersons by 20 percent, what will be the percentage increase in daily sales? c. Test the overall model for statistical significance at the 5 percent significance level.
1. Suppose output (Q) is related to labor (L) and capital (K) in the following nonlinear way: Q = albKc When taking log to this equation, it is transformed into a linear LnQ = Ina + b In(L) + c Ln (K) One hundred twenty-three observations are used to obtain the following regression results: Dependant Variable: Observations: Variable Intercept L K Q 123 5.5215 Parameter Standard Estimate error 0.650 R-square 0.350 0.7547 0.9750 0.2950 0.1450 F-ratio 184.56 t-ratio 5.66 2.20 2.41 p-value on F 0.00001 p-value 0.0001 0.0295 0.0173 a. Write the regression equation based on the output either in the transformed linear form or the original non-linear form.
5. Confounding variables and multiple regressions Suppose you are interested in studying the effect of square footage on the price of houses. You propose the following regression of housing prices on square footage: price - Bo + P sqft + u where price the price of the house. sqft = the square footage of the house residual Failing to account for the number of bathrooms in a house, which may influence housing prices and may be positively correlated with square footage, could lead to estimate of the effect . This issue of confounding variables would likely bias the estimate of
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9. The (incomplete) table below shows the x and y values for three data points. It also shows the estimated coefficients, predicted y values and residuals for two potential regression lines (A and B). Fill in the missing values in the table below. obs 1 W23 2 3 100 90 80 70 60 50 40 30 20 10 0 y 60 20 0 1 X 3 2 8 Using the values you just calculated, plot the three data points and the two estimated regression equations in the graph below. Be sure to label everything clearly VA 40 50 3 aA A BA Regression A UA 4 -30 -15 5 10 YB 6 40 60 B Regression B A Вв 7 UB 25 -20 15 8 20 9 10
Question 1 For the estimated regression equation ŷ = 15 + 6x1 + 5x2 + 4x1x2, a unit increase in x2, while keeping x1 constant, increases the value of y on average by: a. an amount that depends on the value of x1. O b. 5. O c. 9. O d. 20.
What is the impact of autocorrelation on the likelihood of rejecting the null hypothesis of no statistical significance of the model estimates? (a) It increases the likelihood of rejecting the null hypothesis. (b) It decreases the likelihood of rejecting the null hypothesis. (c) It has no effect on the likelihood of rejecting the null hypothesis. (d) It makes the estimates biased and increases the likelihood of rejecting the null hypothesis. (e) It makes the estimates unbiased and decreases the likelihood of rejecting the null hypothesis.

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