Decks in this Class (15):

A Calculate And Evaluate The Predicted T
a calculate and evaluate the predicted trend value for a time series, modeled as either a linear trend or a loglinear trend, given the estimated trend coefficients;
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B Describe Factors That Determine Whethe
b describe factors that determine whether a linear or a loglinear 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 Ser
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 Autoregre
d describe the structure of an autoregressive (AR) model of order p, and calculate one and twoperiod ahead forecasts given the estimated coefficients;
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E Explain How Autocorrelations Of The Re
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
f explain mean reversion, and calculate a meanreverting level;
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G Contrast In Sample And Out Of Sample F
g contrast insample and outof sample forecasts, and compare the forecasting accuracy of different timeseries models based on the root mean squared error criterion;
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H Explain The Instability Of Coefficient
h explain the instability of coefficients of timeseries models;
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I Describe Characteristics Of Random Wal
i describe characteristics of random walk processes, and contrast them to covariance stationary processes;
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J Describe Implications Of Unit Roots Fo
j describe implications of unit roots for timeseries 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 Te
k describe the steps of the unit root test for nonstationarity, and explain the relation of the test to autoregressive timeseries models;
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L Explain How To Test And Correct For Se
l explain how to test and correct for seasonality in a timeseries model, and calculate and interpret a forecasted value using an AR model with a seasonal lag;
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M Explain Autoregressive Conditional Het
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 Shou
n explain how timeseries 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 M
o determine an appropriate timeseries model to analyze a given investment problem, and justify that choice.
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