Multiple Choice Questions PDF

From 'Lecture Note 4: Time Series Tests on Data' What does ΔY represent in time series analysis? A. Autocorrelation B. First difference C. Unit root D. None of the above **Answer: B** Why is differencing often applied to time series? A. To estimate trends B. To induce stationarity C. To m...

From 'Lecture Note 4: Time Series Tests on Data' What does ΔY represent in time series analysis? A. Autocorrelation B. First difference C. Unit root D. None of the above **Answer: B** Why is differencing often applied to time series? A. To estimate trends B. To induce stationarity C. To measure autocorrelation D. None of the above **Answer: B** What does a high autocorrelation coefficient indicate? A. Stationarity B. Dependency between lagged variables C. Random data D. None of the above **Answer: B** What is the primary property of a stationary series? A. High correlation over time B. No memory of past values C. Increasing variance D. None of the above **Answer: B** What is the key feature of a unit root series? A. Nonstationarity B. Stationarity C. Independence D. None of the above **Answer: A** What is the significance of a random walk model? A. Predicts systematic changes B. Demonstrates unpredictability C. Highlights deterministic trends D. None of the above **Answer: B** What is the purpose of testing for stationarity? A. Validate OLS estimates B. Ensure reliable regression results C. Identify unit roots D. All of the above **Answer: D** What does the AR(1) model equation include? A. Current and past values of Y B. Autocorrelation values only C. Stationary parameters only D. None of the above **Answer: A** What does the lag length (p) signify in time series? A. Autocorrelation strength B. Relationship with past values C. Both A and B D. None of the above **Answer: C** What does “difference stationary” imply? A. First differences are nonstationary B. Differenced data is stationary C. No trends in original data D. None of the above **Answer: B**  From 'Lecture Note 5: Multivariate Regression Analysis for Time Series' What is the main purpose of multivariate regression analysis? A. Study one variable over time B. Examine relationships among multiple independent variables and a dependent variable C. Identify univariate trends D. None of the above **Answer: B** Which metric assesses a model’s goodness of fit? A. Coefficients B. R-squared C. Error terms D. P-values **Answer: B** What is a challenge in multivariate regression? A. Stationarity B. Multicollinearity C. Random errors D. Both A and B **Answer: B** What is the primary goal of transforming data to stationary in regression? A. Increase sample size B. Avoid misleading results C. Enhance temporal dependencies D. None of the above **Answer: B** What does a VIF value above 10 typically indicate? A. High autocorrelation B. Multicollinearity issues C. Low model reliability D. High prediction accuracy **Answer: B** What is the purpose of the Jarque-Bera test in regression? A. Test for stationarity B. Assess residual normality C. Evaluate multicollinearity D. Test autocorrelation **Answer: B** How does the Breusch-Godfrey LM test help in time series analysis? A. Detect heteroskedasticity B. Assess serial correlation C. Test for model specification D. None of the above **Answer: B** What is the null hypothesis in the Ramsey RESET test? A. Model specification is correct B. No multicollinearity exists C. Residuals are stationary D. Coefficients are insignificant **Answer: A** Which variable transformation can address heteroskedasticity? A. Differencing B. Logging C. Lagging D. None of the above **Answer: B** Which diagnostic identifies non-linear relationships in a model? A. Jarque-Bera test B. Ramsey RESET test C. Breusch-Pagan test D. White test **Answer: B**  From 'Lecture Note 6: Hypothesis Testing' What is the first step in hypothesis testing? A. Select a test statistic B. State the hypothesis C. Specify the level of significance D. Collect the sample **Answer: B** Which symbol is always included in the null hypothesis (H0)? A. > B. ≠ C. = D. None of the above **Answer: C** What does a two-tailed test examine? A. Whether the parameter is positive B. Whether the parameter is different from zero C. Whether the parameter is less than zero D. Whether the parameter is greater than zero **Answer: B** What happens if the test statistic exceeds the critical value? A. Fail to reject H0 B. Accept H0 C. Reject H0 D. None of the above **Answer: C** What is a Type I error in hypothesis testing? A. Accepting a true null hypothesis B. Rejecting a true null hypothesis C. Failing to reject a false null hypothesis D. Rejecting a true alternative hypothesis **Answer: B** What does statistical significance ensure? A. The result is economically meaningful B. The result is different from random chance C. The result is always practical D. None of the above **Answer: B** In hypothesis testing, what does a significance level of 5% mean? A. There is a 5% chance of rejecting a true H0 B. There is a 5% chance of accepting a true H0 C. The p-value must be exactly 0.05 D. None of the above **Answer: A** Which of the following is a one-tailed hypothesis test example? A. H0: µ = 0 versus Ha: µ ≠ 0 B. H0: µ ≥ 0 versus Ha: µ < 0 C. H0: µ ≤ 0 versus Ha: µ ≠ 0 D. None of the above **Answer: B** What does economic significance address? A. Statistical p-values only B. Feasibility and real-world impact C. Always rejecting H0 D. None of the above **Answer: B** What does rejecting H0 indicate in a test? A. H0 is definitely true B. Ha is supported by evidence C. Data is unreliable D. None of the above **Answer: B**  From 'Basics of Homoskedasticity and Heteroskedasticity' What does homoskedasticity mean in regression analysis? A. Errors have constant variance B. Errors have changing variance C. Model assumptions are violated D. None of the above **Answer: A** Why is homoskedasticity important for OLS estimators? A. Ensures efficiency B. Validates normality C. Confirms linearity D. None of the above **Answer: A** Which test is used to detect heteroskedasticity? A. Breusch-Pagan test B. Jarque-Bera test C. RESET test D. All of the above **Answer: A** What does a p-value below 0.05 in heteroskedasticity tests indicate? A. Homoskedasticity exists B. Heteroskedasticity exists C. Model is linear D. None of the above **Answer: B** What is the main consequence of heteroskedasticity? A. Biased coefficient estimates B. Inefficient estimators C. Invalid predictions D. All of the above **Answer: D** What is a common method to remedy heteroskedasticity? A. Weighted least squares B. Increasing sample size C. Ignoring the problem D. None of the above **Answer: A** What shape in residual plots suggests heteroskedasticity? A. Funnel shape B. Uniform scatter C. Symmetrical scatter D. None of the above **Answer: A** Which method provides valid standard errors in the presence of heteroskedasticity? A. OLS regression B. Robust standard errors C. Unweighted regression D. None of the above **Answer: B** How does the White test differ from the Breusch-Pagan test? A. It requires no functional form assumptions B. It is more complex C. It tests for normality D. None of the above **Answer: A** What is the null hypothesis in heteroskedasticity tests? A. Errors have constant variance B. Errors have increasing variance C. Errors are correlated D. None of the above **Answer: A**