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  1. David Harper CFA FRM

    P1.T2.21.2. Unit roots and Dickey-Fuller

    Learning objectives: Explain the challenges of modeling time series containing unit roots. Describe how to test if a time series contains a unit root. Explain how to construct an h-step-ahead point forecast for a time series with seasonality. Calculate the estimated trend value and form an...
  2. David Harper CFA FRM

    P1.T2.21.1. Nonlinear time trends and unit roots

    Learning objectives: Describe linear and nonlinear time trends. Explain how to use regression analysis to model seasonality. Describe a random walk and a unit root. Questions: 21.1.1. The following seasonal dummy model estimates the quarterly growth rate (in percentage terms) of housing...
  3. Nicole Seaman

    P1.T2.20.25 Forecasting ARMA models

    Learning objectives: Explain how forecasts are generated from ARMA models. Describe the role of mean reversion in long-horizon forecasts. Explain how seasonality is modeled in a covariance-stationary ARMA. Questions: 20.25.1. Below is plotted the monthly growth rate of a new cryptocurrency...
  4. Nicole Seaman

    P1.T2.700. Seasonality in time series analysis (Diebold)

    Learning objective: Describe the sources of seasonality and how to deal with it in time series analysis. Questions 700.1. Which of the following time series is MOST LIKELY to contain a seasonal pattern? a. Price of solar panels b. Employment participation rate c. Climate data data recorded...