180 câu trắc nghiệm Kinh tế lượng – Phần 5
Chapter 14: Introduction to Time Series Regression and Forecasting
KTL_001_C14_1: Pseudo out of sample forecasting can be used for the following reasons with the exception of
○ giving the forecaster a sense of how well the model forecasts at the end of the sample.
○ estimating the RMSFE.
● analyzing whether or not a time series contains a unit root.
○ evaluating the relative forecasting performance of two or more forecasting models.
KTL_001_C14_2: One reason for computing the logarithms (ln), or changes in logarithms, of economic time series is that
○ numbers often get very large.
○ economic variables are hardly ever negative.
● they often exhibit growth that is approximately exponential.
○ natural logarithms are easier to work with than base 10 logarithms.
KTL_001_C14_3: The AR(p) model
○ is defined as \({Y_i} = {\beta _0} + {\beta _p}{Y_{t – p}} + {u_i}\).
● represents Yt as a linear function of p of its lagged values.
○ can be represented as follows: \({Y_i} = {\beta _0} + {\beta _1}{X_t} + {\beta _p}{Y_{t – p}} + {u_i}\).
○ can be written as \({Y_i} = {\beta _0} + {\beta _1}{Y_{t – 1}} + {u_{t – p}}\).
KTL_001_C14_4: The Granger Causality Test
● uses the F-statistic to test the hypothesis that certain regressors have no predictive content for the dependent variable beyond that contained in the other regressors.
○ establishes the direction of causality (as used in common parlance) between X and Y in addition to correlation.
○ is a rather complicated test for statistical independence.
○ is a special case of the Augmented Dickey-Fuller test.
KTL_001_C14_5: You should use the QLR test for breaks in the regression coefficients, when
○ the Chow F-test has a p value of between 0.05 and 0.10.
● the suspected break data is not known.
○ there are breaks in only some, but not all, of the regression coefficients.
○ the suspected break data is known.
KTL_001_C14_6: The Bayes-Schwarz Information Criterion (BIC) is given by the following formula
● \(BIC\left( p \right) = \ln \left[ {\frac{{SSR(p)}}{T}} \right] + \left( {p + 1} \right)\frac{{\ln (T)}}{T}\)
○ \(BIC\left( p \right) = \ln \left[ {\frac{{SSR(p)}}{T}} \right] + \left( {p + 1} \right)\frac{2}{T}\)
○ \(BIC\left( p \right) = \ln \left[ {\frac{{SSR(p)}}{T}} \right] – \left( {p + 1} \right)\frac{{\ln (T)}}{T}\)
○ \(BIC\left( p \right) = \ln \left[ {\frac{{SSR(p)}}{T}} \right] * \left( {p + 1} \right)\frac{{\ln (T)}}{T}\)
KTL_001_C14_7: The Akaike Information Criterion (AIC) is given by the following formula
○ \(AIC\left( p \right) = \ln \left[ {\frac{{SSR(p)}}{T}} \right] + \left( {p + 1} \right)\frac{{\ln (T)}}{T}\)
● \(AIC\left( p \right) = \ln \left[ {\frac{{SSR(p)}}{T}} \right] + \left( {p + 1} \right)\frac{2}{T}\)
○ \(AIC\left( p \right) = \ln \left[ {\frac{{SSR(p)}}{T}} \right] + \frac{{p + 2}}{T}\)
○ \(AIC\left( p \right) = \ln \left[ {\frac{{SSR(p)}}{T}} \right]*\left( {p + 1} \right)\frac{2}{T}\)
KTL_001_C14_8: The BIC is a statistic
○ commonly used to test for serial correlation
○ only used in cross-sectional analysis
○ developed by the Bank of England in its river of blood analysis
● used to help the researcher choose the number of lags in an autoregression
KTL_001_C14_9: The AIC is a statistic
○ that is used as an alternative to the BIC when the sample size is small (T < 50)
○ often used to test for heteroskedasticity
● used to help a researcher chose the number of lags in a time series with multiple predictors
○ all of the above
KTL_001_C14_10: The formulae for the AIC and the BIC are different. The
○ AIC is preferred because it is easier to calculate
● BIC is preferred because it is a consistent estimator of the lag length
○ difference is irrelevant in practice since both information criteria lead to the same conclusion
○ AIC will typically underestimate p with non-zero probability