Answer-first summary for fast verification
Answer: AR(2)
## Explanation In time series analysis using Partial Autocorrelation Function (PACF) plots: - **AR(p) models**: The PACF plot shows significant spikes at the first p lags and then cuts off to zero. The order p is determined by the number of significant lags in the PACF. - **MA(q) models**: The PACF plot decays gradually rather than cutting off sharply. Since the question mentions using a PACF plot to determine the best regression approach, and AR(2) is one of the options, this suggests the PACF plot likely shows significant spikes at lags 1 and 2, then cuts off to zero. This pattern is characteristic of an **AR(2) model**. **Key points:** - AR(1) would have only one significant spike in the PACF - MA models are identified using ACF plots, not PACF plots - The PACF cutting off after lag 2 indicates AR(2) is appropriate Therefore, AR(2) is the best regression approach based on the PACF plot analysis.
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A risk manager would like to analyze and forecast a security performance and has obtained the historical time series for that security. Based on the Partial Autocorrelation Function (PACF) plot, which of the following is the best regression approach for the security?
A
AR(1)
B
MA(1)
C
AR(2)
D
MA(2)