Reading 2
Time Series Analysis
5 modules 34 min
0/5
Exam Focus
A time series is a set of observations of a random variable spaced evenly through time (e.g., quarterly sales revenue for a company over the past 60 quarters). For the exam, given a regression output, identifying violations such as heteroskedasticity, nonstationarity, serial correlation, etc., will be important, as well as being able to calculate a predicted value given a time series model. Know why a log linear model is sometimes used; understand the implications of seasonality and how to detect and correct it, as well as the root mean squared error (RMSE) criterion.
Modules
- Module 2.1Linear and Log Linear Trend ModelsCalculate 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. · 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 min · 2 LOS
- Module 2.2Autoregressive (AR) ModelsExplain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary. · Describe the structure of an autoregressive (AR) model of order p and calculate one and two period ahead forecasts given the estimated coefficients. · Explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series. · Explain mean reversion and calculate a mean reverting level. · 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. · Explain the instability of coefficients of time series models.9 min · 6 LOS
- Module 2.3Random Walks and Unit RootsDescribe characteristics of random walk processes and contrast them to covariance stationary processes. · 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. · Describe the steps of the unit root test for nonstationarity and explain the relation of the test to autoregressive time series models.6 min · 3 LOS
- Module 2.4SeasonalityExplain 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.4 min · 1 LOS
- Module 2.5ARCH and Multiple Time SeriesExplain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time · Explain how time series variables should be analyzed for nonstationarity and/or cointegration before use in a linear regression. · Determine an appropriate time series model to analyze a given investment problem and justify that choice.8 min · 3 LOS