A process, xt, which has a constant mean and variance, and zero autocovariance for all non-zero lags is best described as:
A. A white noise process
B. A covariance stationary process
C. An autocorrelated process
D. A moving average process
Giải bởi Vietjack
Chọn đáp án: A
Which of the following would probably NOT be a potential “cure” for non-normal residuals?
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?
What would be the consequences for the OLS estimator if autocorrelation is present in a regression model but ignored?
Which of the following sets of characteristics would usually best describe an autoregressive process of order 3 (i.e. an AR(3))?
If a regression equation contains an irrelevant variable, the parameter estimates will be
If the number of non-zero eigenvalues of the pi matrix under a Johansen test is 2, this implies that
A normal distribution has coefficients of skewness and excess kurtosis which are respectively:
If a residual series is negatively autocorrelated, which one of the following is the most likely value of the Durbin Watson statistic?
Consider the following picture and suggest the model from the following list that best characterises the process:
What is the optimal three-step ahead forecast from the AR(2) model given in question 14?
Which one of the following best describes most series of asset prices?
If the residuals of a model containing lags of the dependent variable are autocorrelated, which one of the following could this lead to?
Which of the following conditions must hold for the autoregressive part of an ARMA model to be stationary?
If a series, yt, follows a random walk (with no drift), what is the optimal 1-step ahead forecast for y?
If a series, yt, follows a random walk (with no drift), what is the optimal 1-step ahead forecast for y?
