ISYE 6402 MIDTERM PREP EXAM QUESTIONS &
DETAILED SOLUTIONS PASSED ALREADY
GRADED A+
1. Getting a 3 variable VAR model first matrix: first row are coefficients for Xt1, second
row are
from summary(model) output of coefficients for Xt2, etc...
a VAR(1) model
second matrix is Xt-1, i b/c this is a VAR(1)
model last matrix are the constants
eta_t is covariance matrix, direct copy
2. (c) Based on the fitted contemporaneous cross-correlation is NOT present
mod- el, is there if the variance-covariance matrix is a diagonal
contemporane- ous cross- matrix
correlation? Is there there is lagged correlation if the order p of the VAR(p)
lagged cross-correlation? model
Is there lagged auto- >0
correlation? Explain.
3. T/F - Differencing the data might
True not make the series
stationary in the presence of
cointegration.
4. Cointegration and See image
long-run equilibrium
5. Does cov(x,x) = var(x)? You betcha
1/
23
,ISYE 6402 MIDTERM PREP EXAM QUESTIONS &
DETAILED SOLUTIONS PASSED ALREADY
GRADED A+
6. Autocovariance T/F see image
7. T/F - The AR(1) process is FALSE! the absolute value of phi must lie b/w -1 and
causal if and only if the 1
autoregressive parameter
phi is between 0 and
2/
23
, ISYE 6402 MIDTERM PREP EXAM QUESTIONS &
DETAILED SOLUTIONS PASSED ALREADY
GRADED A+
1. However, it is always
invert- ible.
8. T/F - A linear process is a FALSE - the moving average is a special case of a
spe- cial case of the linear process.
moving average model.
9. T/F - A guassian time false - gaussian processes can have varying means
series is always stationary
10. T/F 'In autoregressive FALSE - there are no analogies of
models the current value of dependent/independent variables w/ AR models, as
dependent variable is there are w/ regression models
influenced by past values
of both dependent and
independent variables.'
11. in AR models the current False - We don't have dependent and independent
val- ue of the dependent variables in AR models like we do in regression
variable is affected by the models
past values of both
dependent and indepen-
dent variables
12. how do ACF and PACF differ? TBD
13. what in an ACF plot would stationary?
show non-stationarity?
14. what in an ACF and PACF
plot would show
3/
23
DETAILED SOLUTIONS PASSED ALREADY
GRADED A+
1. Getting a 3 variable VAR model first matrix: first row are coefficients for Xt1, second
row are
from summary(model) output of coefficients for Xt2, etc...
a VAR(1) model
second matrix is Xt-1, i b/c this is a VAR(1)
model last matrix are the constants
eta_t is covariance matrix, direct copy
2. (c) Based on the fitted contemporaneous cross-correlation is NOT present
mod- el, is there if the variance-covariance matrix is a diagonal
contemporane- ous cross- matrix
correlation? Is there there is lagged correlation if the order p of the VAR(p)
lagged cross-correlation? model
Is there lagged auto- >0
correlation? Explain.
3. T/F - Differencing the data might
True not make the series
stationary in the presence of
cointegration.
4. Cointegration and See image
long-run equilibrium
5. Does cov(x,x) = var(x)? You betcha
1/
23
,ISYE 6402 MIDTERM PREP EXAM QUESTIONS &
DETAILED SOLUTIONS PASSED ALREADY
GRADED A+
6. Autocovariance T/F see image
7. T/F - The AR(1) process is FALSE! the absolute value of phi must lie b/w -1 and
causal if and only if the 1
autoregressive parameter
phi is between 0 and
2/
23
, ISYE 6402 MIDTERM PREP EXAM QUESTIONS &
DETAILED SOLUTIONS PASSED ALREADY
GRADED A+
1. However, it is always
invert- ible.
8. T/F - A linear process is a FALSE - the moving average is a special case of a
spe- cial case of the linear process.
moving average model.
9. T/F - A guassian time false - gaussian processes can have varying means
series is always stationary
10. T/F 'In autoregressive FALSE - there are no analogies of
models the current value of dependent/independent variables w/ AR models, as
dependent variable is there are w/ regression models
influenced by past values
of both dependent and
independent variables.'
11. in AR models the current False - We don't have dependent and independent
val- ue of the dependent variables in AR models like we do in regression
variable is affected by the models
past values of both
dependent and indepen-
dent variables
12. how do ACF and PACF differ? TBD
13. what in an ACF plot would stationary?
show non-stationarity?
14. what in an ACF and PACF
plot would show
3/
23
