Econometrics- Chapter 3 Questions with Correct
Answers
The Classical Linear Regression Model (CLRM) Correct Answer-
Assumptions:
The model is linear in the parameters
The explanatory variables X are uncorrelated with the error term
The previous assumption leads to E(e|X)=0
The model is correctly specified (i.e. there is no specification bias). This
means that we included all relevant variables
There is no correlation between two error terms. i.e. cov(ui, uj)=0
u ~ N(0, s2).
Homoskedasticity Correct Answer-refers to the assumption that that the
dependent variable exhibits similar amounts of variance across the range
of values for an independent variable
Gauss-Markov Theorem Correct Answer-Given the assumptions of the
CLRM, the OLS estimators have minimum variance in the class of
linear estimators. That is, they are BLUE (best linear unbiased
estimators).
Properties of an OLS Correct Answer-Assumptions:
b1 and b2 are linear estimators; that is, they are linear functions for the
random variable Y.
They are unbiased, thus E(b)=b.
, The variance of the estimators is also unbiased.
b1 and b2 are efficient estimators; that is, the variance of each estimator
is less than the variance of any of the linear unbiased estimator.
t test Correct Answer-Degrees of freedom: This test has a distribution t
with n-2 degrees of freedom.
The level of significance a.
One tail or two tails?
r2 Correct Answer-measures the proportion of the total variation in Y
explained by the regression model
The Method of Least Squares Correct Answer-estimates the regression
parameters in such a way that the sum of the squared difference between
the actual Y values(i.e. the value dependent on the variable) and the
estimated Y values is as small as possible.
OLS Estimator Correct Answer-The estimators od the regression
parameters obtained by the method of least squares.
The variance of an estimator Correct Answer-An estimator being a
random variable, its variance, like the variance of any random variable,
measures the spread of the estimated values around the mean value of
the estimator.
Answers
The Classical Linear Regression Model (CLRM) Correct Answer-
Assumptions:
The model is linear in the parameters
The explanatory variables X are uncorrelated with the error term
The previous assumption leads to E(e|X)=0
The model is correctly specified (i.e. there is no specification bias). This
means that we included all relevant variables
There is no correlation between two error terms. i.e. cov(ui, uj)=0
u ~ N(0, s2).
Homoskedasticity Correct Answer-refers to the assumption that that the
dependent variable exhibits similar amounts of variance across the range
of values for an independent variable
Gauss-Markov Theorem Correct Answer-Given the assumptions of the
CLRM, the OLS estimators have minimum variance in the class of
linear estimators. That is, they are BLUE (best linear unbiased
estimators).
Properties of an OLS Correct Answer-Assumptions:
b1 and b2 are linear estimators; that is, they are linear functions for the
random variable Y.
They are unbiased, thus E(b)=b.
, The variance of the estimators is also unbiased.
b1 and b2 are efficient estimators; that is, the variance of each estimator
is less than the variance of any of the linear unbiased estimator.
t test Correct Answer-Degrees of freedom: This test has a distribution t
with n-2 degrees of freedom.
The level of significance a.
One tail or two tails?
r2 Correct Answer-measures the proportion of the total variation in Y
explained by the regression model
The Method of Least Squares Correct Answer-estimates the regression
parameters in such a way that the sum of the squared difference between
the actual Y values(i.e. the value dependent on the variable) and the
estimated Y values is as small as possible.
OLS Estimator Correct Answer-The estimators od the regression
parameters obtained by the method of least squares.
The variance of an estimator Correct Answer-An estimator being a
random variable, its variance, like the variance of any random variable,
measures the spread of the estimated values around the mean value of
the estimator.
