93 câu trắc nghiệm Kinh tế lượng – Phần 1

KTL_002_C4_1: A researcher conducts a Breusch-Godfrey test for autocorrelation using 3 lags of the residuals in the auxiliary regression. The original regression contained 5 regressors including a constant term, and was estimated using 105 observations. What is the critical value using a 5% significance level for the LM test based on T \({R^2}\)?
○ 1.99
○ 2.70
● 7.81
○ 8.56.

KTL_002_C4_2: Which of the following would NOT be a potential remedy for the problem of multicollinearity between regressors?
○ Removing one of the explanatory variables
● Transforming the data into logarithms
○ Transforming two of the explanatory variables into ratios
○ Collecting higher frequency data on all of the variables

KTL_002_C4_3: Which of the following conditions must be fulfilled for the Durbin Watson test to be valid?
(i) The regression includes a constant term
(ii) The regressors are non-stochastic
(iii) There are no lags of the dependent variable in the regression
(iv) There are no lags of the independent variables in the regression

● (i), (ii) and (iii) only
○ (i) and (ii) only
○ (i), (ii), (iii) and (iv)
○ (i), (ii), and (iv) only

KTL_002_C4_4: If the residuals of a regression on a large sample are found to be heteroscedastic which of the following might be a likely consequence?
(i) The coefficient estimates are biased
(ii) The standard error estimates for the slope coefficients may be too small
(iii) Statistical inferences may be wrong

○ (i) only
● (ii) and (iii) only
○ (i), (ii) and (iii)
○ (i) and (ii) only

KTL_002_C4_5: The value of the Durbin Watson test statistic in a regression with 4 regressors (including the constant term) estimated on 100 observations is 3.6. What might we suggest from this?
○ The residuals are positively autocorrelated
● The residuals are negatively autocorrelated
○ There is no autocorrelation in the residuals
○ The test statistic has fallen in the intermediate region

KTL_002_C4_6: Which of the following is NOT a good reason for including lagged variables in a regression?
○ Slow response of the dependent variable to changes in the independent variables
○ Over-reactions of the dependent variables
○ The dependent variable is a centred moving average of the past 4 values of the series
● The residuals of the model appear to be non-normal

KTL_002_C4_7: What is the long run solution to the following dynamic econometric model?
\(\Delta {Y_t} = {\beta _1} + {\beta _2}\Delta {X_{2t}} + {\beta _3}\Delta {X_{3t}} + {U_t}\)
○ \({Y_t} = {\beta _1} + {\beta _2}{X_2} + {\beta _3}{X_3}\)
○ \({Y_t} = {\beta _1} + {\beta _2}{X_{2t}} + {\beta _3}{X_{3t}}\)
○ \({Y_t} = – \frac{{{\beta _2}}}{{{\beta _1}}}{X_2} – \frac{{{\beta _3}}}{{{\beta _1}}}{X_3}\)
● There is no long run solution to this equation

KTL_002_C4_8: Which of the following would you expect to be a problem associated with adding lagged values of the dependent variable into a regression equation?
● The assumption that the regressors are non-stochastic is violated
○ A model with many lags may lead to residual non-normality
○ Adding lags may induce multicollinearity with current values of variables
○ The standard errors of the coefficients will fall as a result of adding more explanatory variables

KTL_002_C4_9: A normal distribution has coefficients of skewness and excess kurtosis which are respectively
● 0 and 0
○ 0 and 3
○ 3 and 0
○ Will vary from one normal distribution to another

KTL_002_C4_10: Which of the following would probably NOT be a potential “cure” for non-normal residuals?
● Transforming two explanatory variables into a ratio
○ Removing large positive residuals
○ Using a procedure for estimation and inference which did not assume normality
○ Removing large negative residuals

KTL_002_C4_11: What would be the consequences for the OLS estimator if autocorrelation is present in a regression model but ignored?
○ It will be biased
○ It will be inconsistent
● It will be inefficient
○ All of a, b and c will be true.

KTL_002_C4_12: If OLS is used in the presence of heteroscedasticity, which of the following will be likely consequences?
(i) Coefficient estimates 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

● (ii) and (iv) only
○ (i) and (iii) only
○ (i), (ii), and (iii) only
○ (i), (ii), (iii), and (iv).

KTL_002_C4_13: If a residual series is negatively autocorrelated, which one of the following is the most likely value of the Durbin Watson statistic?
○ Close to zero
○ Close to two
● Close to four
○ Close to one.

KTL_002_C4_14: If the residuals of a model containing lags of the dependent variable are autocorrelated, which one of the following could this lead to?
○ Biased but consistent coefficient estimates
● Biased and inconsistent coefficient estimates
○ Unbiased but inconsistent coefficient estimates
○ Unbiased and consistent but inefficient coefficient estimates.

KTL_002_C4_15: Which one of the following is NOT a symptom of near multicollinearity?
○ The \({R^2}\) value is high
○ The regression results change substantively when one particular variable is deleted
● Confidence intervals on parameter estimates are narrow
○ Individual parameter estimates are insignificant

KTL_002_C4_16: Which one of the following would be the most appropriate auxiliary regression for a Ramsey RESET test of functional form?
● \({y_t} = {\alpha _0} + {\alpha _1}{\hat y_t}^2 + {v_t}\)
○ \({y_t}^2 = {\alpha _0} + {\alpha _1}{x_{2t}} + {\alpha _2}{x_{3t}} + {\alpha _4}x_{2t}^2 + {\alpha _5}x_{3t}^2 + {\alpha _6}{x_{2t}}{x_{3t}} + {v_t}\)
○ \({\hat u_t}^2 = {\alpha _0} + {\alpha _1}{\hat y_t}^2 + {v_t}\)
○ \({u_t} = {\alpha _0} + {\alpha _1}{x_{2t}} + {\alpha _2}{x_{3t}} + {\alpha _4}x_{2t}^2 + {\alpha _5}x_{3t}^2 + {\alpha _6}{x_{2t}}{x_{3t}} + {v_t}\)

KTL_002_C4_17: If a regression equation contains an irrelevant variable, the parameter estimates will be
● Consistent and unbiased but inefficient
○ Consistent and asymptotically efficient but biased
○ Inconsistent
○ Consistent, unbiased and efficient.

KTL_002_C4_18: Put the following steps of the model-building process in the order in which it would be statistically most appropriate to do them:
(i) Estimate model
(ii) Conduct hypothesis tests on coefficients
(iii) Remove irrelevant variables
(iv) Conduct diagnostic tests on the model residuals

○ (i) then (ii) then (iii) then (iv)
○ (i) then (iv) then (ii) then (iii)
● (i) then (iv) then (iii) then (ii)
○ (i) then (iii) then (ii) then (iv).