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Basic Question 1 of 11
Which equation(s) represent(s) an AR time-series model?
II. yt = b0 + b1t + εt.
III. lnyt = b0 + b1t + εt.
IV. xt = b0 + b1xt-1 + b2xt-2 + ... + bpxt-p +εt.
I. xt = b0 + b1xt-1 + εt.
II. yt = b0 + b1t + εt.
III. lnyt = b0 + b1t + εt.
IV. xt = b0 + b1xt-1 + b2xt-2 + ... + bpxt-p +εt.
User Contributed Comments 5
| User | Comment |
|---|---|
| MasterD | Am I mistaken or does II and III not have any t-1 values, only t. If so, then what's all this about all options relating to PREVIOUS period values? |
| ucsbdan | II and III are special cases: check the text book. |
| ericczhang | I got it wrong too, but I'm guessing since t = (t-1)+1 you can rewrite the regression equation in terms of t-1 and thus it's an AR model since then you can rewrite the regression equation in terms of y-1. |
| sahilb7 | In II and III, y is dependent upon b1t which is also related to (t-1)... i.e. b1[(t-1)+1] |
| sahilb7 | II is a Linear trend model III is a Log-Linear trend model |
I am using your study notes and I know of at least 5 other friends of mine who used it and passed the exam last Dec. Keep up your great work!
Barnes
Learning Outcome Statements
describe the structure of an autoregressive (AR) model of order p and calculate one- and two-period-ahead forecasts given the estimated coefficients;
explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series;
explain mean reversion and calculate a mean-reverting level;
contrast in-sample and out-of-sample forecasts and compare the forecasting accuracy of different time-series models based on the root mean squared error criterion;
CFA® 2025 Level II Curriculum, Volume 1, Module 5.