# Introduction to Econometrics Solved MCQs

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1.

## Two events, A and B, are said to be mutually exclusive if:

A. P(A | B) = 1
B. P(B | A) = 1
C. P(A and B) = 1
D. P(A and B) = 0
Answer» D. P(A and B) = 0
2.

## A Type I error occurs when we:

A. reject a false null hypothesis
B. reject a true null hypothesis
C. do not reject a false null hypothesis
D. do not reject a true null hypothesis
Answer» B. reject a true null hypothesis
3.

## What is the meaning of the term "heteroscedasticity"?

A. The variance of the errors is not constant
B. The variance of the dependent variable is not constant
C. The errors are not linearly independent of one another
D. The errors have non- zero mean
Answer» A. The variance of the errors is not constant
4.

## What would be then consequences for the OLS estimator if heteroscedasticity is present in a regression model but ignored?

A. It will be ignored
B. It will be inconsistent
C. It will be inefficient
D. All of a),c), b) will be true.
Answer» C. It will be inefficient
5.

## Which one of the following is NOT a plausible remedy for near multicollinearity?

A. Use principal components analysis
B. Drop one of the collinear variables
C. Use a longer run of data
D. Take logarithems of each of the variables
Answer» D. Take logarithems of each of the variables
6.

## What will be the properties of the OLS estimator in the presence of multicollinearity?

A. It will be consistent unbiased and efficient
B. It will be consistent and unbiased but not efficient
C. It will be consistent but not unbiased
D. It will not be consistent
Answer» A. It will be consistent unbiased and efficient
7.

## A sure way of removing multicollinearity from the model is to

A. Work with panel data
B. Drop variables that cause multicollinearity in the first place
C. Transform the variables by first differencing them
Answer» B. Drop variables that cause multicollinearity in the first place
8.

## Autocorrelation is generally occurred in

A. Cross-section data
B. Time series data
C. Pooled data
D. None of the above
9.

## The regression coefficient estimated in the presence of autocorrelation in the sample data are NOT

A. Unbiased estimators
B. Consistent estimators
C. Efficient estimators
D. Linear estimators
10.

## In the regression function y=α + βx +c

A. x is the regressor
B. Y is the regressor
C. x is the regressand
D. none of these
Answer» A. x is the regressor
11.

## The coefficient of determination, r2 shows

A. Proportion of the variation in the dependent variable Y is explained by the independent variable X
B. Proportion of the variation in the dependent variable X is explained by the independent variable Y
C. Proportion of the variation in ui is explained by the independent variable X
D. Both a and c
Answer» A. Proportion of the variation in the dependent variable Y is explained by the independent variable X
12.

## BLUE is

A. Best Linear Unbiased Estimator
B. Best Linear Unconditional Estimator
C. Basic Linear Unconditional Estimator
D. Both b and c
Answer» A. Best Linear Unbiased Estimator
13.

## Data on one or variables collected at a given point of time

A. Panel Data
B. Time series data
C. Pooled data
D. Cross-section data
14.

## The violation of the assumption of constant variance of the residual is known as

A. Heteroscedasticity
B. Multicollinearity
C. Homoscedasticity
D. Autocorrelation
15.

16.

A. 0.50%
B. 50%
C. 5%
D. 95%
17.

A. 3.125
B. 2.5
C. 3.75
D. 5.625
18.

## In the case of multicollinearity which test will be insignificant?

A. f test
B. t test
C. both a and b
D. both are significant
19.

## Hetroscedasticity is generally occurred in

A. Cross-section data
B. Time series data
C. Pooled data
D. None of the above
20.

## When there are both qualitative and quantitative variables are there in the model,

A. ANOVA
B. ANCOVA
C. CHI SQUARE
D. All of the above
21.

## When is the problem of dummy variable trap occur?

A. When we take dummy variables more than the categories
B. When we take dummy variables less than the categories
C. When we take dummy variables equal to the no of categories
D. Both a and c
Answer» D. Both a and c
22.

## Durbin Watson test is associated with:

A. Heteroscedasticity
B. Multicollinearity
C. Autocorrelation
D. Both a and c
23.

## All are the types of specification errors EXCEPT:

A. Omission of relevant variable
B. Inclusion of unnecessary variable
C. errors of measurement
D. over identified
24.

## White's test is used for the detection of ---------- -?

A. multicollinearity
B. hetroscedasticity
C. Autocorrelation
D. None of the above
25.

## Which one is not the assumption of OLS?

A. Perfect Multicollinearity
B. zero covariance between error terms
C. equal variance of disturbances
D. Mean value of disturbances is
26.

## Scaling a dependent variable in log form in the log-lin model will------------

A. change both the intercept and slope
B. change the slope but not the intercept
C. change the intercept but not the slope
D. intercept and slope both remains unchanged
Answer» C. change the intercept but not the slope
27.

## Individual respondents, focus groups, and panels of respondents are categorised as

A. Primary Data Sources
B. Secondary Data Sources
C. Itemized Data Sources
D. Pointed Data Sources
28.

## The scale applied in statistics which imparts a difference of magnitude and proportions is considered as

A. Exponential Scale
B. Goodness Scale
C. Ratio Scale
D. Satisfactory Scale
29.

A. F-test
B. t-test
C. Z-test
D. χ2-test
30.

## The successive trials are with replacement in

A. Hypergeometric distribution
B. Binomial distribution
C. Geometric distribution
D. None of these
31.

A. −1 and 0
B. −1 and 1
C. 1 and 0
D. 100 and -100
32.

## A discrete probability distribution may be represented by

A. Graph
B. Table
C. Mathematical Equation
D. All of These
33.

## Student’s t-distribution curve is symmetrical about mean, it means that

A. Odd Order Moments are Zero
B. Even Order Moments are Zero
C. Both (A) and (B)
D. None of (A) and (B)
Answer» A. Odd Order Moments are Zero
34.

## Which one is equal to explained variation divided by total variation?

A. Sum of squares due to regression
B. Coefficient of Determination
C. Standard Error of Estimate
D. Coefficient of Correlation