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ISYE 6402 Final - Part 1 WITH 100- SURE ANSWER
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ISYE 6402 Final - Part 1 WITH 100- SURE ANSWER
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10/6/24, 7:46 AMEUNICE
ISYE 6402 Final - Part 1 WITH QUESTIONS AND 100% SURE
ANSWERS
If the time series YtYt can be represented False. It would be E(Yt) = E(βt) + E(εt) = βt + 0.
as trend plus Gaussian white noise with
Yt=βt+ϵtYt=βt+ϵt , then its expectation is E( Yt
) = β.
If {Xt} is a stationary process, then its True
autocorrelation function has an expected
value of 0 for lag values greater than 0.
A time series generally can be True
decomposed into three components mt, st
and Xt. Where mt is the trend, st is the
seasonality, and Xt is a residual time
process after accounting for trend and
seasonality.
Var(X+Y)=Var(X)+Var(Y) for any X and Y FALSE (The statement would only be true if you knew the two variables were
variables. independent.)
If the mean of a time series doesn't depend False. (While constant mean is a necessary condition for stationarity, non-constant
on time t, then the time series is stationary. variance or significant auto-correlation may be present.)
For a random walk process St=∑tj=1Xjwhere True
Xt∼IID(0,σ2), we have that Var(St) > Var(St-1)
The mean of a random walk process False
depends on time.
All auto-regressive processes are False
stationary.
Consecutive observations in a white noise False
process are independent.
The random walk process is not variance True
stationary.
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