What is the optimal three-step ahead forecast from the AR(2) model given in question 14?

Which of the following would probably NOT be a potential “cure” for non-normal residuals?

If a residual series is negatively autocorrelated, which one of the following is the most likely value of the Durbin Watson statistic?

If a series, yt, follows a random walk (with no drift), what is the optimal 1-step ahead forecast for 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?

A normal distribution has coefficients of skewness and excess kurtosis which are respectively:

If a regression equation contains an irrelevant variable, the parameter estimates will be

A process, xt, which has a constant mean and variance, and zero autocovariance for all non-zero lags is best described as:

Which criticism of Dickey-Fuller (DF) -type tests is addressed by stationarity tests, such as the KPSS test?

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?

If the number of non-zero eigenvalues of the pi matrix under a Johansen test is 2, this implies that

What would be the consequences for the OLS estimator if autocorrelation is present in a regression model but ignored?

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?