- CFA Exams
- 2025 Level II
- Topic 1. Quantitative Methods
- Learning Module 5. Time-Series Analysis
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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; |
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