Times-series Analysis (2021 Level II CFA® Exam – Reading 6)

Level II CFA® Program Video Lessons offered by AnalystPrep For Level II Practice Cases and Mock Exams: https://analystprep.com/shop/practice-package-for-level-ii-of-the-cfa-exam-by-analystprep/ For Level I Video Lessons, Study Notes, Question Bank, CBT Mock Exams & More: https://analystprep.com/shop/cfa-level-1-learn-practice-package/ For FRM (Part I & Part II) Video Lessons, Study Notes, Question Bank, CBT Mock Exams & More: https://analystprep.com/shop/unlimited-package-for-frm-part-i-part-ii/ Readings 6 – Times-series Analysis 0:00 Introduction and Learning Outcome Statements 1:24 LOS: 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 5:45 LOS: 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 7:24 LOS: Explain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary 8:45 LOS: Describe the structure of an autoregressive (AR) model of order p and calculate one- and two period-ahead forecasts given the estimated coefficients 14:07 LOS: Explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series 18:58 LOS: Explain mean reversion and calculate a mean-reverting level 21:06 LOS: 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 25:01 LOS: Explain the instability of coefficients of time-series models 27:30 LOS: Describe characteristics of random walk processes and contrast them to covariance stationary processes. 31:24 LOS: 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 33:25 LOS: Describe the steps of the unit root test for non-stationary and explain the relation of the test to autoregressive time-series models 36:49 LOS: 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 42:35 LOS: Explain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time series 46:59 LOS: Explain how time-series variables should be analyzed for nonstationary and/or cointegration before use in linear regression 53:27 LOS: Determine an appropriate time-series model to analyze a given investment problem and justify that choice