- CFA Exams
- 2025 Level II
- Topic 1. Quantitative Methods
- Learning Module 5. Time-Series Analysis
Seeing is believing!
Before you order, simply sign up for a free user account and in seconds you'll be experiencing the best in CFA exam preparation.
Learning Outcome Statements PDF Download
1. Trend Models 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; | |
2. Autoregressive (AR) Time-Series Models explain the instability of coefficients of time-series models; describe characteristics of random walk processes and contrast them to covariance stationary processes; | |
3. Random Walks describe implications of unit roots for time-series analysis, explain when unit roots are likely to occur and how to test for them, and demonstrate how a time series with a unit root can be transformed so it can be analyzed with an AR model; describe the steps of the unit root test for nonstationarity and explain the relation of the test to autoregressive time-series models; | |
4. Unit Roots for Time-Series Analysis explain how to test and correct for seasonality in a time-series model and calculate and interpret a forecasted value using an AR model with a seasonal lag; | |
5. Seasonality in Time-Series Models explain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time series; | |
6. Autoregressive Conditional Heteroskedasticity Models explain how time-series variables should be analyzed for nonstationarity and/or cointegration before use in a linear regression; determine an appropriate time-series model to analyze a given investment problem and justify that choice. | |
7. Regressions with More Than One Time Series describe supervised machine learning, unsupervised machine learning, and deep learning; |

You have a wonderful website and definitely should take some credit for your members' outstanding grades.

Colin Sampaleanu
My Own Flashcard
No flashcard found. Add a private flashcard for the module.
Add