20 câu hỏi
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
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
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
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
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
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
Which of the following sets of characteristics would usually best describe an autoregressive process of order 3 (i.e. an AR(3))?
A slowly decaying acf, and a pacf with 3 significant spikes
A slowly decaying pacf and an acf with 3 significant spikes
A slowly decaying acf and pacf
An acf and a pacf with 3 significant spikes
A process, xt, which has a constant mean and variance, and zero autocovariance for all non-zero lags is best described as:
A white noise process
A covariance stationary process
An autocorrelated process
A moving average process
Which of the following conditions must hold for the autoregressive part of an ARMA model to be stationary?
All roots of the characteristic equation must lie outside the unit circle
All roots of the characteristic equation must lie inside the unit circle
All roots must be smaller than unity
At least one of the roots must be bigger than one in absolute value
If a series, yt, follows a random walk (with no drift), what is the optimal 1-step ahead forecast for y?
The current value of y
Zero
The historical unweighted average of y
An exponentially weighted average of previous values of y
If a series, yt, follows a random walk (with no drift), what is the optimal 1-step ahead forecast for y?
The current value of y
Zero
The historical unweighted average of y
An exponentially weighted average of previous values of y
If a series, yt, follows a random walk (with no drift), what is the optimal 1-step ahead forecast for y?
The current value of y
Zero
The historical unweighted average of y
An exponentially weighted average of previous values of y
Consider a series that follows an MA(1) with zero mean and a moving average coefficient of 0.4. What is the value of the autocorrelation function at lag 1?
0.4
0.34
1
It is not possible to determine the value of the autocovariances without knowing the disturbance variance
Consider the following picture and suggest the model from the following list that best characterises the process:
An AR(1)
An AR(2)
An ARMA(1,1)
An MA(3)
What is the optimal three-step ahead forecast from the AR(2) model given in question 14?
-0.1
0.27
-0.34
-0.31
Which criticism of Dickey-Fuller (DF) -type tests is addressed by stationarity tests, such as the KPSS test?
DF tests have low power to reject the null hypothesis of a unit root, particularly in small samples
DF tests are always over-sized
DF tests do not allow the researcher to test hypotheses about the cointegrating vector
DF tests can only find at most one cointegrating relationship
Which one of the following best describes most series of asset prices?
An independently and identically distributed (iid, i.e. “completely random”) process
A random walk with drift
An explosive process
A deterministic trend process
If there are three variables that are being tested for cointegration, what is the maximum number of linearly independent cointegrating relationships that there could be?
0
1
2
3
If the number of non-zero eigenvalues of the pi matrix under a Johansen test is 2, this implies that
There are 2 linearly independent cointegrating vectors
There are at most 2 linearly independent cointegrating vectors
There are 3 variables in the system
There are at least 2 linearly independent cointegrating vectors
If a Johansen “max” test for a null hypothesis of 1 cointegrating vectors is applied to a system containing 4 variables is conducted, which eigenvalues would be used in the test?
The largest 1
The Second largest
The Second smallest
The smallest