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MCQ’s Subject:Introductory Econometrics Answers are highlighted in yellow color
- Which of the following assumptions are required to show the consistency, unbiasedness and efficiency of the OLS estimator? i) E(ut) = 0 ii) Var(ut) = σ^2 ii) Cov(ut, ut-j) = 0 and j iv) ut~ N(0, σ²) a) ii and iv only b) i and iii only c) i, ii, and iii only d) i, ii, iii and iv
- Which of the following may be consequences of one or more of the CLRM assumptions being violated? i) The coefficient estimates are not optimal ii) The standard error estimates are not optimal ii) The distributions assumed for the test statistics are inappropriate įv) Conclusions regarding the strength of relationships between the dependent and independent variables may be invalid. a)ii and iv only b) i and iii only c) i, ii, and iii d) I,ii,iii and iv. 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 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.
- Near multicollinearity occurs when a)Two or more explanatory variables are perfectly correlated with one another b)The explanatory variables are highly correlated with the error term c)The explanatory variables are highly correlated with the dependent variable d)Two or more explanatory variables are highly correlated with one another 6)Which of the following are plausible approaches to dealing with a model that exhibits heteroscedasticity? a) Take logarithms of each of the variables b) Add lagged values of the variables to the regression equation c) Use suitably modified standard error
d)Use a generalized least square procedure a)i and iv b)i and iii ci, ii, and iv only d) i, ii, iii, and iv. 7)Negative residual autocorrelation is indicated by which one of the following a)A cyclical pattern in the residual b)An alternating pattern in the residuals c)A complete randomness in the residuals d) Residuals is that are all close to zero 8)If OLS is used in the presence of autocorrelation, which of the following will be like consequences? i)Coefficient estimate may be misleading ii) Hypothesis tests could reach the wrong conclusions iii) Forecasts made from the model could be biased iv)Standard errors may inappropriate a)ii and iv b)i and iii c)I,ii and iii d)i ii, iii and iv 9)Which of the following are plausible approaches to dealing with residual autocorrelation? a)Take logarithms of each of the variables b)Add lagged values of the variables to the regression equation c) Use dummy variables to remove outlying observations d)Try a model in first differenced form rather than in levels a)ii and iv b)i and iii ci, ii, and iii only d) i, ii, iii, and iv. 10)Which of the following could result in autocorrelated residuals? i)Slowness of response of the dependent variable to changes in the values of the independent variables ii)Over-reaction of the dependent variable to changes in the independent variables iii)Omission of relevant explanatory variables that are autocorrelated iv)Outliers in the data a. ii and iv b. i and iii c. i ,ii and iii d. I,ii,iii,iv
- Including relevant lagged values of the dependent variable on the right hand side of a regression equation could lead to which one of the following? i)Biased but consistent coefficient estimate ii)Biased and inconsistent coefficient estimate iii) Unbiased but inconsistent coefficient estimate iv) Unbiased and consistent but inefficient coefficient estimate
17)The assumption that the error terms in a regression model follow the normal distribution with zero mean and constant variance is required a)Point estimation of the parameters b)Hypothesis testing and inference c)Estimation of the regression model using OLS method d)Both a and b 18)One of the assumption of CLRM is that the number of observations in the sample must be greater the number of a)Regressor b)Regressands c)Dependent variable d)Dependent and independent variable 19)If there exist high multicollinearity, then the regression coefficients are, a) Determinate b)Indeterminate c)Infinite values d)Small negative values 20)If multicollinearity is perfect in a regression model then the regression coefficients of the explanatory variables are a) Determinate b)Indeterminate c)Infinite values d)Small negative values
- If multicollinearity is perfect in a regression model the standard errors of the regression coefficients are a) Determinate b)Indeterminate c)Infinite values d)Small negative values 22)The coefficients of explanatory variables in a regression model with less than perfect multicollinearity cannot be estimated with great precision and accuracy. This statement is a)Always true b)Always false c)Sometimes true d)Nonsense statement 23)In a regression model with multicollinearity being very high, the estimators a Are unbiased b. Are consistent c. Standard errors are correctly estimated d All of the above
- Micronumerosity in a regression model according to Goldberger refers to a) A type of multicollinearity b). Sample size n being zero c) Sample size n being slightly greater than the number of parameters to be estimated d)Sample size n being just smaller than the number of parameters to be estimated
- Multicollinearity is essentially a a. Sample phenomenon b. Population phenomenon c. Both a and b d. Either a or b 26)Which of the following statements is NOT TRUE about a regression model in the presence of multicol-linearity a. t ratio of coefficients tends to be significantly b. R^2 is high c. OLS estimators are not BLUE d. OLS estimators are sensitive to small changes in the data 27).Which of these is NOT a symptom of multicollinearity in a regression model a. High R^2 with few significant t ratios for coefficients b. High pair-wise correlations among regressors c. High R^2 and all partial correlation among regressors d. VIF of a variable is below 10 28). 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 d. Obtaining additional sample data
- Assumption of 'No multicollinearity' means the correlation between the regresand and regressor is a. High b. Low C. Zero d. Any of the above
- An example of a perfect collinear relationship is a quadratic or cubic function. This statement is a. True b. False c. Depends on the functional form d. Depends on economic theory 31.Multicollinearity is limited to a Cross-section data b. Time series data c. Pooled data d. All of the above
- Multicollinearity does not hurt is the objective of the estimation is a. Forecasting only b. Prediction only C. Getting reliable estimation of parameters
40). 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 41)Estimating the coefficients of regression model in the presence of autocorrelation leads to this test being NOT valid a)t test b)F test c)Chi-square test d) All of the above 42)There are several reasons for serial correlation to occur in a sample data. Which of these is NOT a). Business cycle b). Specification bias c) Manipulation of data d). Stationary data series
- When supply of a commodity, for example agricultural commodities, react to price with a lag of one time period due to gestation period in production, such a phenomenon is referred to as a. Lag phenomenon b. Cobweb phenomenon e. Inertia d. Business cycle 44). If in our regression model, one of the explanatory variables included is the lagged value of the dependent variable, then the model is referred to as a. Best fit model b. Dynamic model C. Autoregressive model d. First-difference form 45). A time series sample data is considered stationary if the following characteristics of the series are time invariant: d. Mean b. Variance c. Covariance d. All of the above
- By autoconrelation we mean a) That the residuals of a regression model are not independent b) That the residuals of a regression model are related with one or more of the regressors c) That the squared residuals of a regression model are not equally spread d That the variance of the residuals of a regression model is not constant for all observations
- The p value is a)2 minimum power
b)2 plus power c)the power d)none of these
- 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 49)The full form of CLR is a)Class line ratio b)Classical linear regression c)Classical linear relation d) none of the above 50)Locus of the conditional mean of the dependent variable for the fixed values of the explanatory variable a)Indifference curve b)Population regression curve c)Production Possibility curve d)None of these. 51)Sample regression function is the estimated version of the___________ a)Estimated version of population regression function b)Estimated version of population correlation function c)Not an estimated version of population regression function d)Both b and c 52)Full form of OLS a)Ordinary least square method b)Ordinary least statistical method c)Ordinary least sample method d) Both b and c 53)The conditional mean of Y is a) The expected value of Y for given values of the independent variables, Xi b) The expected value of Y for given values of the independent variables, ui. c) The expected value of Y for given values of the independent variables, Yi. d)Both b and c
60)Data on one or variables collected at a given point of time a)Time series data b)Cross-section data b)Pooled data c)Panel data d) Both and b 61)i)Pooled data imply combination of time series and cross sectional data. ii) Panel data is special type of pooled data in which the same cross-section unit is surveyed over time a)Only a is correct b)Only b is correct c)Both a and b are wrong d)Both a and b are correct… 62)i)Least square estimators. Unbiased, minimum variance, Linear is BLUE ii) Least square estimators. Biased, minimum variance, Linear is BLUE iii Least square estimators. Unbiased, maximum variance, Linear is BLUE a)Only a… b)Only b C) Both a and b d) Only c 63)The statistical properties of OLS estimators are a)Linearity, Unbiasedness, and minimum variance b) Linearity and Unbiasedness c) Unbiasedness, and minimum variance d) Linearity and minimum variance 64)Procedure for testing Hypothesis i)Set up hypothesis ii)Selecting the level of significance iii)Select the suitable test statistic iv)Determining the critical region v)Performing computations vi)Decision- making a)i, ii, and iv b)i,ii,iii,iv c)i,iii,iv d)i,ii,iii,iv,v,vi..
65)Method of ordinary least square is attributed to a)Carl Friedrick Gauss b)William Sealy Goss c)Durbin Watson d) Both b and c
- r^2 refers to a)Coefficient of determination b)Coefficient of correlation c)Square of correlation coefficient d)Both a and c 67)The coefficient of determination shows, a)Variation in the dependent variable Y is explained by the independent variable X b) Variation in the independent variable Y is explained by the dependent variable X. c)Both a and b are correct d)Both a and b are wrong 68)The violation of the assumption of constant variance of the residual is known as a)Heteroscedasticity b)Homoscedasticity c)Both a and b are correct d)Both a and b are wrong 69)Multicollinearity is used to denote, a)The presence of linear relationships among explanatory variables b) The presence of non-linear relationships among explanatory variables c) The presence of linear relationships among dependent variables d) The presence of linear relationships among endogenous variables 69)What is ui? a)Errror term b)Disturbance term c)Both a and b are correct d)Both a and b are wrong 70)Hoomoscedasticity means a)Constant variance b)Minimum variance c)Maximum variance d)Zero variance
79)The term regression was coined by a)Francis Galton b)Karl pearson c)Carl Friedrick Gauss.. d)William Sealy Goss 80)Given the sample, each estimator will provide only a single point value of the relevant population parameter is a)Point estimator b)Interval estimator c)Least square estimator d)Both b and c 81)Assumption of CLRM a)No Autocorrelation between error term b)Positive correlation c)Negative correlation d)Both b and d are correct
- Reliability of a point estimation is measured by its a. Standard deviation b. Standard normal curve c. Standard error d. Coefficient of determination 83). Rejecting a true hypothesis results in this type of error a. Type I error b. Type II error c. Structural error d.Hypothesis error 8 3. Accepting a false hypothesis results in this type of error a. Type I error b. Type II error c. Structural error d. Hypothesis error 8 4. The end points of the confidence interval (ⱽβ 2 + δ) are known as a. Critical error b. Confidence limit
c. Confidence value d. Limiting value 8 5. The α in a confidence interval given by Pr(ⱽβ 2 - δ≤ⱽβ 2 - δ) = 1-α is known as b. Level of confidence C. Level of significance d. Significance coefficient 8 6. The (1-α) in a confidence interval given by Pr(ⱽβ 2 - δ≤ⱽβ 2 - δ) = 1-α is known as a. Confidence coefficient b. Level of confidence c. Level of significance d. Significance coefficient 8 7. The α in a confidence interval given by Pr(ⱽβ 2 - δ≤ⱽβ 2 - δ) = 1-α should be, a.< b. > 0 c.< d. >0 and < 8 8. In confidence interval estimation, α = 5%, this means that this interval includes the true β with probability of a.5% b.50% C. 95% d. 45% 8 9. The confidence interval constructed for β 2 will be same irrespective of the sample analyzed. This statement is a. True b. False c. May be true d. Nonsense statement 9 0. The larger the standard error of the estimator, the greater is the uncertainty of estimating the true value of the unknown parameters. This statement is a. True b. False c. May be true d. Nonsense statement 91 Standard error of an estimator is a measure of a. Population estimator b. Precision of the estimator c. Power of the estimator
99)In sample regression function, the observed Yi can be expressed asYi= ⱽYi+ ⱽβ 1 +ⱽβ 2 X+ⱽui. This statement is a. True b. False c. Depends on ⱽβ 2 d.Depends on ⱽYi 100)The statement that-There can be more than one SRF representing a population regression function is a.Always true b.Always false c.Sometimes true, sometimes false d.Nonsense statement
1 - Heteroscedasticity is more common in
(A) Time-series data than cross-Sectional data (B) Cross-sectional data than time-series data
(C)Panel data (D) Meta Data
2 - Which of the following statements is true about autocorrelation?
(A) Consecutive values of Errors term or observations are correlated.
(B) Regressors are correlated.
(C) The conditional distribution of error terms is constant.
(D) Consecutive errors or observations are uncorrelated
3 - Which of the action does not make sense to take in order to struggle against
multicollinearity?
(A) Add more regressors in the model.
(B) Increase more observations.
(C) Decrease the number of regressors in the model.
(D) None of these
4 - Which of the following assumptions are required to show the consistency, unbiasedness
and efficiency of the OLS estimator? i) E(ut) = 0 ii) Var(ut) = σ 2 ii) Cov(ut, ut=j) = 0 and j
iv) ut~ N(0, σ²)
(A) ii and iv only (B) i and iii only (C) i, ii, and iii only (D) i, ii, iii and iv
5 - Which of the following may be consequences of one or more of the CLRM assumptions
being violated?
(A) The coefficient estimates are not optimal (B) The standard error estimates are not optimal
(C) The distributions assumed for the test statistics are inappropriate (D) All of the above.
6 - 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
7 - 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
8 - One of the assumptions of CLRM is that the number of observations in the sample must
be greater the number of
(A)Regressor (B)Regressands (C)Dependent variable (D)Dependent and independent variable
9 - If there exist high multicollinearity, then the regression coefficients are,
(A) Determinate (B)Indeterminate (C)Infinite values (D)Small negative values
1 - A specific value calculated from sample is called
(A) Estimator (B) Estimate (C) Estimation (D) Bias
2 - If E(θ)=θ then θ is called
(A) Biased estimator (B) Unbiased estimator (C) Positively Biased (D) Negatively Biased
3 - Accepting a false hypothesis results in this type of error
(A) Type I error (B) Type II error (C) Structural error (D) Hypothesis error
4 - 1−α is called
(A) Level of significance (B) Power of the test (C) Level of confidence (D) Error
5 - Type-I error will occur if an innocent person is
(A) Arrested by police (B) Not arrested (C)Police care for him (D) None of these
6 - The region of acceptance of H 0 is called
(A) Critical region (B)Test statistics (C) Type-I error (D) Acceptance region
7 - The probability of rejecting a false H 0 is
(A)Level of significance (B)Level of confidence (C)Critical region (D) Power of test
8 - In confidence interval estimation, α = 5%, this means that this interval includes the true
β with probability of
(A) 5% (B) 50% (C) 95% (D) 45%
9 - The confidence interval constructed for β2 will be same irrespective of the sample
analyzed. This statement is
(A) True (B) False (C) May be true (D) Nonsense statement
10 - Which one assumption is not related to error in explanatory variables?
(A) Cov(ui,εi)=0Cov(ui,εi)=0 (B) Cov(Z,Xi)≠0Cov(Z,Xi)≠0 (C) Cov(ui,wi)=0Cov(ui,wi)=
(D) E(Zi)=0E(Zi)=
11 - Which of the following may be consequences of one or more of the CLRM assumptions
being violated?
(i) The coefficient estimates are not optimal
(ii) The standard error estimates are not optimal
(ii) The distributions assumed for the test statistics are inappropriate
(įv) Conclusions regarding the strength of relationships between the dependent and
independent variables may be invalid.
(A)ii and iv only (B) i and iii only (C) i, ii, and iii (D) I,ii,iii and iv.
12 - 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,B,C will be
true
13 - Near multicollinearity occurs when
(A) Two or more explanatory variables are perfectly correlated with one another
(B)The explanatory variables are highly correlated with the error term
(C)The explanatory variables are highly correlated with the dependent variable
(D)Two or more explanatory variables are highly correlated with one another
14 - The formula used to estimate a parameter is called
(A) Estimate (B) Estimation (C)Estimator (D) Confidence Interval
15 - When supply of a commodity, for example agricultural commodities, react to price
with a lag of one time period due to gestation period in production, such a phenomenon is
referred to as
(A) Lag phenomenon (B) Cobweb phenomenon (C) Inertia (D) Business cycle
16 - If in our regression model, one of the explanatory variables included is the lagged value
of the dependent variable, then the model is referred to as
(A). Best fit model (B). Dynamic model (C). Autoregressive model (D) First-difference form
17 - 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
18 - The full form of CLR is
(A)Class line ratio (B)Classical linear regression (C)Classical linear relation
(D) none of the above
19 - Student ‘t’ test was formulated by
(A)William Sealy Gosset (B)Carl Friedrick Gauss (C)Durbin Watson (D) Both b and c
20 - Information about numerical values of variables from period to period is
(A)Time series data (B)Cross-section data (C)Pooled data (D) Both a and b
