introductory econometrics test bank
TRANSCRIPT
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Test Bank –Introductory Econometrics: A Modern Approach, 5th Edition by
Jefrey M. oo!drid"e
ith #E$%E&T '()*TI(+ AAI)AB)E (% A)) &-A#TE$'
&hapter
1. Econometrics is the branch of economics that _____.
a. studies the behavior of individual economic agents in making economic decisions
b. develops and uses statistical methods for estimating economic relationships
c. deals with the performance, structure, behavior, and decision-making of
an economy as a whole
d. applies mathematical methods to represent economic theories and solve
economic problems.
Answer: b
i!culty: Easy"loom#s: $nowledge
A-%ead: &hat is Econometrics'
"()*+:
eedback: Econometrics is the branch of economics that develops and uses
statistical methods for estimating economic relationships.
/. 0oneperimental data is called _____.
a. cross-sectional data
b. time series data
c. observational data
d. panel data
Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: &hat is Econometrics'
"()*+:
eedback:
2. &hich of the following is true of eperimental data'
a. Eperimental data are collected in laboratory environments in the naturalsciences.
b. Eperimental data cannot be collected in a controlled environment.
c. Eperimental data is sometimes called observational data.
d. Eperimental data is sometimes called retrospective data.
Answer: a
i!culty: Easy
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"loom#s: $nowledge
A-%ead: &hat is Econometrics'
"()*+:
eedback:
3. An empirical analysis relies on _____to test a theory.a. common sense
b. ethical considerations
c. data
d. customs and conventions
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: )teps in Empirical Economic Analysis
"()*+:
eedback: An empirical analysis relies on data to test a theory.
4. 5he term 6u’ in an econometric model is usually referred to as the _____.
a. error term
b. parameter
c. hypothesis
d. dependent variable
Answer: a
i!culty: Easy
"loom#s: $nowledgeA-%ead: )teps in Empirical Economic Analysis
"()*+:
eedback: 5he term u in an econometric model is called the error term or
disturbance term.
7. 5he parameters of an econometric model _____.
a. include all unobserved factors a8ecting the variable being studied
b. describe the strength of the relationship between the variable under study and
the factors a8ecting it
c. refer to the eplanatory variables included in the modeld. refer to the predictions that can be made using the model
Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: )teps in Empirical Economic Analysis
"()*+:
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eedback: 5he parameters of an econometric model describe the direction and
strength of the relationship between the variable under study and the factors
a8ecting it.
9. &hich of the following is the rst step in empirical economic analysis'
a. ;ollection of datab. )tatement of hypotheses
c. )pecication of an econometric model
d. 5esting of hypotheses
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: )teps in Empirical Economic Analysis
"()*+:
eedback: 5he rst step in empirical economic analysis is the specication of the
econometric model.
_____.
a. cross-sectional data set
b. longitudinal data set
c. time series data set
d. eperimental data set
Answer: ai!culty: Easy
"loom#s: $nowledge
A-%ead: 5he )tructure of Economic ata
"()*+:
eedback: A data set that consists of a sample of individuals, households, rms,
cities, states, countries, or a variety of other units, taken at a given point in time, is
called a cross-sectional data set.
?. ata on the income of law graduates collected at di8erent times during the same
year is_____.a. panel data
b. eperimental data
c. time series data
d. cross-sectional data
Answer: d
i!culty: Easy
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"loom#s: Application
A-%ead: 5he )tructure of Economic ata
"()*+: Analytic
eedback: A data set that consists of a sample of individuals, households, rms,
cities, states, countries, or a variety of other units, taken at a given point in time, is
called a cross-sectional data set. 5herefore, data on the income of law graduates ona particular year are eamples of cross-sectional data.
1@. A data set that consists of observations on a variable or several variables over
time is called a _____ data set.
a. binary
b. cross-sectional
c. time series
d. eperimental
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he )tructure of Economic ata
"()*+:
eedback: A time-series data set consists of observations on a variable or several
variables over time.
11. &hich of the following is an eample of time series data'
a. ata on the unemployment rates in di8erent parts of a country during a year.
b. ata on the consumption of wheat by /@@ households during a year.
c. ata on the gross domestic product of a country over a period of 1@ years.d. ata on the number of vacancies in various departments of an organiation on a
particular month.
Answer: c
i!culty: Easy
"loom#s: Application
A-%ead: 5he )tructure of Economic ata
"()*+: Analytic
eedback: A time-series data set consists of observations on a variable or several
variables overtime. 5herefore, data on the gross domestic product of a country over a period of 1@
years is an eample of time series data.
1/. &hich of the following refers to panel data'
a. ata on the unemployment rate in a country over a 4-year period
b. ata on the birth rate, death rate and population growth rate in developing
countries over a 1@-year period.
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c. ata on the income of 4 members of a family on a particular year.
d. ata on the price of a company#s share during a year.
Answer: b
i!culty: Easy
"loom#s: ApplicationA-%ead: 5he )tructure of Economic ata
"()*+: Analytic
eedback: A panel data set consists of a time series for each cross-sectional
member in the data set. 5herefore, data on the birth rate, death rate and infant
mortality rate in developing countries over a 1@-year period refers to panel data.
12. &hich of the following is a di8erence between panel and pooled cross-sectional
data'
a. A panel data set consists of data on di8erent cross-sectional units over a given
period of time while a pooled data set consists of data on the same cross-sectional
units over a given period of time.
b. A panel data set consists of data on the same cross-sectional units over a given
period of time while a pooled data set consists of data on di8erent cross-sectional
units over a given period of time
c. A panel data consists of data on a single variable measured at a given point in
time while a pooled data set consists of data on the same cross-sectional units over
a given period of time.
d. A panel data set consists of data on a single variable measured at a given point in
time while a pooled data set consists of data on more than one variable at a given
point in time.
Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he )tructure of Economic ata
"()*+:
eedback: A panel data set consists of data on the same cross-sectional units over a
given period of time while a pooled data set consists of data on the same cross-
sectional units over a given period of time.
13. _____ has a causal e8ect on _____.a. BncomeC unemployment
b. %eightC health
c. BncomeC consumption
d. AgeC wage
Answer: c
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i!culty: Doderate
"loom#s: Application
A-%ead: ;ausality and the 0otion of ;eteris *aribus in Econometric Analysis
"()*+: Analytic
eedback: Bncome has a causal e8ect on consumption because an increase in
income leads to an increase in consumption.
14. &hich of the following is true'
a. A variable has a causal e8ect on another variable if both variables increase or
decrease simultaneously.
b. 5he notion of 6ceteris paribus# plays an important role in causal analysis.
c. i!culty in inferring causality disappears when studying data at fairly high levels
of aggregation.
d. 5he problem of inferring causality arises if eperimental data is used for analysis.
Answer: b
i!culty: Doderate
"loom#s: $nowledge
A-%ead: ;ausality and the 0otion of ;eteris *aribus in Econometric Analysis
"()*+:
eedback: 5he notion of 6ceteris paribus# plays an important role in causal analysis.
17. Eperimental data are sometimes called retrospective data.
Answer: alse
i!culty: Easy
"loom#s: $nowledgeA-%ead: &hat is Econometrics'
"()*+:
eedback: 0oneperimental data are sometimes called retrospective data.
19. An economic model consists of mathematical euations that describe various
relationships between economic variables.
Answer: 5rue
i!culty: Easy
"loom#s: $nowledgeA-%ead: )teps in Empirical Economic Analysis
"()*+:
eedback: An economic model consists of mathematical euations that describe
various relationships between economic variables.
1
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Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he )tructure of Economic ata
"()*+:eedback: A time series data set consists of observations on a variable or several
variables over
time.
1?. A time series data is also called a longitudinal data set.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he )tructure of Economic ata
"()*+:
eedback: A time series data is also called a longitudinal data set.
/@. 5he notion of ceteris paribus means Fother factors being eual.G
Answer: 5rue
i!culty: Easy
"loom#s: $nowledge
A-%ead: ;ausality and the 0otion of ;eteris *aribus in Econometric Analysis
"()*+:
eedback: 5he notion of ceteris paribus means Fother factors being eual.G
&hapter /
1. A dependent variable is also known as a=n> _____.
a. eplanatory variable
b. control variable
c. predictor variable
d. response variable
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: enition of the )imple +egression Dodel
"()*+:
eedback: A dependent variable is known as a response variable.
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/. Bf a change in variable causes a change in variable y, variable is called the
_____.
a. dependent variable
b. eplained variable
c. eplanatory variable
d. response variable
Answer: c
i!culty: Easy
"loom#s: ;omprehension
A-%ead: enition of the )imple +egression Dodel
"()*+:
eedback: Bf a change in variable causes a change in variable y, variable is
called the independent variable or the eplanatory variable.
2. Bn the euation y H β
0 I β
1 I u, β
0 is the _____.
a. dependent variable
b. independent variable
c. slope parameter
d. intercept parameter
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: enition of the )imple +egression Dodel
"()*+:
eedback: Bn the euation y H β
0 I β
1 I u, β
0 is the intercept parameter.
3. Bn the euation y H β
0 I β
1 I u, what is the estimated value of β
0 '
a. ´ y−^ β1 ´ x
b.´ y+ β
1´ x
c.
y
yi−¿́¿¿
( x i−´ x)¿
∑i=1
n
¿
¿
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d. ∑i=1
n
xy
Answer: a
i!culty: Easy"loom#s: $nowledge
A-%ead: eriving the rdinary Jeast )uares Estimates
"()*+:
eedback: 5he estimated value of β
0 is ´ y−^ β1 ´ x .
4. Bn the euation c H β0 I β1 i I u, c denotes consumption and i denotes
income. &hat is the residual for the 4th observation ifc5 HK4@@ and
ĉ5 HK394'
a. K?94b. K2@@
c. K/4
d. K4@
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: eriving the rdinary Jeast )uares Estimates
"()*+:
eedback: 5he formula for calculating the residual for the ith
observation isûi= y i− ̂y i . Bn this case, the residual is û5=c5−ĉ5 HK4@@ -K394H K/4.
7. &hat does the euation ̂y=^ β0+^ β1 x denote if the regression euation is y H L@
I L11 I u'
a. 5he eplained sum of suares
b. 5he total sum of suares
c. 5he sample regression function
d. 5he population regression function
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: eriving the rdinary Jeast )uares Estimates
"()*+:
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eedback: 5he euation ̂y=^ β0+^ β1 x denotes the sample regression function of the
given regression model.
9. ;onsider the following regression model: y H L@ I L11 I u. &hich of the following
is a property of rdinary Jeast )uare =J)> estimates of this model and theirassociated statistics'
a. 5he sum, and therefore the sample average of the J) residuals, is positive.
b. 5he sum of the J) residuals is negative.
c. 5he sample covariance between the regressors and the J) residuals is positive.
d. 5he point = ´ x , ´ y > always lies on the J) regression line.
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: *roperties of J) on Any )ample of ata"()*+:
eedback: An important property of the J) estimates is that the point = ´ x , ´ y >
always lies on the J) regression line. Bn other words, if x=´ x , the predicted value
of y is ́y .
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"()*+:
eedback: 5he eplained sum of suares is dened as ∑i=1
n
( y i− ̂y )2
?. Bf the total sum of suares =))5> in a regression euation is is /4, what is the eplained sum of suares =))E>'
a. 73
b. 47
c. 2/
d. 1<
Answer: b
i!culty: Doderate
"loom#s: Application
A-%ead: *roperties of J) on Any )ample of ata
"()*+: Analytic
eedback: 5otal sum of suares =))5> is given by the sum of eplained sum of
suares =))E> and residual sum of suares =))+>. 5herefore, in this case, ))EH in a regression analysis is 77 and the total
sum of suares =))5> is eual to ?@, what is the value of the coe!cient of
determination'
a. @.92
b. @.44
c. @./9d. 1./
Answer: c
i!culty: Doderate
"loom#s: Application
A-%ead: *roperties of J) on Any )ample of ata
"()*+: Analytic
eedback: 5he formula for calculating the coe!cient of determination is
R2=1−
SSR
SST . Bn this case, R
2=1−66
90
=0.27
11. &hich of the following is a nonlinear regression model'
a. y H L@ I L11M/ I u
b. log y H L@ I L1log Iu
c. y H 1 M =L@ I L1> I u
d. y H L@ I L1 I u
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Answer: c
i!culty: Doderate
"loom#s: ;omprehension
A-%ead: *roperties of J) on Any )ample of ata
"()*+:eedback: A regression model is nonlinear if the euation is nonlinear in the
parameters. Bn this case, yH1 M =L@ I L1> I u is nonlinear as it is nonlinear in its
parameters.
1/. &hich of the following is assumed for establishing the unbiasedness of rdinary
Jeast )uare =J)> estimates'
a. 5he error term has an epected value of 1 given any value of the eplanatory
variable.
b. 5he regression euation is linear in the eplained and eplanatory variables.
c. 5he sample outcomes on the eplanatory variable are all the same value.
d. 5he error term has the same variance given any value of the eplanatory
variable.
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: Epected Nalues and Nariances of the J) Estimators
"()*+:
eedback: 5he error u has the same variance given any value of the eplanatory
variable.
12. 5he error term in a regression euation is said to ehibit homoskedasticty if
_____.
a. it has ero conditional mean
b. it has the same variance for all values of the eplanatory variable.
c. it has the same value for all values of the eplanatory variable
d. if the error term has a value of one given any value of the eplanatory variable.
Answer: b
i!culty: Easy
"loom#s: $nowledgeA-%ead: Epected Nalues and Nariances of the J) Estimators
"()*+:
eedback: 5he error term in a regression euation is said to ehibit homoskedasticty
if it has the same variance for all values of the eplanatory variable.
13. Bn the regression of y on , the error term ehibits heteroskedasticity if _____.
a. it has a constant variance
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b. Nar=yO> is a function of
c. is a function of y
d. y is a function of
Answer: b
i!culty: Easy"loom#s: $nowledge
A-%ead: Epected Nalues and Nariances of the J) Estimators
"()*+:
eedback: %eteroskedasticity is present whenever Nar=yO> is a function of
because Nar=uO> H Nar=yO>.
14. &hat is the estimated value of the slope parameter when the regression
euation, y H L@ I L11 I u passes through the origin'
a. ∑i=1
n
y i
b.
y¿¿¿
∑i=1
n
¿
>
c.
∑i=1
n
x i y i
∑i=1
n
x i2
d. ∑i=1
n
( y i− ́ y )2
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: +egression through the rigin and +egression on a ;onstant"()*+:
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eedback: 5he estimated value of the slope parameter when the regression
euation passes through the origin is
∑i=1
n
x i y i
∑i=1
n
x i2
.
17. A natural measure of the association between two random variables is the
correlation coe!cient.
Answer: 5rue
i!culty: Easy
"loom#s: $nowledge
A-%ead: enition of the )imple +egression Dodel
"()*+:
eedback: A natural measure of the association between two random variables is
the correlation coe!cient.
19. 5he sample covariance between the regressors and the rdinary Jeast )uare
=J)> residuals is always positive.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: *roperties of J) on Any )ample of ata
"()*+:eedback: 5he sample covariance between the regressors and the rdinary Jeast
)uare =J)> residuals is ero.
1 residuals is ero.
1?. 5here are n-1 degrees of freedom in rdinary Jeast )uare residuals.
Answer: alse
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i!culty: Easy
"loom#s: $nowledge
A-%ead: Epected Nalues and Nariances of the J) Estimators
"()*+:
eedback: 5here are n-/ degrees of freedom in rdinary Jeast )uare residuals.
/@. 5he variance of the slope estimator increases as the error variance decreases.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: Epected Nalues and Nariances of the J) Estimators
"()*+:
eedback: 5he variance of the slope estimator increases as the error variance
increases.
&hapter 0
1. Bn the euation, 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: ci!culty: Easy
"loom#s: $nowledge
A-%ead: Dotivation for Dultiple +egression
"()*+:
eedback: Bn the euation, y= β
0+ β
1 x
1+ β
2 x
2+u
, β
2 is a slope parameter.
/. ;onsider the following regression euation: y= β
1+ β
2 x
1+ β
2 x
2+u
. &hat does L1
imply'
a. β1 measures the ceteris paribus e8ect of x1 on x2 .
b. β
1 measures the ceteris paribus e8ect of y on x
1 .
c. β
1 measures the ceteris paribus e8ect of x
1 on y .
d. β
1 measures the ceteris paribus e8ect of x
1 on u .
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Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: Dotivation for Dultiple +egression
"()*+:
eedback: β
1 measures the ceteris paribus e8ect of x
1 on y .
2. Bf the eplained sum of suares is 24 and the total sum of suares is 3?, what is
the residual sum of suares'
a. 1@
b. 1/
c. 1<
d. 13
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: Dechanics and Bnterpretation of rdinary Jeast )uares
"()*+: Analytic
eedback: 5he residual sum of suares is obtained by subtracting the eplained
sum of suares from the total sum of suares, or 3?-24H13.
3. &hich of the following is true of +/'
a. +/ is also called the standard error of regression.
b. A low +/ indicates that the rdinary Jeast )uares line ts the data well.c. +/ usually decreases with an increase in the number of independent variables in a
regression.
d. +/ shows what percentage of the total variation in the dependent variable, P, is
eplained by the eplanatory variables.
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: Dechanics and Bnterpretation of rdinary Jeast )uares
"()*+:eedback: +/ shows what percentage of the total variation in P is eplained by the
eplanatory variables.
4. 5he value of +/ always _____.
a. lies below @
b. lies above 1
c. lies between @ and 1
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d. lies between 1 and 1.4
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: Dechanics and Bnterpretation of rdinary Jeast )uares"()*+:
eedback: "y denition, the value of +/ always lies between @ and 1.
7. Bf an independent variable in a multiple linear regression model is an eact linear
combination of other independent variables, the model su8ers from the problem of
_____.
a. perfect collinearity
b. homoskedasticity
c. heteroskedasticty
d. omitted variable bias
Answer: a
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he Epected Nalue of the J) Estimators
"()*+:
eedback: Bf an independent variable in a multiple linear regression model is an
eact linear combination of other independent variables, the model su8ers from the
problem of perfect collinearity.
9. 5he assumption that there are no eact linear relationships among theindependent variables in a multiple linear regression model fails if _____, where n is
the sample sie and k is the number of parameters.
a. nQ/
b. nHkI1
c. nQk
d. nRkI1
Answer: d
i!culty: Easy
"loom#s: $nowledgeA-%ead: 5he Epected Nalue of the J) Estimators
"()*+:
eedback: 5he assumption of no perfect collinearity among independent variables
fails if nRkI1.
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b. β
2 R@ and 1 and / are positively correlated
c. β
2 H@ and 1 and / are negatively correlated
d. β
2 H@ and 1 and / are negatively correlated
Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he Epected Nalue of the J) Estimators
"()*+:
eedback: &hen the variable / is omitted from the regression, the bias in~ β
1 is
negative if β
2 R@ and 1 and / are positively correlated.
11. )uppose the variable / has been omitted from the following regression
euation, y= β0+ β1 x1+ β2 x2+u .~ β
1 is the estimator obtained when / is omitted
from the euation. Bf E=~ β
1 > QL1,~ β
1 is said to _____.
a. have an upward bias
b. have a downward bias
c. be unbiased
d. be biased toward ero
Answer: a
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he Epected Nalue of the J) Estimators
"()*+:
eedback: &hen the variable / is omitted from the following regression euation,
y= β0+ β
1 x
1+ β
2 x
2+u , , β1 has an upward bias if E= β1 > QL1.
1/. %igh =but not perfect> correlation between two or more independent variables is
called _____.
a. heteroskedasticty
b. homoskedasticty
c. multicollinearity
d. micronumerosity
Answer: c
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i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he Nariance of the J) Estimators
"()*+:
eedback: %igh, but not perfect, correlation between two or more independent
variables is called multicollinearity.
12. 5he term _____ refers to the problem of small sample sie.
a. micronumerosity
b. multicollinearity
c. homoskedasticity
d. heteroskedasticity
Answer: a
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he Nariance of the J) Estimators
"()*+:
eedback: 5he term micronumerosity refers to the problem of small sample sie.
13. ind the degrees of freedom in a regression model that has 1@ observations and
9 independent variables.
a. 19
b. /
c. 2
d. 3
Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he Nariance of the J) Estimators
"()*+: Analytic
eedback: 5he degrees of freedom in a regression model is computed by
subtracting the number of parameters from the number of observations in a
regression model. )ince, the number of parameters is one more than the number of
independent variables, the degrees of freedom in this case is 1@-=9 I 1> H /.
14. 5he auss-Darkov theorem will not hold if _____.
a. the error term has the same variance given any values of the eplanatory
variables
b. the error term has an epected value of ero given any values of the independent
variables
c. the independent variables have eact linear relationships among them
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d. the regression model relies on the method of random sampling for collection of
data
Answer: c
i!culty: Easy
"loom#s: $nowledgeA-%ead: E!ciency of J): 5he auss-Darkov 5heorem
"()*+:
eedback: 5he auss-Darkov theorem will not hold if the independent variables
have eact linear relationships among them.
17. 5he term FlinearG in a multiple linear regression model means that the euation
is linear in parameters.
Answer: 5rue
i!culty: Easy
"loom#s: $nowledge
A-%ead: Dotivation for Dultiple +egression
"()*+:
eedback: 5he term FlinearG in a multiple linear regression model means that the
euation is linear in parameters.
19. 5he key assumption for the general multiple regression model is that all factors
in the unobserved error term be correlated with the eplanatory variables.
Answer: alse
i!culty: Easy"loom#s: $nowledge
A-%ead: Dotivation for Dultiple +egression
"()*+:
eedback: 5he key assumption of the general multiple regression model is that all
factors in the unobserved error term be uncorrelated with the eplanatory variables.
1 decreases when an independent variable is
added to a multiple regression model.
Answer: alsei!culty: Easy
"loom#s: $nowledge
A-%ead: Dechanics and Bnterpretation of rdinary Jeast )uares
"()*+:
eedback: 5he coe!cient of determination =+/> never decreases when an
independent variable is added to a multiple regression model.
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1?. An eplanatory variable is said to be eogenous if it is correlated with the error
term.
Answer: alse
i!culty: Easy
"loom#s: $nowledgeA-%ead: 5he Epected Nalue of the J) Estimators
"()*+:
eedback: An eplanatory variable is said to be endogenous if it is correlated with
the error term.
/@. A larger error variance makes it di!cult to estimate the partial e8ect of any of
the independent variables on the dependent variable.
Answer: 5rue
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he Nariance of the J) Estimators
"()*+:
eedback: A larger error variance makes it di!cult to estimate the partial e8ect of
any of the independent variables on the dependent variable.
&hapter 1
1. 5he normality assumption implies that:
a. the population error u is dependent on the eplanatory variables and is normally
distributed with mean eual to one and variance S/
.b. the population error u is independent of the eplanatory variables and is normally
distributed with mean eual to one and variance S.
c. the population error u is dependent on the eplanatory variables and is normally
distributed with mean ero and variance S.
d. the population error u is independent of the eplanatory variables and is normally
distributed with mean ero and variance S/.
Answer: d
i!culty: Doderate
"loom#s: $nowledge
A-%ead: )ampling istributions of the J) Estimators
"()*+:
eedback: 5he normality assumption implies that the population error 6u’ is
independent of the eplanatory variables and is normally distributed with mean
ero and variance S/.
/. &hich of the following statements is true'
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a. 5aking a log of a nonnormal distribution yields a distribution that is closer to
normal.
b. 5he mean of a nonnormal distribution is @ and the variance is S/.
c. 5he ;J5 assumes that the dependent variable is una8ected by unobserved
factors.
d. J) estimators have the highest variance among unbiased estimators.
Answer: a
i!culty: Doderate
"loom#s: $nowledge
A-%ead: )ampling istribution of the J) Estimators
"()*+:
eedback: 5ransformations such as logs of nonnormal distributions, yields
distributions which are closer to normal.
2. A normal variable is standardied by:
a. subtracting o8 its mean from it and multiplying by its standard deviation.
b. adding its mean to it and multiplying by its standard deviation.
c. subtracting o8 its mean from it and dividing by its standard deviation.
d. adding its mean to it and dividing by its standard deviation.
Answer: c
i!culty: Doderate
"loom#s: $nowledge
A-%ead: )ampling istribution of the J) Estimators
"()*+:
eedback: A normal variable is standardied by subtracting o8 its mean from it anddividing by its standard deviation.
3. &hich of the following is a statistic that can be used to test hypotheses about a
single population parameter'
a. statistic
b. t statistic
c. T/ statistic
d. urbin &atson statistic
Answer: bi!culty: Easy
"loom#s: $nowledge
A-%ead: 5esting %ypotheses about a )ingle *opulation *arameter: 5he t 5est
"()*+:
eedback: 5he t statistic can be used to test hypotheses about a single population
parameter.
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4. ;onsider the euation, P H L1 I L/U/ I u. A null hypothesis, %@: L/ H @ states that:
a. U/ has no effect on the epected value of L/.
b. U/ has no e8ect on the epected value of P.
c. L/ has no e8ect on the epected value of P.
d. P has no e8ect on the epected value of U/.
Answer: b
i!culty: Doderate
"loom#s: ;omprehension
A-%ead: 5esting %ypotheses about a )ingle *opulation *arameter: 5he t 5est
"()*+:
eedback: Bn such an euation, a null hypothesis, %@: L/ H @ states that U/ has no
effect on the epected value of P. 5his is because L/ is the coe!cient associated
with U/.
7. 5he signicance level of a test is:
a. the probability of reVecting the null hypothesis when it is false.
b. one minus the probability of reVecting the null hypothesis when it is false.
c. the probability of reVecting the null hypothesis when it is true.
d. one minus the probability of reVecting the null hypothesis when it is true.
Answer: c
i!culty: Doderate
"loom#s: $nowledge
A-%ead: 5esting %ypotheses about a )ingle *opulation *arameter: 5he t 5est
"()*+:
eedback: 5he signicance level of a test refers to the probability of reVecting thenull hypothesis when it is in fact true.
9. 5he general t statistic can be written as:
a. t H Hypothesized value
Standard e rror
b. t Hestimate – hypothesized value
¿ >
c. t H
(estimate – hypothesized value)
variance
d. t H(estimate – hypothesized value)
standard error
Answer: d
i!culty: Easy
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"loom#s: $nowledge
A-%ead: 5esting %ypotheses about a )ingle *opulation *arameter: 5he t 5est
"()*+:
eedback: 5he general t statistic can be written as t H =estimate W hypothesied
value>Mstandard error.
[
d. 5he upper bound of the condence interval for a regression coe!cient, say L V, is
given by^ β
X I Y;ritical value Z standard error = ^ β
X>[
Answer: d
i!culty: Doderate
"loom#s: $nowledge
A-%ead: ;ondence Bntervals
"()*+:
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eedback: 5he upper bound of the condence interval for a regression coe!cient,
say L V, is given by^ β
X I Y;ritical value Z standard error = ^ β
X>[.
1@. &hich of the following tools is used to test multiple linear restrictions'
a. t testb. test
c. test
d. (nit root test
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5esting Dultiple Jinear +estrictions: 5he test
"()*+:
eedback: 5he test is used to test multiple linear restrictions.
11. &hich of the following statements is true of hypothesis testing'
a. 5he t test can be used to test multiple linear restrictions.
b. A test of single restriction is also referred to as a Voint hypotheses test.
c. A restricted model will always have fewer parameters than its unrestricted model.
d. J) estimates maimie the sum of suared residuals.
Answer: c
i!culty: Doderate
"loom#s: $nowledge
A-%ead: 5esting Dultiple Jinear +estrictions: 5he test"()*+:
eedback: A restricted model will always have fewer parameters than its
unrestricted model.
1/. &hich of the following correctly denes statistic if ))+r represents sum of
suared residuals from the restricted model of hypothesis testing, ))+ur represents
sum of suared residuals of the unrestricted model, and is the number of
restrictions placed'
a. H
(SSR ur−SSRr)/q
SSRur /(n−k −1)
b. H(SSRr−SSRur )/qSSRur /(n−k −1)
c. H(SSRur−SSRr)/q
SSRr /(n−k −1)
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d. H(SSRur−SSRr )/(n−k −1)
SSRur /q
Answer: b
i!culty: Doderate"loom#s: $nowledge
A-%ead: 5esting Dultiple Jinear +estrictions: 5he test
"()*+:
eedback: 5he statistic is given by, H(SSRr −SSRur )/ qSSR ur/(n−k −1)
12. &hich of the following statements is true'
a. Bf the calculated value of statistic is higher than the critical value, we reVect the
alternative hypothesis in favor of the null hypothesis.
b. 5he statistic is always nonnegative as ))+r is never smaller than ))+ur.c. egrees of freedom of a restricted model is always less than the degrees of
freedom of an unrestricted model.
d. 5he statistic is more \eible than the t statistic to test a hypothesis with a
single restriction.
Answer: b
i!culty: Doderate
"loom#s: ;omprehension
A-%ead: 5esting Dultiple Jinear +estrictions: 5he test
"()*+:eedback: 5he statistic is always nonnegative as ))+r is never smaller than ))+ur.
13. Bf +/ur H @.7M[ M Y=1-+/ur>Mn W k W 1[.
%ere, represents the number of restrictions. Bn this case it is eual to [email protected][ M Y=1 W @.7M//?[ H Y@.@3?
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14. &hich of the following correctly identies a reason why some authors prefer to
report the standard errors rather than the t statistic'
a. %aving standard errors makes it easier to compute condence intervals.
b. )tandard errors are always positive.
c. 5he statistic can be reported Vust by looking at the standard errors.d. )tandard errors can be used directly to test multiple linear regressions.
Answer: a
i!culty: Dedium
"loom#s: ;omprehension
A-%ead: +eporting +egression +esults
"()*+:
eedback: ne of the advantages of reporting standard errors over t statistics is
that condence intervals can be easily calculated using stand errors.
17. &henever the dependent variable takes on Vust a few values it is close to a
normal distribution.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: )ampling istribution of the J) Estimators
"()*+:
eedback: &henever the dependent variable takes on Vust a few values it cannot
have anything close to a normal distribution. A normal distribution reuires the
dependent variable to take up a large range of values.
19. Bf the calculated value of the t statistic is greater than the critical value, the null
hypothesis, %@ is reVected in favor of the alternative hypothesis, %1.
Answer: 5rue
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5esting %ypotheses about a )ingle *opulation *arameter: 5he t 5est
"()*+:
eedback: Bf the calculated value of the t statistic is greater than the critical value,%@ is reVected in favor of %1.
1
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"loom#s: $nowledge
A-%ead: 5esting %ypotheses about a )ingle *opulation *arameter: 5he t 5est
"()*+:
eedback: %1: L V ] @, where L V is a regression coe!cient associated with an
eplanatory variable, represents a two-sided alternative hypothesis.
1?. Bf ^ β
1 and^ β
/ are estimated values of regression coe!cients associated with
two eplanatory variables in a regression euation, then the standard error = ^ β
1 W
^ β/> H standard error =
^ β1> W standard error =
^ β/>.
Answer: alse
i!culty: Easy
"loom#s: ;omprehension
A-%ead: 5esting %ypotheses about a )ingle Jinear ;ombinations of the *arameters"()*+:
eedback: Bf ^ β
1 and^ β
/ are estimated values of regression coe!cients
associated with two eplanatory variables in a regression euation, then the
standard error = ^ β
1 W^ β
/> ] standard error = ^ β
1> W standard error = ^ β
/>.
/@. )tandard errors must always be positive.
Answer: 5ruei!culty: Easy
"loom#s: $nowledge
A-%ead: 5esting %ypotheses about a )ingle Jinear ;ombinations of the *arameters
"()*+:
eedback: )tandard errors must always be positive since they are estimates of
standard deviations.
&hapter 5
1. &hich of the following statements is true'
a. 5he standard error of a regression, σ̂ , is not an unbiased estimator for σ ,
the standard deviation of the error, u, in a multiple regression model.
b. Bn time series regressions, J) estimators are always unbiased.
c. Almost all economists agree that unbiasedness is a minimal reuirement for an
estimator in regression analysis.
d. All estimators in a regression model that are consistent are also unbiased.
Answer: b
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i!culty: Doderate
"loom#s: $nowledge
A-%ead: ;onsistency
"()*+:
eedback: 5he standard error of a regression is not an unbiased estimator for the
standard deviation of the error in a multiple regression model.
/. Bf^ β
V, an unbiased estimator of β
V, is consistent, then the:
a. distribution of^ β
V becomes more and more loosely distributed around β
V as
the sample sie grows.
b. distribution of^ β
V becomes more and more tightly distributed around β
V as
the sample sie grows.
c. distribution of^ β
V tends toward a standard normal distribution as the sample
sie grows.
d. distribution of^ β
V remains una8ected as the sample sie grows.
Answer: b
i!culty: Dedium
"loom#s: $nowledge
A-%ead: ;onsistency
"()*+:
eedback: Bf
^ β V, an unbiased estimator of
β V, is consistent, then the distribution
of^ β
V becomes more and more tightly distributed around β
V as the sample sie
grows.
2. Bf^ β
V, an unbiased estimator of β
V, is also a consistent estimator of β
V,
then when the sample sie tends to innity:
a. the distribution of^ β
V collapses to a single value of ero.
b. the distribution of
^
β V diverges away from a single value of ero.
c. the distribution of^ β
V collapses to the single point β
V.
d. the distribution of^ β
V diverges away from β
V.
Answer: c
i!culty: Easy
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"loom#s: $nowledge
A-%ead: ;onsistency
"()*+:
eedback: Bf^ β
V, an unbiased estimator of β
V, is also a consistent estimator of
β V, then when the sample sie tends to innity the distribution of^ β V collapses
to the single point β V.
3. Bn a multiple regression model, the J) estimator is consistent if:
a. there is no correlation between the dependent variables and the error term.
b. there is a perfect correlation between the dependent variables and the error
term.
c. the sample sie is less than the number of parameters in the model.
d. there is no correlation between the independent variables and the error term.
Answer: d
i!culty: Doderate
"loom#s: $nowledge
A-%ead: ;onsistency
"()*+:
eedback: Bn a multiple regression model, the J) estimator is consistent if there is
no correlation between the eplanatory variables and the error term.
4. Bf the error term is correlated with any of the independent variables, the J)
estimators are:a. biased and consistent.
b. unbiased and inconsistent.
c. biased and inconsistent.
d. unbiased and consistent.
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: ;onsistency
"()*+:
eedback: Bf the error term is correlated with any of the independent variables, then
the J) estimators are biased and inconsistent.
7. Bf ^1 H ;ov=1M/> M Nar=1> where 1 and / are two independent variables in a
regression euation, which of the following statements is true'
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a. Bf / has a positive partial effect on the dependent variable, and ^1 Q @, then the
inconsistency in the simple regression slope estimator associated with 1 is
negative.
b. Bf / has a positive partial effect on the dependent variable, and ^1 Q @, then the
inconsistency in the simple regression slope estimator associated with 1 is positive.
c. Bf 1 has a positive partial effect on the dependent variable, and ^1 Q @, then theinconsistency in the simple regression slope estimator associated with 1 is
negative.
d. Bf 1 has a positive partial effect on the dependent variable, and ^1 Q @, then the
inconsistency in the simple regression slope estimator associated with 1 is positive.
Answer: b
i!culty: Doderate
"loom#s: $nowledge
A-%ead: ;onsistency
"()*+:
eedback: iven that ^1 H ;ov=1M/>MNar=1> where 1 and / are two independent
variables in a regression euation, if / has a positive partial e8ect on the
dependent variable, and ^1 Q @, then the inconsistency in the simple regression
slope estimator associated with 1 is positive.
9. Bf J) estimators satisfy asymptotic normality, it implies that:
a. they are approimately normally distributed in large enough sample sies.
b. they are approimately normally distributed in samples with less than 1@
observations.
c. they have a constant mean eual to ero and variance eual to S/.
d. they have a constant mean eual to one and variance eual to S.
Answer: a
i!culty: Easy
"loom#s: $nowledge
A-%ead: Asymptotic 0ormality and Jarge )ample Bnference
"()*+:
eedback: Bf J) estimators satisfy asymptotic normality, it implies that they are
approimately normally distributed in large enough sample sies.
, is not constant, _____.
a. the t statistics are invalid and condence intervals are valid for small sample
sies
b. the t statistics are valid and condence intervals are invalid for small sample
sies
c. the t statistics condence intervals are valid no matter how large the sample sie
is
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c. the t statistics and condence intervals are both invalid no matter how large the
sample sie is
Answer: d
i!culty: Doderate
"loom#s: $nowledgeA-%ead: Asymptotic 0ormality and Jarge )ample Bnference
"()*+:
eedback: Bf variance of the dependent variable conditional on an eplanatory
variable is not a constant the usual t statistics condence intervals are both invalid
no matter how large the sample sie is.
?. Bf^ β
V is an J) estimator of a regression coe!cient associated with one of the
eplanatory variables, such that VH 1, /, ., n, asymptotic standard error of^ β
V
will refer to the:
a. estimated variance of^ β
V when the error term is normally distributed.
b. estimated variance of a given coe!cient when the error term is not normally
distributed.
c. suare root of the estimated variance of^ β
V when the error term is normally
distributed.
d. suare root of the estimated variance of^ β
V when the error term is not normally
distributed.
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: Asymptotic 0ormality and Jarge )ample Bnference
"()*+:
eedback: Asymptotic standard error refers to the suare root of the estimated
variance of^ β
V when the error term is not normally distributed.
1@. A useful rule of thumb is that standard errors are epected to shrink at a ratethat is the inverse of the:
a. suare root of the sample sie.
b. product of the sample sie and the number of parameters in the model.
c. suare of the sample sie.
d. sum of the sample sie and the number of parameters in the model.
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"loom#s: $nowledge
A-%ead: Asymptotic 0ormality and Jarge )ample Bnference
"()*+:
eedback: 5he JD statistic follows a χ / distribution.
13. &hich of the following statements is true'
a. Bn large samples there are not many discrepancies between the outcomes of the
test and the JD test.
b. egrees of freedom of the unrestricted model are necessary for using the JD
test.
c. 5he JD test can be used to test hypotheses with single restrictions only and
provides ine!cient results for multiple restrictions.
d. 5he JD statistic is derived on the basis of the normality assumption.
Answer: a
i!culty: Doderate"loom#s: $nowledge
A-%ead: Asymptotic 0ormality and Jarge )ample Bnference
"()*+:
eedback: Bn large samples there are not many discrepancies between the test
and the JD test because asymptotically the two statistics have the same probability
of a 5ype 1 error.
14. &hich of the following statements is true under the auss-Darkov assumptions'
a. Among a certain class of estimators, J) estimators are best linear unbiased, but
are asymptotically ine!cient.b. Among a certain class of estimators, J) estimators are biased but
asymptotically e!cient.
c. Among a certain class of estimators, J) estimators are best linear unbiased and
asymptotically e!cient.
d. 5he JD test is independent of the auss-Darkov assumptions.
Answer: c
i!culty: Doderate
"loom#s: $nowledge
A-%ead: Asymptotic E!ciency of J)
"()*+:
eedback: (nder the auss-Darkov assumptions, among a certain class of
estimators, J) estimators are best linear unbiased and asymptotically e!cient.
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17. Bf variance of an independent variable in a regression model, say 1, is greater
than @, or Nar=1> Q @, the inconsistency in^ β
1 =estimator associated with 1> is
negative, if 1 and the error term are positively related.
Answer: alsei!culty: Easy
"loom#s: $nowledge
A-%ead: ;onsistency
"()*+:
eedback: Bf variance of an independent variable, say 1, is greater than @, the
inconsistency in^ β
1 =estimator associated with 1> is positive if 1 and the error
term are positively related.
19. Even if the error terms in a regression euation, u1, u/,.., un, are not normally
distributed, the estimated coe!cients can be normally distributed.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: Asymptotic 0ormality and Jarge )ample Bnference
"()*+:
eedback: Even if the error terms in a regression euation, u1, u/,.., un, are not
normally distributed, the estimated coe!cients cannot be normally distributed.
1, and
more than ?4` of the area under the distribution is within two standard deviations.
Answer: 5rue
i!culty: Easy
"loom#s: $nowledge
A-%ead: Asymptotic 0ormality and Jarge )ample Bnference
"()*+:
eedback: A normally distributed random variable is symmetrically distributed about
its mean, it can take on any positive or negative value =but with ero probability>,and more than ?4` of the area under the distribution is within two standard
deviations.
1?. 5he statistic is also referred to as the score statistic.
Answer: alse
i!culty: Easy
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"loom#s: $nowledge
A-%ead: Asymptotic 0ormality and Jarge )ample Bnference
"()*+:
eedback: 5he JD statistic is also referred to as the score statistic.
/@. 5he JD statistic reuires estimation of the unrestricted model only.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: Asymptotic 0ormality and Jarge )ample Bnference
"()*+:
eedback: 5he JD statistic reuires estimation of the restricted model only.
&hapter 2
1. A change in the unit of measurement of the dependent variable in a model does
not lead to a change in:a. the standard error of the regression.
b. the sum of suared residuals of the regression.
c. the goodness-of-t of the regression.
d. the condence intervals of the regression.
Answer: c
i!culty: Doderate
"loom#s: $nowledge
A-%ead: E8ects of ata )caling on J) )tatistics
"()*+:eedback: ;hanging the unit of measurement of the dependent variable in a model
does not lead to a change in the goodness of t of the regression.
/. ;hanging the unit of measurement of any independent variable, where log of the
dependent variable appears in the regression:
a. a8ects only the intercept coe!cient.
b. a8ects only the slope coe!cient.
c. a8ects both the slope and intercept coe!cients.
d. a8ects neither the slope nor the intercept coe!cient.
Answer: a
i!culty: Doderate
"loom#s: ;omprehension
A-%ead: E8ects of ata )caling on J) )tatistics
"()*+:
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eedback: ;hanging the unit of measurement of any independent variable, where
log of the independent variable appears in the regression only a8ects the intercept.
5his follows from the property log=ab> H log=a> I log=b>.
2. A variable is standardied in the sample:
a. by multiplying by its mean.b. by subtracting o8 its mean and multiplying by its standard deviation.
c. by subtracting o8 its mean and dividing by its standard deviation.
d. by multiplying by its standard deviation.
Answer: c
i!culty: Doderate
"loom#s: $nowledge
A-%ead: E8ects of ata )caling on J) )tatistics
"()*+:
eedback: A variable is standardied in the sample by subtracting o8 its mean and
dividing by its standard deviation.
3. )tandardied coe!cients are also referred to as:
a. beta coe!cients.
b. y coe!cients.
c. alpha coe!cients.
d. V coe!cients.
Answer: a
i!culty: Easy
"loom#s: $nowledgeA-%ead: E8ects of ata )caling on J) )tatistics
"()*+:
eedback: )tandardied coe!cients are also referred to as beta coe!cients.
4. Bf a regression euation has only one eplanatory variable, say 1, its
standardied coe!cient must lie in the range:
a. -/ to @.
b. -1 to 1.
c. @ to 1.
d. @ to /.
Answer: b
i!culty: Easy
"loom#s: ;omprehension
A-%ead: E8ects of ata )caling on J) )tatistics
"()*+:
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eedback: Bf a regression euation has only one eplanatory variable, say 1, its
standardied coe!cient is the correlation coe!cient between the dependent
variable and 1, and must lie in the range -1 to 1.
7. Bn the following euation, gdp refers to gross domestic product, and B refers to
foreign direct investment.
log=gdp) H /.74 I @.4/9log=bankcredit > I @.///FDI
[email protected]> =@.@//> =@.@19>
&hich of the following statements is then true'
a. Bf gdp increases by 1`, bank credit increases by @.4/9`, the level of B
remaining constant.
b. Bf bank credit increases by 1`, gdp increases by @.4/9`, the level of B
remaining constant.
c. Bf gdp increases by 1`, bank credit increases by [email protected]/9>`, the level of B
remaining constant.
d. Bf bank credit increases by 1`, gdp increases by [email protected]/9>`, the level of B
remaining constant.
Answer: b
i!culty: Doderate
"loom#s: Application
A-%ead: Dore on unctional orm
"()*+:
eedback: 5he euation suggests that if bank credit increases by 1`, gdp increases
by @.4/9`. 5his is known from the value of the coe!cient associated with bankcredit.
9. Bn the following euation, gdp refers to gross domestic product, and B refers to
foreign direct investment.
log=gdp) H /.74 I @.4/9log=bankcredit > I @.///FDI
[email protected]> =@.@//> =@.@19>
&hich of the following statements is then true'
a. Bf B increases by 1`, gdp increases by approimately //./`, the amount of
bank credit remaining constant.b. Bf B increases by 1`, gdp increases by approimately /7.4`, the amount of
bank credit remaining constant.
c. Bf B increases by 1`, gdp increases by approimately /3.
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Answer: c
i!culty: %ard
"loom#s: Application
A-%ead: Dore on unctional orm
"()*+:
eedback: 5he euation suggests that if B increases by 1`, gdp increases by1@@=ep=@.///> W 1>`. 5his euals =1./3 H /3.
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1@. &hich of the following models is used uite often to capture decreasing or
increasing marginal e8ects of a variable'
a. Dodels with logarithmic functions
b. Dodels with uadratic functions
c. Dodels with variables in level
d. Dodels with interaction terms
Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: Dore on unctional orm
"()*+:
eedback: Dodels with uadratic functions are used uite often to capture
decreasing or increasing marginal e8ects of a variable
11. &hich of the following correctly represents the euation for adVusted +/'
a.´ R / H 1 – [SSR/(n –1)]/[SST/(n+1)]
b.´ R / H 1 – [SSR/(n –k – 1)]/[SST/(n+1)]
c.´ R / H 1 – [SSR/(n –k – 1)]/[SST/(n – 1)]
d.´ R / H 1 – [SSR]/[SST/(n – 1)]
Answer: c
i!culty: Easy
"loom#s: $nowledgeA-%ead: Dore on oodness-of-it and )election of +egressors
"()*+:
eedback:´ R / H 1 – [SSR/(n –k – 1)]/[SST/(n – 1)]
1/. &hich of the following correctly identies an advantage of using adVusted +/
over +/'
a. AdVusted +/ corrects the bias in +/.
b. AdVusted +/ is easier to calculate than +/.
c. 5he penalty of adding new independent variables is better understood throughadVusted +/ than +/.
d. 5he adVusted +/ can be calculated for models having logarithmic functions while
+/ cannot be calculated for such models.
Answer: c
i!culty: Doderate
"loom#s: $nowledge
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A-%ead: Dore on oodness-of-it and )election of +egressors
"()*+:
eedback: 5he penalty of adding new independent variables is better understood
through adVusted +/ than +/ since its calculation is directly dependent on the
number of independent variables included.
12. 5wo euations form a nonnested model when:
a. one is logarithmic and the other is uadratic.
b. neither euation is a special case of the other.
c. each euation has the same independent variables.
d. there is only one independent variable in both euations.
Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: Dore on oodness-of-it and )election of +egressors
"()*+:
eedback: 5wo euations form a nonnested model when neither euation is a
special case of the other.
13. A predicted value of a dependent variable:
a. represents the di8erence between the epected value of the dependent variable
and its actual value.
b. is always eual to the actual value of the dependent variable.
c. is independent of eplanatory variables and can be estimated on the basis of the
residual error term only.
d. represents the epected value of the dependent variable given particular valuesfor the eplanatory variables.
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: *rediction and +esidual Analysis
"()*+:
eedback: A predicted value of a dependent variable represents the epected value
of the dependent variable given particular values for the eplanatory variables.
14. +esidual analysis refers to the process of:
a. eamining individual observations to see whether the actual value of a
dependent variable di8ers from the predicted value.
b. calculating the suared sum of residuals to draw inferences for the consistency of
estimates.
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c. transforming models with variables in level to logarithmic functions so as to
understand the e8ect of percentage changes in the independent variable on the
dependent variable.
d. sampling and collection of data in such a way to minimie the suared sum of
residuals.
Answer: a
i!culty: Doderate
"loom#s: $nowledge
A-%ead: *rediction and +esidual Analysis
"()*+:
eedback: +esidual analysis refers to the process of eamining individual
observations to see whether the actual value of a dependent variable di8ers from
the predicted value.
17. "eta coe!cients are always greater than standardied coe!cients.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: E8ects of ata )caling on J) )tatistics
"()*+:
eedback: "eta coe!cients the same as standardied coe!cients.
19. Bf a new independent variable is added to a regression euation, the adVusted +/
increases only if the absolute value of the t statistic of the new variable is greater
than one.
Answer: 5rue
i!culty: Easy
"loom#s: $nowledge
A-%ead: Dore on oodness-of-it and )election of +egressors
"()*+:
eedback: Bf a new independent variable is added to a regression euation, the
adVusted +/ increases only if the absolute value of the t statistic of the new variable
is greater than one in absolute value.
1
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eedback: statistic can be used only to test nested models.
1?. *redictions of a dependent variable are subVect to sampling variation.
Answer: 5rue
i!culty: Easy"loom#s: $nowledge
A-%ead: *rediction and +esidual Analysis
"()*+:
eedback: *redictions of a dependent variable are subVect to sampling variation
since they are obtained using J) estimators.
/@. 5o make predictions of logarithmic dependent variables, they rst have to be
converted to their level forms.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: *rediction and +esidual Analysis
"()*+:
eedback: Bt is possible to make predictions of dependent variables when they are in
their logarithmic form. Bt is not necessary to convert them into their level forms.
&hapter 3
1. A _____ variable is used to incorporate ualitative information in a regression
model.
a. dependentb. continuous
c. binomial
d. dummy
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: escribing ualitative Bnformation
"()*+:
eedback: A dummy variable or binary variable is used to incorporate ualitativeinformation in a regression model.
/. Bn a regression model, which of the following will be described using a binary
variable'
a. &hether it rained on a particular day or it did not
b. 5he volume of rainfall during a year
c. 5he percentage of humidity in air on a particular day
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d. 5he concentration of dust particles in air
Answer: a
i!culty: Dedium
"loom#s: ;omprehension
A-%ead: escribing ualitative Bnformation"()*+:
eedback: A binary variable is used to describe ualitative information in regression
model. 5herefore, such a variable will be used to describe whether it rained on a
particular day or it did not.
2. &hich of the following is true of dummy variables'
a. A dummy variable always takes a value less than 1.
b. A dummy variable always takes a value higher than 1.
c. A dummy variable takes a value of @ or 1.
d. A dummy variable takes a value of 1 or 1@.
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: escribing ualitative Bnformation
"()*+:
eedback: A dummy variable takes a value of @ or 1.
5he following simple model is used to determine the annual savings of an individual
on the basis of his annual income and education.
)avings H L@I@ Edu I L1BncIu 5he variable 6Edu# takes a value of 1 if the person is educated and the variable 6Bnc#
measures the income of the individual.
3. +efer to the model above. 5he inclusion of another binary variable in this model
that takes a value of 1 if a person is uneducated, will give rise to the problem of
_____.
a. omitted variable bias
b. self-selection
c. dummy variable trapd. heteroskedastcity
Answer: c
i!culty: Dedium
"loom#s: Application
A-%ead: escribing ualitative Bnformation
"()*+: Analytic
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eedback: 5he inclusion of another dummy variable in this model would introduce
perfect collinearity and lead to a dummy variable trap.
5he following simple model is used to determine the annual savings of an individual
on the basis of his annual income and education.
)avings H L@I@ Edu I L1BncIu 5he variable 6Edu# takes a value of 1 if the person is educated and the variable 6Bnc#
measures the income of the individual.
4. +efer to the model above. 5he benchmark group in this model is _____.
a. the group of educated people
b. the group of uneducated people
c. the group of individuals with a high income
d. the group of individuals with a low income
Answer: b
i!culty: Doderate
"loom#s: Application
A-%ead: A )ingle ummy Bndependent Nariable
"()*+: Analytic
eedback: 5he benchmark group is the group against which comparisons are made.
Bn this case, the savings of a literate person is being compared to the savings of an
illiterate personC therefore, the group of illiterate people is the base group or
benchmark group.
5he following simple model is used to determine the annual savings of an individual
on the basis of his annual income and education.)avings H L@I@ Edu I L1BncIu
5he variable 6Edu# takes a value of 1 if the person is educated and the variable 6Bnc#
measures the income of the individual.
7. +efer to the above model. Bf @ Q @, _____.
a. uneducated people have higher savings than those who are educated
b. educated people have higher savings than those who are not educated
c. individuals with lower income have higher savings
d. individual with lower income have higher savings
Answer: b
i!culty: Doderate
"loom#s: Application
A-%ead: A )ingle ummy Bndependent Nariable
"()*+: Analytic
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eedback: 5he coe!cient @ measures the impact of education on an individual#s
annual savings. Bf it has a positive impact, as in this case, educated people should
have higher savings.
9. 5he income of an individual in "udopia depends on his ethnicity and several other
factors which can be measured uantitatively. Bf there are 4 ethnic groups in"udopia, how many dummy variables should be included in the regression euation
for income determination in "udopia'
a. 1
b. 4
c. 7
d. 3
Answer: d
i!culty: Doderate
"loom#s: Application
A-%ead: (sing ummy Nariables for Dultiple ;ategories
"()*+: Analytic
eedback: Bf a regression model is to have di8erent intercepts for, say, g groups or
categories, we need to include g -1 dummy variables in the model along with an
intercept. Bn this case, the regression euation should include 4-1H3 dummy
variables since there are 4 ethnic groups.
_____
variable.a. dependent variable
b. ordinal variable
c. continuous variable
d. *oisson variable
Answer: b
i!culty: Doderate
"loom#s: Application
A-%ead: (sing ummy Nariables for Dultiple ;ategories
"()*+: Analyticeedback: 5he value of the variable 6+ating# depends on the employer#s rating of
the worker. 5herefore, it incorporates ordinal information and is called an ordinal
variable.
?. &hich of the following is true of ;how test'
a. Bt is a type of t test.
b. Bt is a type of sign test.
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c. Bt is only valid under homoskedasticty.
d. Bt is only valid under heteroskedasticity.
Answer: c
i!culty: Easy
"loom#s: $nowledgeA-%ead: Bnteractions Bnvolving ummy Nariables
"()*+:
eedback: )ince the ;how test is Vust an test, it is only valid under
homoskedasticity.
1@. &hich of the following is true of dependent variables'
a. A dependent variable can only have a numerical value.
b. A dependent variable cannot have more than / values.
c. A dependent variable can be binary.
d. A dependent variable cannot have a ualitative meaning.
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: A "inary ependent Nariable: 5he Jinear *robability Dodel
"()*+:
eedback: A dependent variable is binary if it has a ualitative meaning.
11. Bn the following regression euation, y is a binary variable:
yH L@IL11ILk kI u
Bn this case, the estimated slope coe!cient, ^
β1 measures _____.
a. the predicted change in the value of y when 1 increases by one unit, everything
else remaining constant
b. the predicted change in the value of y when 1 decreases by one unit, everything
else remaining constant
c. the predicted change in the probability of success when 1 decreases by one unit,
everything else remaining constant
d. the predicted change in the probability of success when 1 increases by one unit,
everything else remaining constant
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: A "inary ependent Nariable: 5he Jinear *robability Dodel
"()*+:
eedback: A binary dependent variable is used when a regression model is used to
eplain a ualitative event. 5he dependent variable takes a value of 1 when the
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event takes place =success> and it takes a value of ero when the event does not
take place. 5he coe!cient of an independent variable in this case measures the
predicted change in the probability of success when the independent variable
increases by one unit.
1/. ;onsider the following regression euation: y H L@IL11ILk kI u Bn which of the following cases, the dependent variable is binary'
a. y indicates the gross domestic product of a country
b. y indicates whether an adult is a college dropout
c. y indicates household consumption ependiture
d. y indicates the number of children in a family
Answer: b
i!culty: Easy
"loom#s: Application
A-%ead: A "inary ependent Nariable: 5he Jinear *robability Dodel
"()*+: Analytic
eedback: 5he dependent variable, y is binary if it is used to indicate a ualitative
outcome.
12. &hich of the following auss-Darkov assumptions is violated by the linear
probability model'
a. 5he assumption of constant variance of the error term.
b. 5he assumption of ero conditional mean of the error term.
c. 5he assumption of no eact linear relationship among independent variables.
d. 5he assumption that none of the independent variables are constants.
Answer: a
i!culty: Easy
"loom#s: $nowledge
A-%ead: A "inary ependent Nariable: 5he Jinear *robability Dodel
"()*+:
eedback: 5he linear probability model violates the assumption of constant variance
of the error term.
13. &hich of the following problems can arise in policy analysis and program
evaluation using a multiple linear regression model'a. 5here eists homoscedasticity in the model.
b. 5he model can produce predicted probabilities that are less than ero and greater
than one.
c. 5he model leads to the omitted variable bias as only two independent factors can
be included in the model.
d. 5he model leads to an overestimation of the e8ect of independent variables on
the dependent variable.
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Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: Dore on *olicy Analysis and *rogram Evaluation
"()*+:eedback: 5he model can produce predicted probabilities that are less than ero and
greater than one.
14. ;onsider the following regression euation: y H L@IL11ILk kI u
Bn which of the following cases, is 6y# a discrete variable'
a. y indicates the gross domestic product of a country
b. y indicates the total volume of rainfall during a year
c. y indicates household consumption ependiture
d. y indicates the number of children in a family
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: Bnterpreting +egression +esults with iscrete ependent Nariables
"()*+:
eedback: 5he number of children in a family can only take a small set of integer
values. 5herefore, y is a discrete variable if it measures the number of children in a
family.
17. A binary variable is a variable whose value changes with a change in the
number of observations.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: escribing ualitative Bnformation
"()*+:
eedback: A binary variable is one whose value depends on the event taking place.
19. A dummy variable trap arises when a single dummy variable describes a given
number of groups.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: A )ingle ummy Bndependent Nariable
"()*+:
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eedback: A dummy variable trap arises when too many dummy variables describe
a given number of groups.
1
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c. 5he rdinary Jeast )uare estimators are not the best linear unbiased estimators
if heteroskedasticity is present.
d. Bt is not possible to obtain statistics that are robust to heteroskedasticity of an
unknown form.
Answer: ci!culty: Easy
"loom#s: $nowledge
A-%ead: ;onseuences of %eteroskedasticity for J)
"()*+:
eedback: 5he rdinary Jeast )uare estimators are no longer the best linear
unbiased estimators if heteroskedasticity is present in a regression model.
/. ;onsider the following regression model: yiHL@IL1 iIui. Bf the rst four auss-
Darkov assumptions hold true, and the error term contains heteroskedasticity, then
_____.
a. Nar=uiOi> H@
b. Nar=uiOi> H1
c. Nar=uiOi> H Si/
d. Nar=uiOi> HS
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: %eteroskedasticity-+obust Bnference after J) Estimation
"()*+:
eedback: Bf the rst four auss-Darkov assumptions hold and the error termcontains heteroskedasticity, then Nar=uiOi> H Si/.
2. 5he general form of the t statistic is _____.
a. t =estimate−hypothesized value
standard error
b. t =hypothesized value−estimate
standard error
c. t =
standard error
estimate−hypothesized value
d. t =estimate−hypothesized value
Answer: a
i!culty: Easy
"loom#s: $nowledge
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A-%ead: %eteroskedasticity-+obust Bnference after J) Estimation
"()*+:
eedback: 5he general form of the t statistic is t =estimate−hypothesized value
standard error .
3. &hich of the following is true of the J) t statistics'
a. 5he heteroskedasticity-robust t statistics are Vustied only if the sample sie is
large.
b. 5he heteroskedasticty-robust t statistics are Vustied only if the sample sie is
small.
c. 5he usual t statistics do not have eact t distributions if the sample sie is large.
d. Bn the presence of homoscedasticity, the usual t statistics do not have eact t
distributions if the sample sie is small.
Answer: a
i!culty: Easy"loom#s: $nowledge
A-%ead: %eteroskedasticity-+obust Bnference after J) Estimation
"()*+:
eedback: 5he heteroskedasticity-robust t statistics are Vustied only if the sample
sie is large.
4. 5he heteroskedasticity-robust _____ is also called the heteroskedastcity-robust
&ald statistic.
a. t statistic
b. F statisticc. L statistic
d. statistic
Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: %eteroskedasticity-+obust Bnference after J) Estimation
"()*+:
eedback: 5he heteroskedasticity-robust F statistic is also called the
heteroskedastcity-robust &ald statistic.
7. &hich of the following tests helps in the detection of heteroskedasticity'
a. 5he "reusch-*agan test
b. 5he "reusch-odfrey test
c. 5he urbin-&atson test
d. 5he ;how test
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Answer: a
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5esting for %eteroskedasticity
"()*+:
eedback: 5he "reusch-*agan test is used for the detection of heteroskedasticity ina regression model.
9. &hat will you conclude about a regression model if the "reusch-*agan test results
in a small p-value'
a. 5he model contains homoskedasticty.
b. 5he model contains heteroskedasticty.
c. 5he model contains dummy variables.
d. 5he model omits some important eplanatory factors.
Answer: b
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5esting for %eteroskedasticity
"()*+:
eedback: 5he "reusch-*agan test results in a small p-value if the regression model
contains heteroskedasticty.
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c. 5he number of regressors used in the &hite test is larger than the number of
regressors used in the "reusch-*agan test.
d. 5he number of regressors used in the "reusch-*agan test is larger than the
number of regressors used in the &hite test.
Answer: ci!culty: Easy
"loom#s: $nowledge
A-%ead: 5esting for %eteroskedasticity
"()*+:
eedback: 5he &hite test includes the suares and cross products of all
independent variables. 5herefore, the number of regressors is larger for the &hite
test.
1@. &hich of the following is true of the &hite test'
a. 5he &hite test is used to detect the presence of multicollinearity in a linear
regression model.
b. 5he &hite test cannot detect forms of heteroskedasticity that invalidate the usual
rdinary Jeast )uares standard errors.
c. 5he &hite test can detect the presence of heteroskedasticty in a linear regression
model even if the functional form is misspecied.
d. 5he &hite test assumes that the suare of the error term in a regression model is
uncorrelated with all the independent variables, their suares and cross products.
Answer: d
i!culty: Easy
"loom#s: $nowledgeA-%ead: 5esting for %eteroskedasticity
"()*+:
eedback: 5he &hite test assumes that the suare of the error term in a regression
model is uncorrelated with all the independent variables, the suares of
independent variables and all the cross products.
11. &hich of the following is true'
a. Bn ordinary least suares estimation, each observation is given a di8erent weight.
b. Bn weighted least suares estimation, each observation is given an identical
weight.c. Bn weighted least suares estimation, less weight is given to observations with a
higher error variance.
d. Bn ordinary least suares estimation, less weight is given to observations with a
lower error variance.
Answer: c
i!culty: Easy
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"loom#s: $nowledge
A-%ead: &eighted Jeast )uares Estimation
"()*+:
eedback: Bn weighted Jeast )uares estimation, less weight is given to
observations with a higher error variance.
1/. &eighted least suares estimation is used only when _____.
a. the dependent variable in a regression model is binary
b. the independent variables in a regression model are correlated
c. the error term in a regression model has a constant variance
d. the functional form of the error variances is known
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: &eighted Jeast )uares Estimation
"()*+:
eedback: &eighted Jeast )uares estimation is used only when the functional form
of the error variances is known.
12. ;onsider the following regression euation: y= β
0+ β
1 x
1+u
. &hich of the
following indicates a functional form misspecication in E=yO>'
a. rdinary Jeast )uares estimates eual &eighted Jeast )uares estimates.
b. rdinary Jeast )uares estimates eceed &eighted Jeast )uares estimates by a
small magnitude.
c. &eighted Jeast )uares estimates eceed rdinary Jeast )uares estimates by asmall magnitude.
d. rdinary Jeast )uare estimates are positive while &eighted Jeast )uares
estimates are negative.
Answer: d
i!culty: Easy
"loom#s: $nowledge
A-%ead: &eighted Jeast )uares Estimation
"()*+:
eedback: Bf rdinary Jeast )uare estimates are positive while &eighted Jeast)uares estimates are negative, the functional form of a regression euation is said
to be misspecied.
13. &hich of the following tests is used to compare the rdinary Jeast )uares
=J)> estimates and the &eighted Jeast )uares =&J)> estimates'
a. 5he &hite test
b. 5he %ausman test
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c. 5he urbin-&atson test
d. 5he "reusch-odfrey test
Answer: b
i!culty: Easy
"loom#s: $nowledgeA-%ead: &eighted Jeast )uares Estimation
"()*+:
eedback: 5he %ausman test can be used to formally compare the J) and &J)
estimates to see if they di8er by more than sampling error suggests they should.
14. 5he linear probability model contains heteroskedasticity unless _____.
a. the intercept parameter is ero
b. all the slope parameters are positive
c. all the slope parameters are ero
d. the independent variables are binary
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: 5he Jinear *robability Dodel +evisited
"()*+:
eedback: 5he linear probability model contains heteroskedasticity unless all the
slope parameters are ero.
17. 5he interpretation of goodness-of-t measures changes in the presence of
heteroskedasticity.
Answer: alse
i!culty: Easy
"loom#s: $nowledge
A-%ead: ;onseuences of %eteroskedasticity for J)
"()*+:
eedback: 5he interpretation of goodness-of-t measures is una8ected by the
presence of heteroskedasticty.
19. Dulticollinearity among the independent variables in a linear regression modelcauses the heteroskedasticity-robust standard errors to be large.
Answer: 5rue
i!culty: Easy
"loom#s: $nowledge
A-%ead: %eteroskedasticity-+obust Bnference after J) Estimation
"()*+:
-
8/9/2019 Introductory econometrics test bank
58/133
eedback: Dulticollinearity among the independent variables in a linear regression
model causes the heteroskedasticity-robust standard errors to be large.
1 H L@ I L11 I L/1/
I L22 I u. 5hismodel will su8er from functional form misspecication if _____.
a. L@ is omitted from the model
b. u is heteroskedastic
c. 1/ is omitted from the model
d. 2 is a binary variable
Answer: c
-
8/9/2019 Introductory econometrics test bank
59/133
i!culty: Easy
"loom#s: ;omprehension
A-%ead: unctional orm Disspecication
"()*+:
eedback: 5he model su8ers from functional form misspecication if 1/ is omitted
from the model since it is a function of 1 which is an observed eplanatory variable.
/. A regression model su8ers from functional form misspecication if _____.
a. a key variable is binary.
b. the dependent variable is binary.
c. an interaction term is omitted.
d. the coe!cient of a key variable is ero.
Answer: c
i!culty: Easy
"loom#s: $nowledge
A-%ead: unctional orm Disspecication
"()*+:
eedback: A regression model su8ers from functional form misspecication if an
interaction term is omitted.
2. &hich of the following is true'
a. A functional form misspecication can occur if the level of a variable is used when
the logarithm is more appropriate.
b. A functional form misspecication occurs only if a key variable is uncorrelated
with the error term. .
c. A functional form misspecication does not lead to biasedness in the ordinaryleast suares estimators.
d. A functional form misspecication does not lead to inconsistency in the ordinary
least suares estimators.
Answer: a
i!culty: Easy
"loom#s: $nowledge
A-%ead: unctional orm Disspecication
"()*+:
eedback: A functional form misspecication can occur if the level of a variable isused when the logarithm is more appropriate.
3. &hich of the following is true of +egression )pecication Error 5est =+E)E5>'
a. Bt tests if the functional form of a regression model is misspecied.
b. Bt detects the presence of dummy variables in a regression model.
c. Bt helps in the detection of heteroskedasticity when the functional form of the
model is correctly specied.