ISYE 6402 FINAL - PART 2 EXAM QUESTIONS WELL ANSWERED LATEST UPDATE 2026
A white noise process has zero auto-covariance for all lags including lag zero. - Answers False
If a time series is Gaussian then it is non-stationary. - Answers False
AR(p) processes are always invertible. - Answers True
The ACF plot can always be used to determine the order q of ARMA(p,q) models. - Answers False
In some cases, the PACF plot can be used to determine the order p of ARMA(p,q) models. - Answers
True
The PACF of an ARMA(p,q) process cuts off after lag p. - Answers False. (The PACF of an ARMA(p,q)
process tails off, while the PACF of an AR(p) process cuts off after lag p.)
MA(q) processes are always causal. - Answers True
If Xt and Ytϕ1 are independent AR(1) processes, then Xt+Yt ϕ1 is an AR(2) process. - Answers False.
(The order of the sum of two independent AR processes is not necessarily the sum of each individual
processes' order.)
Let Wt be a white noise process. Then Xt=Wt−Wt−1 is stationary. - Answers True
An ARIMA(p,0,q) model is always stationary. - Answers False
There is no auto-correlation in an ARIMA(1,d,q) process. - Answers False
The best ARIMA(p,d,q) model to choose is always the one with the lowest AIC score. - Answers False.
(If two models have very similar scores but different coefficient counts, it is often best practice to
select the simpler model. Additionally, we may wish to use a measure that penalizes coefficient
counts more (e.g. BIC).)
A pure MA(q) process always has constant variance. - Answers True. (All MA(q) processes are
stationary.)
A pure AR(p) process always has significant autocorrelation. - Answers True.
If an ARMA(p,q) process in causal and invertible then is must also be stationary. - Answers True
A white noise process has zero auto-covariance for all lags including lag zero. - Answers False
If a time series is Gaussian then it is non-stationary. - Answers False
AR(p) processes are always invertible. - Answers True
The ACF plot can always be used to determine the order q of ARMA(p,q) models. - Answers False
In some cases, the PACF plot can be used to determine the order p of ARMA(p,q) models. - Answers
True
The PACF of an ARMA(p,q) process cuts off after lag p. - Answers False. (The PACF of an ARMA(p,q)
process tails off, while the PACF of an AR(p) process cuts off after lag p.)
MA(q) processes are always causal. - Answers True
If Xt and Ytϕ1 are independent AR(1) processes, then Xt+Yt ϕ1 is an AR(2) process. - Answers False.
(The order of the sum of two independent AR processes is not necessarily the sum of each individual
processes' order.)
Let Wt be a white noise process. Then Xt=Wt−Wt−1 is stationary. - Answers True
An ARIMA(p,0,q) model is always stationary. - Answers False
There is no auto-correlation in an ARIMA(1,d,q) process. - Answers False
The best ARIMA(p,d,q) model to choose is always the one with the lowest AIC score. - Answers False.
(If two models have very similar scores but different coefficient counts, it is often best practice to
select the simpler model. Additionally, we may wish to use a measure that penalizes coefficient
counts more (e.g. BIC).)
A pure MA(q) process always has constant variance. - Answers True. (All MA(q) processes are
stationary.)
A pure AR(p) process always has significant autocorrelation. - Answers True.
If an ARMA(p,q) process in causal and invertible then is must also be stationary. - Answers True
