ISYE 6402 MIDTERM PREP EXAM
QUESTIONS WITH GUARANTEED
CORRECT ANSWERS
Getting a 3 variable VAR model from summary(model) output of a VAR(1) model
- ACCURATE ANSWER✅✅first matrix: first row are coefficients for Xt1,
second row are coefficients for Xt2, etc...
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
(c) Based on the fitted model, is there contemporaneous cross-correlation? Is there
lagged cross-correlation? Is there lagged auto-correlation? Explain. - ACCURATE
ANSWER✅✅contemporaneous cross-correlation is NOT present if the variance-
covariance matrix is a diagonal matrix
there is lagged correlation if the order p of the VAR(p) model > 0
T/F - Differencing the data might not make the series stationary in the presence of
cointegration. - ACCURATE ANSWER✅✅True
Cointegration and long-run equilibrium - ACCURATE ANSWER✅✅See image
Does cov(x,x) = var(x)? - ACCURATE ANSWER✅✅You betcha
, Autocovariance T/F - ACCURATE ANSWER✅✅see image
T/F - The AR(1) process is causal if and only if the autoregressive parameter phi is
between 0 and 1. However, it is always invertible. - ACCURATE
ANSWER✅✅FALSE! the absolute value of phi must lie b/w -1 and 1
T/F - A linear process is a special case of the moving average model. -
ACCURATE ANSWER✅✅FALSE - the moving average is a special case of a
linear process.
T/F - A guassian time series is always stationary - ACCURATE
ANSWER✅✅false - guassian processes can have varying means
T/F 'In autoregressive models the current value of dependent variable is influenced
by past values of both dependent and independent variables.' - ACCURATE
ANSWER✅✅FALSE - there are no analogies of dependent/independent variables
w/ AR models, as there are w/ regression models
in AR models the current value of the dependent variable is affected by the past
values of both dependent and independent variables - ACCURATE
ANSWER✅✅False - We don't have dependent and independent variables in AR
models like we do in regression models
how do ACF and PACF differ? - ACCURATE ANSWER✅✅TBD
what in an ACF plot would show non-stationarity? - ACCURATE
ANSWER✅✅slowly decreasing lags
what in an ACF and PACF plot would show stationary? - ACCURATE
ANSWER✅✅few lags outside of confidence bands, quickly decreasing
QUESTIONS WITH GUARANTEED
CORRECT ANSWERS
Getting a 3 variable VAR model from summary(model) output of a VAR(1) model
- ACCURATE ANSWER✅✅first matrix: first row are coefficients for Xt1,
second row are coefficients for Xt2, etc...
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
(c) Based on the fitted model, is there contemporaneous cross-correlation? Is there
lagged cross-correlation? Is there lagged auto-correlation? Explain. - ACCURATE
ANSWER✅✅contemporaneous cross-correlation is NOT present if the variance-
covariance matrix is a diagonal matrix
there is lagged correlation if the order p of the VAR(p) model > 0
T/F - Differencing the data might not make the series stationary in the presence of
cointegration. - ACCURATE ANSWER✅✅True
Cointegration and long-run equilibrium - ACCURATE ANSWER✅✅See image
Does cov(x,x) = var(x)? - ACCURATE ANSWER✅✅You betcha
, Autocovariance T/F - ACCURATE ANSWER✅✅see image
T/F - The AR(1) process is causal if and only if the autoregressive parameter phi is
between 0 and 1. However, it is always invertible. - ACCURATE
ANSWER✅✅FALSE! the absolute value of phi must lie b/w -1 and 1
T/F - A linear process is a special case of the moving average model. -
ACCURATE ANSWER✅✅FALSE - the moving average is a special case of a
linear process.
T/F - A guassian time series is always stationary - ACCURATE
ANSWER✅✅false - guassian processes can have varying means
T/F 'In autoregressive models the current value of dependent variable is influenced
by past values of both dependent and independent variables.' - ACCURATE
ANSWER✅✅FALSE - there are no analogies of dependent/independent variables
w/ AR models, as there are w/ regression models
in AR models the current value of the dependent variable is affected by the past
values of both dependent and independent variables - ACCURATE
ANSWER✅✅False - We don't have dependent and independent variables in AR
models like we do in regression models
how do ACF and PACF differ? - ACCURATE ANSWER✅✅TBD
what in an ACF plot would show non-stationarity? - ACCURATE
ANSWER✅✅slowly decreasing lags
what in an ACF and PACF plot would show stationary? - ACCURATE
ANSWER✅✅few lags outside of confidence bands, quickly decreasing
