MCQs Econometrics-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 test

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

1. The regression predictions are inefficient

 In the case of autocorrelation

 In the case of heteroscedasticity

 In the case of multicollinearity

 In all situations

2. Does White General Heteroscedasticity test follow $\chi^2$ distribution with degrees of freedom?

 $k-1$

 $k+1$

 $k+2$

 $k-2$

3. Which one of the following is NOT an example of misspecification of functional form?

 Excluding a relevant variable from a linear regression model

 Using a linear specification when a double-logarithmic model would be more appropriate

 Using a linear specification when scales as a function of the squares of $X$

 Modeling $Y$ as a function of $X$ when in fact it scales as a function of $\frac{1}{X}$

4. 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.

 Heteroscedasticity

 Homoscedasticity

 Autocorrelation

 Multicollinearity

5. For Durbin-Watson test:

 If p-value < $\alpha$ then there is negative autocorrelation

 If p-value < $\alpha$ then we do not have the problem of autocorrelation in the model

 If p-value < $\alpha$ then there is positive autocorrelation

 If p-value < $\alpha$ then we have the problem of autocorrelation in the model

6. In GLS the weights assigned to each observation are _______ to its $\sigma_i$

 None of the given option

 Directory proportional

 Inversely proportional

 Remain constant

7. Multicollinearity causes

 Low R-squares

 None of the given option

 Biased Coefficient estimates

 Insignificant t-ratios

8. In case of homoscedasticity

 $ E(u_i^2) = 0 $

 $ E(u_i^2 ) \ne σ^2 = Var(u_i^2) $

 $ E(u_i^2 ) = σ^2 = Var(u_i^2) $

 $ E(u_i^2 ) = n \times p $

9. Heteroscedasticity may _____________ the variance and standard errors of the OLS estimates.

 Minor affect

 Highly affect

 Partially affect

 Not affect

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

 The expected value of $u_i$ given $X_i$

 The conditional variance of $u_i$ given $X_i$

 The square root of the inverse of the conditional variance of $u_i$ given $X_i$

 The inverse of the conditional variance of $u_i$ given $X_i$

11. Which of the following test is used to compare OLS estimates and WLS estimates?

 Hausman test

 The White test

 Breusch Godfrey test

 Durbin-Watson test

12. Which of the following is an indication of the existence of multicollinearity in a model?

 Significant regression coefficients

 High R-squared value and low t-ratios

 Multiple R-squared is 77%

 Sample correlation between regressors is 0.4

13. Which of the following is a consequence in case of imperfect multicollinearity?

 Significant regression coefficients

 $ \beta $-estimators are biased

 Residual of the model are not normal

 High standard errors of the regression coefficients

14. In the presence of autocorrelation, the OLS estimates are no longer

 Unbiased

 All of the given options

 Consistent

 Efficient

15. In correct data transformation (ratio or difference, linear, log-linear) is also source of

 None of the given options

 Multicollinearity

 Autocorrelation

 Heteroscedasticity

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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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