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ISyE 6402 MIDTERM PREP QUESTIONS SOLVED CORRECTLY & GRADED A+
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ISyE 6402 MIDTERM PREP QUESTIONS SOLVED CORRECTLY & GRADED A+
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Name: Score:82 Multiple choice questions
Definition 1 of 82
Seek large P values
## Testing for Normality
normality.test(model) - p-values are not smaller than 0.05: don't reject the null hypothesis of
normality
## Testing for heterogeneous variance
arch.test(model) - p-value large, do not reject the null of constant variances
## Testing for serial correlation (Multivariate Portmonteau test)
serial.test(model) - p-value large, do not reject the null of uncorrelation
VAR diagnostics
What makes a process causal?
In var modeling, residual analysis
Wald test granger causality
Term 2 of 82
T/F AR(p) processes are always invertible.
True
False
,Term 3 of 82
How many terms to estimate in a VAR model?
The number of terms decreases as the data becomes more noisy
In a n‐variate system, the number of coefficients in each equation is 1+np and the total
number is n(1+np)=n+𝑛^2p
The number of terms is fixed regardless of the data complexity
The number of terms increases linearly with the number of observations
Term 4 of 82
what in an ACF and PACF plot would show stationary?
there is no lead-lag relationship among the six series correct
false - otherwise, you wouldn't have to consider autocorrelation
few lags outside of confidence bands, quickly decreasing
slowly decreasing lags
Term 5 of 82
can the Wald test be used to test if higher order VAR(p) needed?
Yes
No
,Definition 6 of 82
NOT seasonality!
Which assumptions violated?
Constant mean and covariance b/w any two observations depends only on the time lag b/w them
which assumptions of stationary are violated?
Which of the following characteristics are present in the time series plot of the original data
(figure 1)?
trend and heteroskedasticity
what makes a process causal?
Consecutive observations in a white noise process are uncorrelated and identically
distributed, but not necessarily independent.
Term 7 of 82
T/F Consecutive observations in a white noise process are identically distributed.
True
False
, Definition 8 of 82
FALSE! the absolute value of phi must lie b/w -1 and 1
T/f the mean of a random walk process does not depend on time (the sequence is mean
stationary).
T/f 9. an arima (p, 0, q) model is stationary.
The ar(1) process is causal if and only if the autoregressive parameter phi is between -1 and
1. however, it is always invertible.
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.
Definition 9 of 82
If P ~ 0, then Granger causality
Wald test Granger causality
T/f your boss hands you the u.s. inflation rate and exchange rate time series. the two
sequileries are not stationary, and are known to have a long-run equilibrium relationship.
building a var model on the differenced data is a preferable way to go about analyzing the
two series.
What makes a process causal?
Cointegration and Long-run equilibrium
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