LevelUp CFA
Time-Series Analysis
đ Quantitative Methods - Reading 3
Overall Progress
0 / 13 LOS
describe the structure of a time series.
explain the assumption of covariance stationarity and describe the importance of this assumption in time-series analysis.
explain an autoregressive (AR) model of order p and the assumptions of the model.
determine if an AR model is covariance stationary.
calculate a one- and two-period-ahead forecast given an AR(1) or AR(2) model and an initial value(s).
explain the concept of mean reversion.
describe the problem of nonstationarity and its consequences.
describe the characteristics and challenges of random walk time series.
explain how to test for a unit root.
explain the concept of cointegration.
describe the application of time-series analysis to model seasonality.
describe the application of autoregressive conditional heteroskedasticity (ARCH) models.
explain how to select the best time-series model.
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