This class was created by Brainscape user Steven Popovic. Visit their profile to learn more about the creator.
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Steven Popovic
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Decks in this class (15)
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;
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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;
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c explain the requirement for a time series to be covariance stationary, and describe the significance of a series that is not stationary;
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d describe the structure of an autoregressive (AR) model of order p, and calculate one- and two-period- ahead forecasts given the estimated coefficients;
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e explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series;
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f explain mean reversion, and calculate a mean-reverting level;
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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;
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h explain the instability of coefficients of time-series models;
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i describe characteristics of random walk processes, and contrast them to covariance stationary processes;
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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;
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k describe the steps of the unit root test for nonstationarity, and explain the relation of the test to autoregressive time-series models;
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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;
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m explain autoregressive conditional heteroskedasticity (ARCH), and describe how ARCH models can be applied to predict the variance of a time series;
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n explain how time-series variables should be analyzed for nonstationarity and/or cointegration before use in a linear regression;
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o determine an appropriate time-series model to analyze a given investment problem, and justify that choice.
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More about
l2 13 time-series analysis
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