GM Statistics

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. Which one is NOT the rule of thumb?

 Farrar and Glauber procedure

 Pair-wise correlation coefficients

 Wider Confidence Intervals

 High $R^2$ but few significant t-ratios

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

 White test

 Gold-Field Quandt test

 Runs test

 BPG test

3. The dummy variable trap is basically caused by:

 Measurement error

 Autocorrelation

 Heteroscedasticity

 Multicollinearity

4. Variance inflation factor is a common measure for:

 Detection of multicollinearity

 Testing homoscedasticity

 Detection of auto-correlation

 Testing normality

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

 Unbiased

 Infinite

 Biased

 Indeterminate

6. In the case of homoscedasticity, we have:

 Same error variances

 Different error mean

 Same error mean

 Different error variances

7. The high value of VIF indicates

 No multicollinearity

 Moderate multicollinearity

 Low multicollinearity

 High multicollinearity

8. he term Homoscedasticity means

 The errors are not linearly independent of one another

 The variance of the errors is not constant

 The variance of the dependent variable is constant

 The errors have non-zero mean

9. The range of partial correlation coefficient is:

 $0$ to $\infty$

 $–1$ to $+1$

 $0$ to $+1$

 $–1$ to $0$

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

 Zero

 Positive

 Negative

 Infinity

11. The dummy variable trap can be avoided by:

 Dropping a continuous explanatory variable

 Dropping intercept of the model

 Dropping slope of the model

 Add one more variable

12. The range of covariance between two variables is:

 $-1$ to $0$

 $-1$ to $+1$

 $0$ to $1$

 $-\infty$ to $+\infty$

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

 Greater than 1000

 Less than 10

 Greater than 10

 Greater than 30

14. Heteroscedasticity refers to situation in which:

 The variance of errors is different for different observations

 More than 2 $X$-variables are strongly correlated

 The variance of errors is the same for all observations

 Errors do not satisfy the zero mean assumption

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

 2

 1

 3

 4

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

 Dummy variable

 Quantitative variable

 Independent variable

 Continuous variable

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

 Perfect Multicollinearity

 Homoscedasticity

 Autocorrelation

 No Multicollinearity

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

 3

 8

 2

 1

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

 No specification error

 No Multicollinearity

 Homoscedasticity

 No autocorrelation

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

 Non-randomness of the response variable

 A greater level of heteroscedasticity

 Complete orthogonality

 Perfect multicollinearity

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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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The greatest scientists of their era, Abu Jaffar Muhammad Ibn Musa Al-Khawarizmi (780-850), Alan Mathison Turing (1912-1954), and John Von Neuman (1903-1957) took part in the invention of computers. A mathematician and a scientist (of France) Blaise Pascal developed the first mechanical computing machine called “Paccaline” in 1642. Leibnitz’s invented the machine and named it the “Multiplier Wheel” to perform basic arithmetic operations such as addition, subtraction, multiplication, and division. The “MUltiplier Wheel” machine was further modified in 1971.

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The greatest scientists of their era, Abu Jaffar Muhammad Ibn Musa Al-Khawarizmi (780-850), Alan Mathison Turing (1912-1954), and John Von Neuman (1903-1957) took part in the invention of computers. A mathematician and a scientist (of France) Blaise Pascal developed the first mechanical computing machine called “Paccaline” in 1642. Leibnitz’s invented the machine and named it the “Multiplier Wheel” to perform basic arithmetic operations such as addition, subtraction, multiplication, and division. The “MUltiplier Wheel” machine was further modified in 1971.

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