Basic Econometrics MCQs 3

This quiz is about Basic Econometrics MCQs with Answers, which cover the topics of Regression analysis, correlation, dummy variable, multicollinearity, heteroscedasticity, autocorrelation, and many other topics. Let’s start with the Basic Econometrics MCQs with Answers Quiz.

MCQs about Multicollinearity, Dummy Variable, Selection of Variables, Error in Variables, Autocorrelation, Time Series, Heteroscedasticity, Simultaneous Equations, and Regression analysis

1. If $d*<d_l$ then we

 
 
 
 

2. Zero tolerance or VIF equal to one indicate

 
 
 
 

3. The existence of a perfect or near-to-exact linear relationship among some or all explanatory variables of a regression model is called

 
 
 
 

4. An assumption about underlying the d statistics “The explanatory variable $X$’s are non-stochastic or fixed in ____________”

 
 
 
 

5. The log transformation is _____________ if some of the $Y$ and $X$ values are zero or negative.

 
 
 
 

6. The generalized least square (GLS) is an efficient procedure that weights each squared residual by:

 
 
 
 

7. Which combination of regressors might lead to perfect collinearity?

 
 
 
 

8. Which one assumption is not related to an error in the independent variable

 
 
 
 

9. The rule of thumb is if k is between 100 and 1000 then there is ____________ multicollinearity?

 
 
 
 

10. What is the meaning of the term heteroscedasticity?

 
 
 
 

11. Durbin and Watson have suggested a test to detect the presence of autocorrelation which applies to

 
 
 
 

12. In the case of perfect multicollinearity, the OLS estimates are

 
 
 
 

13. If $\rho$ is zero, then there is

 
 
 
 

14. If the calculated value of tolerance is 1, then there is an issue of

 
 
 
 

15. In case of perfect or near-to-perfect multicollinearity, the OLS estimates are

 
 
 
 

16. In the case of perfect multicollinearity, the $X’X$ is a _________.

 
 
 
 

17. The Glejser test is similar to ________.

 
 
 
 

18. In a regression model with 3 explanatory variables, there will be ________ auxiliary regressions.

 
 
 
 

19. The existence of a perfect or near-to-exact linear relationship among some or all explanatory variables of a regression model is called

 
 
 
 

20. A system which has an infinite number of solutions has _______

 
 
 
 

An application of different statistical methods applied to the economic data used to find empirical relationships between economic data is called Econometrics.

Econometrics means “Economic Measurement”. Econometrics is the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of statistical inference.

Econometrics can also be defined as the empirical determination of economic laws. Econometrics can be classified as (i) Theoretical Econometrics and (ii) Applied Econometrics.

(i) Theoretical Econometrics

Theoretical econometrics is concerned with developing appropriate methods for measuring economic relationships specified by econometric models. Theoretical econometrics leans heavily on mathematical statistics and must spell out the assumptions of methods (such as Least Squares), their properties, and what happens to these properties when one or more of the assumptions of the technique are not fulfilled.

(ii) Applied Econometrics

In applied econometrics, the tools of theoretical econometrics are used to study special fields(s) such as production function, investment function, demand and supply function, portfolio theory, etc.

Basic Econometrics MCQs

  • The rule of thumb is if k is between 100 and 1000 then there is _______ multicollinearity?
  • A system which has an infinite number of solutions has ______.
  • The existence of a perfect or near-to-exact linear relationship among some or all explanatory variables of a regression model is called
  • If $\rho$ is zero, then there is
  • Zero tolerance or VIF equal to one indicate
  • The log transformation is __________ if some of the $Y$ and $X$ values are zero or negative.
  • In the case of perfect multicollinearity, the OLS estimates are
  • In case of perfect or near-to-perfect multicollinearity, the OLS estimates are
  • The Glejser test is similar to _________.
  • If the calculated value of tolerance is 1, then there is an issue of
  • If $d*<d_l$ then we
  • An assumption about underlying the d statistics “The explanatory variable $X$’s are non-stochastic or fixed in __________”
  • Which combination of regressors might lead to perfect collinearity?
  • In the case of perfect multicollinearity, the $X’X$ is a ___________.
  • Durbin and Watson have suggested a test to detect the presence of autocorrelation which applies to
  • Which one assumption is not related to an error in the independent variable
  • The existence of a perfect or near-to-exact linear relationship among some or all explanatory variables of a regression model is called
  • What is the meaning of the term heteroscedasticity?
  • The generalized least square (GLS) is an efficient procedure that weights each squared residual by:
  • In a regression model with 3 explanatory variables, there will be __________ auxiliary regressions.

Types of Econometrics Data

Different type of data is used in Econometrics. There are three important types of data for empirical analysis:

  • Time Series Data
    A time series data is a set of observations on the values that a variable takes at different times. The time series data may be collected at regular time intervals such as daily, weekly, monthly, quarterly, annually, etc.
  • Cross-Sectional Data
    Cross-sectional data are data on one or more variables collected at the same point in time. Cross-sectional data has a problem of heterogeneity.
  • Pooled Data
    Pooled data is a combination of both time series and cross-sectional data.
Basic Econometrics MCQs

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MCQs Econometrics Quiz 2

This quiz is about Econometrics, which covers the topics of Regression analysis, correlation, dummy variable, multicollinearity, heteroscedasticity, autocorrelation, and many other topics. Let’s start with the MCQs Econometrics Quiz.

Please go to MCQs Econometrics Quiz 2 to view the test

An application of different statistical methods applied to the economic data used to find empirical relationships between economic data is called Econometrics.

Econometrics means “Economic Measurement”. Econometrics is the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of statistical inference.

Econometrics can also be defined as the empirical determination of economic laws. Econometrics can be classified as (i) Theoretical Econometrics and (ii) Applied Econometrics.

(i) Theoretical Econometrics

Theoretical econometrics is concerned with developing appropriate methods for measuring economic relationships specified by econometric models. Theoretical econometrics leans heavily on mathematical statistics and must spell out the assumptions of methods (such as Least Squares), their properties, and what happens to these properties when one or more of the assumptions of the technique are not fulfilled.

(ii) Applied Econometrics

In applied econometrics, the tools of theoretical econometrics are used to study special fields(s) such as production function, investment function, demand and supply function, portfolio theory, etc.

Types of Econometrics Data

Different type of data is used in Econometrics. There are three important types of data for empirical analysis:

  • Time Series Data
    A time series data is a set of observations on the values that a variable takes at different times. The time series data may be collected at regular time intervals such as daily, weekly, monthly, quarterly, annually, etc.
  • Cross-Sectional Data
    Cross-sectional data are data on one or more variables collected at the same point in time. Cross-sectional data has a problem of heterogeneity.
  • Pooled Data
    Pooled data is a combination of both time series and cross-sectional data.

Online MCQs Econometrics Quiz

  • In the case of homoscedasticity
  • In the presence of autocorrelation, the OLS estimates are no longer
  • Which one of the following is NOT an example of misspecification of functional form?
  • Which of the following tests is used to compare OLS estimates and WLS estimates?
  • Incorrect data transformation (ratio or difference, linear, log-linear) is also a source of
  • The conditional variance of $Y_i (Var(u_i))$ conditional upon the given $X_i$ does not remain the same regardless of the values taken by the variable $X$ when the problem of __________ exists.
  • Which of the following is an indication of the existence of multicollinearity in a model?
  • Which of the following is a consequence in case of imperfect multicollinearity?
  • Heteroscedasticity may _________ the variance and standard errors of the OLS estimates.
  • For the Durbin-Watson test:
  • The regression predictions are inefficient
  • Multicollinearity causes
  • The generalized least square (GLS) is an efficient procedure that weights each squared residual by:
  • In GLS the weights assigned to each observation are to its $\sigma_i$
  • Does the White General Heteroscedasticity test follow $\chi^2$ distribution with degrees of freedom?
  • If we drop a relevant variable from the model
  • In the case of multicollinearity, the confidence interval tends to be much ________, leading to the acceptance of the zero null hypothesis.
  • If the calculated value of tolerance is equal to 1, then it is an indication of
  • If $R^2$ between $X_2$ and $X_3$ approaches 1 (that is $r_{23}^2 \rightarrow 1$ ) then
  • Which of the following is true about autocorrelation?
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  • To be able to explain why a car skids, which of the following characteristics can be ignored?
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Best Computational Thinking Quiz 2

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  • What is categorizing a set of photographs by grouping them into similar piles based on their dates mean?
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  • Data visualization uses the following methods:
  • What is the first step while trying to solve a problem computationally?
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  • Which of the following is not a computational thinking practice?
  • Which of the following needs to be kept in mind while designing an algorithm?
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  • Which of the following will happen if we do not look for patterns?
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