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Learning Outcome Statements PDF Download
|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.
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