Testbank for Applied Econometric Time Series 4th Edition by Walter Enders – Complete Solutions Manual & Answers

Applied Econometric Time Series (4th Edi on,
2014) – Solu ons Manual – by Enders
In the equa on, y=β_0+β_1 x_1+β_2 x_2+u, β_2 is a(n) _____.

a. independent variable

b. dependent variable

c. slope parameter

d. intercept parameter - answer-c. slope parameter



Consider the following regression equa on: y=β_1+β_2 x_1+β_2 x_2+u. What does β1 imply?

a.β_1 measures the ceteris paribus effect of x_1on x_2.

b. β_1 measures the ceteris paribus effect of y on x_1.

c. β_1 measures the ceteris paribus effect of x_1on y.

d. β_1 measures the ceteris paribus effect of x_1on u. - answer-c. β_1 measures the ceteris
paribus effect of x_1on y.



If the explained sum of squares is 35 and the total sum of squares is 49, what is the residual sum
of squares?

a. 10

b. 12

c. 18

d. 14 - answer-d. 14



Which of the following is true of R2?

a. R2 is also called the standard error of regression.



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,b. A low R2 indicates that the Ordinary Least Squares line fits the data well.

c. R2 usually decreases with an increase in the number of independent variables in a regression.

d. R2 shows what percentage of the total varia on in the dependent variable, Y, is explained by
the explanatory variables. - answer-d. R2 shows what percentage of the total varia on in the
dependent variable, Y, is explained by the explanatory variables.



The value of R2 always _____.

a. lies below 0

b. lies above 1

c. lies between 0 and 1

d. lies between 1 and 1.5 - answer-c. lies between 0 and 1



If an independent variable in a mul ple linear regression model is an exact linear combina on of
other independent variables, the model suffers from the problem of _____.

a. perfect collinearity

b. homoskedas city

c. heteroskedas cty

d. omi ed variable bias - answer-a. perfect collinearity



The assump on that there are no exact linear rela onships among the independent variables in
a mul ple linear regression model fails if _____, where n is the sample size and k is the number
of parameters.

a. n>2

b. n=k+1

c. n>k

d. n<k+1 - answer-d. n<k+1



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, Exclusion of a relevant variable from a mul ple linear regression model leads to the problem of
_____.

a. misspecifica on of the model

b. mul collinearity

c. perfect collinearity

d. homoskedas city - answer-a. misspecifica on of the model



Suppose the variable x2 has been omi ed from the following regression equa on, y=β_0+β_1
x_1+β_2 x_2+u. (β_1 ) ̃ is the es mator obtained when x2 is omi ed from the equa on. The bias
in (β_1 ) ̃is posi ve if _____.

a. β_2 >0 and x 1 and x 2 are posi vely correlated

b. β_2 <0 and x 1 and x 2 are posi vely correlated

c. β_2 >0 and x 1 and x 2 are nega vely correlated

d. β_2 = 0 and x 1 and x 2 are nega vely correlated - answer-a. β_2 >0 and x 1 and x 2 are
posi vely correlated



Suppose the variable x2 has been omi ed from the following regression equa on, y=β_0+β_1
x_1+β_2 x_2+u. (β_1 ) ̃ is the es mator obtained when x2 is omi ed from the equa on. The bias
in (β_1 ) ̃ is nega ve if _____.

a. β_2 >0 and x 1 and x 2 are posi vely correlated

b. β_2 <0 and x 1 and x 2 are posi vely correlated

c. β_2 =0 and x 1 and x 2 are nega vely correlated

d. β_2 =0 and x 1 and x 2 are nega vely correlated - answer-b. β_2 <0 and x 1 and x 2 are
posi vely correlated



Suppose the variable x2 has been omi ed from the following regression equa on, y=β_0+β_1
x_1+β_2 x_2+u. (β_1 ) ̃ is the es mator obtained when x2 is omi ed from the equa on. If E((β_

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