MCQs Econometrics Quiz 5

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 MCQs Econometrics test

MCQs about Econometrics for the preparation of Statistics and Econometrics related Examination and for the PPSC& FPSC and University job related to Lecturer in Statistics, and Statistical Officers.

1. The range of covariance between two variables is:

 
 
 
 

2. The variance of regression slopes becomes infinite in the case of:

 
 
 
 

3. he term Homoscedasticity means

 
 
 
 

4. Which of these tests is suitable for only a simple regression model.

 
 
 
 

5. Eigenvalues can be used for detecting violations of the assumption of:

 
 
 
 

6. Which one is NOT the rule of thumb?

 
 
 
 

7. The high value of VIF indicates

 
 
 
 

8. When measurement errors are present in the explanatory variable(s) then parameter estimates become

 
 
 
 

9. Generally, an acceptable value of variance inflation factor (VIF) is:

 
 
 
 

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

 
 
 
 

11. A variable showing presence or absence of something is known as:

 
 
 
 

12. In a multiple regression model, the ideal situation is:

 
 
 
 

13. In the case of homoscedasticity, we have:

 
 
 
 

14. The dummy variable trap can be avoided by:

 
 
 
 

15. The dummy variable trap is basically caused by:

 
 
 
 

16. If covariance between two variables is positive then their correlation coefficient will always be:

 
 
 
 

17. Heteroscedasticity refers to situation in which:

 
 
 
 

18. Variance inflation factor is a common measure for:

 
 
 
 

19. If we have a categorical variable with 4 categories, then how many dummy variables can be used in with intercept regression model

 
 
 
 

20. The range of partial correlation coefficient is:

 
 
 
 

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.

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