IAI Actuarial Core Principles · Risk Modelling and Survival Analysis · Core concepts of time series models
A stationary AR(1) process has autocorrelation at lag 1 equal to 0.5. What is its autocorrelation at lag 3?
The lag 3 autocorrelation is 0.125. In a stationary AR(1) process the autocorrelation at lag k equals the coefficient raised to the power k, and the lag 1 autocorrelation equals the coefficient 0.5, so the result is 0.5 cubed.
- A0.125Correct
- B0.150
- C0.250
- D0.500
- 0.375
Explanation
For an AR(1), rho_k = a^k, and rho_1 = a = 0.5. So rho_3 = 0.5^3 = 0.125. The value 0.375 comes from multiplying 0.5 by 3 instead of raising it to the power 3.
Did you get it right without looking?
One question tells you little. A timed set on Core concepts of time series models shows your real accuracy, how long you take and where you lose marks.
More Core concepts of time series models questions
- For a stationary MA(1) process X_t = e_t + 0.5 e_{t-1}, with e_t white noise of variance sigma^2, what is the autocorrelation at lag 1 and a…
- Which of the following processes, with e_t independent white noise of constant variance, is NOT weakly stationary?
- A stationary AR(1) process has X_t - mu = 0.5 (X_{t-1} - mu) + e_t with mu = 40. Given X_10 = 48, what is the best forecast of X_12 at time …
- Consider the process X_t = 1.5 X_{t-1} - 0.5 X_{t-2} + e_t, where e_t is white noise. Which description is correct?
- Consider the process X_t = 0.5 X_{t-1} + e_t, where e_t are independent N(0, 4) white noise terms, and the process has been running for a lo…
- If X_t follows a random walk X_t = X_{t-1} + e_t with e_t white noise, which transformation produces a stationary series?