The value of p in a pure auto-regressive process, AR(p), can be identified using the Partial auto-correlation function. So, the correct answer is c. Partial auto-correlation function.

Here's why:

The partial autocorrelation function (PACF) gives the partial correlation of a stationary time series with its own lagged values, controlling for the values of the time series at all shorter lags. It contrasts with the autocorrelation function, which does not control for other lags.

When you plot the PACF, you will see it shut off past the order of the AR model, which in this case is p. This means that partial autocorrelations for lag greater than p will be close to zero. This is not the case with the autocorrelation function (ACF), which will gradually decrease and not shut off. Therefore, the PACF is crucial for identifying the order of an AR model.

The Ljung-Box test (option b) is used to check if the residuals are independently distributed, which is a general check for randomness, not specifically for identifying the order of an AR model.

Option d is incorrect because, as explained, the ACF does not shut off past the order of the AR model, and therefore is not sufficient for identifying the value of p.

So, the correct answer is c. Partial auto-correlation function.

Question

The value of p in a pure auto-regressive process, AR(p), can be identified using the Partial auto-correlation function. So, the correct answer is c. Partial auto-correlation function.

Here's why:

The partial autocorrelation function (PACF) gives the partial correlation of a stationary time series with its own lagged values, controlling for the values of the time series at all shorter lags. It contrasts with the autocorrelation function, which does not control for other lags.

When you plot the PACF, you will see it shut off past the order of the AR model, which in this case is p. This means that partial autocorrelations for lag greater than p will be close to zero. This is not the case with the autocorrelation function (ACF), which will gradually decrease and not shut off. Therefore, the PACF is crucial for identifying the order of an AR model.

The Ljung-Box test (option b) is used to check if the residuals are independently distributed, which is a general check for randomness, not specifically for identifying the order of an AR model.

Option d is incorrect because, as explained, the ACF does not shut off past the order of the AR model, and therefore is not sufficient for identifying the value of p.

So, the correct answer is c. Partial auto-correlation function.

Knowee AI · Accepted Answer

The value of p in a pure auto-regressive process, AR(p), can be identified using the Partial auto-correlation function. So, the correct answer is c. Partial auto-correlation function.

Here's why:

The partial autocorrelation function (PACF) gives the partial correlation of a stationary time series with its own lagged values, controlling for the values of the time series at all shorter lags. It contrasts with the autocorrelation function, which does not control for other lags.

When you plot the PACF, you will see it shut off past the order of the AR model, which in this case is p. This means that partial autocorrelations for lag greater than p will be close to zero. This is not the case with the autocorrelation function (ACF), which will gradually decrease and not shut off. Therefore, the PACF is crucial for identifying the order of an AR model.

The Ljung-Box test (option b) is used to check if the residuals are independently distributed, which is a general check for randomness, not specifically for identifying the order of an AR model.

Option d is incorrect because, as explained, the ACF does not shut off past the order of the AR model, and therefore is not sufficient for identifying the value of p.

So, the correct answer is c. Partial auto-correlation function.

In a pure auto-regressive process, AR(p), the value of p can be identified usingSelect one:a. Auto-correlation functionb. Ljung−Box testc. Partial auto-correlation functiond. Auto-correlation and partial auto-correlation function

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