- CFA Exams
- Dec. 2020 Level 2
- Study Session 2. Quantitative Methods I
- Reading 6. Time-Series Analysis
Reading 6. Time-Series Analysis
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Learning Outcome Statements
Reading 6. Time-Series Analysis | |
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1. Trend models a. calculate and evaluate the predicted trend value for a time series, modeled as either a linear trend or a log-linear trend, given the estimated trend coefficients; b. describe factors that determine whether a linear or a log-linear trend should be used with a particular time series and evaluate limitations of trend models; c. explain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary; | |
2. Autoregressive (AR) time-series models d. describe the structure of an autoregressive (AR) model of order p and calculate one- and two-period-ahead forecasts given the estimated coefficients; e. explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series; f. explain mean reversion and calculate a mean-reverting level; g. 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; | |
3. Random walks and unit roots h. explain the instability of coefficients of time-series models; i. describe characteristics of random walk processes and contrast them to covariance stationary processes; j. 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; | |
4. Seasonality in time-series models k. describe the steps of the unit root test for nonstationarity and explain the relation of the test to autoregressive time-series models; l. 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. Autoregressive conditional heteroskedasticity models m. explain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time series; | |
6. Regressions with more than one time series n. explain how time-series variables should be analyzed for nonstationarity and/or cointegration before use in a linear regression; o. determine an appropriate time-series model to analyze a given investment problem and justify that choice. |