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?

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

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?

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:

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

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

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?

Consider the following picture and suggest the model from the following list that best characterises the process:

Which one of the following best describes most series of asset prices?

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?