Which of the following sets of characteristics would usually best describe an autoregressive process of order 3 (i.e., an AR(3))?Group of answer choicesA slowly decaying acf, and a pacf with 3 significant spikesA slowly decaying pacf and an acf with 3 significant spikesAn acf and a pacf with 3 significant spikesA slowly decaying acf and pacf

What type of a process is ? Group of answer choicesAn autoregressive modelAn autoregressive integrated moving average modelA periodic lag modelAn autoregressive moving average model

Which of the following is true for an autoregressive model?Answer choicesSelect only one optionREVISITIn an autoregressive model, the value from a time series is regressed on future values from that same time seriesIn an autoregressive model, the value from a time series is regressed on previous values from that same time seriesIn an autoregressive model, the value from a time series is regressed on previous values from a different time seriesIn an autoregressive model, the value from a time series is regressed on future values from a different time series

Consider two time series, {𝑥𝑡}𝑡=1𝑇 and {𝑦𝑡}𝑡=1𝑇, one generated using AR(1) and the other using MA(1), as follows:𝑥𝑡=𝛼+𝛽𝑥𝑡−1+𝜀𝑡, and𝑦𝑡=𝜇+𝜀𝑡+𝜃𝜀𝑡−1.Suppose 𝛽=𝜃=0.5.Based on the provided information, we can claim that:Group of answer choicesNone of the presented answers are correct.The first order autocorrelation of the series following the presented AR model is larger than the first order autocorreation of the series following the presented MA model.The autocorrelation functions of the two models are identical for lags greater than one.The second order autocorrelation of the series following the presented AR model is equal to zeroThe two step-ahead forecast of the series following the presented AR model is equal to the unconditional mean of the series.

AR(1) is equivalent to MA(1) when the autoregressive parameter is equal to 1.Group of answer choicesTrueFalse

Assume you collected data representing the number of full-time employed males ('000) without access to paid leave entitlements in Australia from 1992 to 2007, fitted a third-order autoregressive model and found a p-value = 0.50 for the third-order autoregressive parameter. Based on this information what conclusion can you make. a. The second-order parameter of the autoregressive model is insignificant and can be deleted. b. The third-order parameter of the autoregressive model is significant and should remain in the model. c. The second-order parameter of the autoregressive model is significant and should remain in the model. d. The third-order parameter of the autoregressive model is insignificant and can be deleted.