Which of the following statements are true concerning the autocorrelation function (acf) and partial autocorrelation function (pacf)?

Group of answer choices

The acf and pacf will be the same at lag two for an MA(1) model

The pacf for an AR(p) model will be zero beyond lag p

No answer text provided.

No answer text provided.

The acf and pacf will always be identical at lag one whatever the model

The pacf for an MA(q) model will in general be non-zero beyond lag q

Flag question: Question 17Question 175pts

The pacf is necessary for distinguishing between

Group of answer choices

An AR and an MA model

Different models from within the ARMA family

An MA and an ARMA model

An AR and an ARMA model

Flag question: Question 18Question 185pts

Consider the picture below and suggest the model from the following list that best characterises the process:

Group of answer choices

An MA(2)

An AR(2)

An AR(1)

An ARMA(2,1)

Flag question: Question 19Question 195pts

Consider the picture below and suggest the model from the following list that best characterises the process:

Group of answer choices

An MA(2)

An AR(2)

An AR(1)

An ARMA(1,1)

Flag question: Question 20Question 205pts

The purpose of "augmenting" the Dickey-Fuller test regression is to

Group of answer choices

Ensure that the test regression residuals are normally distributed

Ensure that all of the non-stationarity is taken into account.

Ensure that there is no autocorrelation in the test regression residuals

Ensure that there is no heteroscedasticity in the test regression residuals.

Flag question: Question 21Question 215pts

Which one of the following would NOT be a consequence of using non-stationary data in levels form?

Group of answer choices

Statistical inferences may be invalid

Test statistics may not follow standard distributions

Parameter estimates may be biased

The regression R2 may be spuriously high

Flag question: Question 22Question 225pts

Which of the following conditions are necessary for a series to be classifiable as a weakly stationary process?

Group of answer choices

It must have a constant mean

It must have a constant variance

It must have constant autocovariances for given lags

It must have a constant probability distribution

Flag question: Question 23Question 235pts

Which of the following statements are true concerning the autocorrelation function (acf) and partial autocorrelation function (pacf)?

Group of answer choices

The acf and pacf will be the same at lag two for an MA(1) model

The pacf for an AR(p) model will be zero beyond lag p

The acf and pacf will always be identical at lag one whatever the model

The pacf for an MA(q) model will in general be non-zero beyond lag q

Flag question: Question 24Question 245pts

An ARMA(p,q) (p, q are integers bigger than zero) model will have

Group of answer choices

An acf that declines geometrically and a pacf that is zero after p lags

An acf that declines geometrically and a pacf that is zero after q lags

An acf and pacf that both decline geometrically

An acf that is zero after p lags and a pacf that is zero after q lags

Flag question: Question 25Question 255pts

Which of the following statements are true concerning the class of ARIMA(p,d,q) models?

Group of answer choices

The estimation of ARIMA models is incompatible with the notion of cointegration

An ARIMA(p,1,q) model estimated on a series of logs of prices is equivalent to an ARIMA(p,0,q) model estimated on a set of continuously compounded returns

The "I" stands for independent

It is plausible for financial time series that the optimal value of d could be 2 or 3.

Flag question: Question 26Question 265pts

Which of the following is not an example of a time series model?

Group of answer choices

None of the above

Naive approach

Exponential smoothing

Moving Average

Flag question: Question 27Question 275pts

What does auto-covariance measure?

Group of answer choices

Linear dependence between multiple points on the different series observed at different times

Quadratic dependence between two points on the same series observed at different times

Linear dependence between two points on the same series observed at different times

Linear dependence between two points on different series observed at same time

Flag question: Question 28Question 285pts

Looking at the below ACF plot, would you suggest to apply AR or MA in ARIMA modeling technique?

Group of answer choices

ARIMA

AR

MA

ARMA

Flag question: Question 29Question 295pts

If theACFdropssharplyat a given lag or the first lag autocorrelation ispositive, then use anARmodelwith orderpequal to the lag just before the sharp decline

Group of answer choices

True

False

Flag question: Question 30Question 305pts

If thePACFdropssharplyat a given lag or the first lag autocorrelation isnegative, then use anMAmodelwith orderqequal to the lag just before the sharp decline.

Group of answer choices

True

False

Flag question: Question 31Question 315pts

There are a few things you should know aboutARIMAmodels includes:

Group of answer choices

d is the number of times to difference the data

p is the order of the AR model

The ARIMA model is denoted ARIMA(p, d, q)

q is the order of the MA model

p, d, and q are nonnegative integers