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  1. Nicole Seaman

    P1.T2.699. Linear and nonlinear trends (Diebold)

    Learning objectives: Describe linear and nonlinear trends. Describe trend models to estimate and forecast trends Questions: 699.1. Consider the following quadratic trend model: Which of the following functions correctly characterizes this trend? a. Tr = 10 + 0.3*TIME + 0.3*TIME^2 b. Tr = 10...
  2. Nicole Seaman

    P1.T2.701. Regression analysis to model seasonality (Diebold)

    Learning objectives: Explain how to use regression analysis to model seasonality. Explain how to construct an h-step-ahead point forecast. Questions: 701.1. Based on a regression analysis, the following model was produced to predict housing starts (given in thousands) within a certain...
  3. 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...
  4. Dr. Jayanthi Sankaran

    Diebold, Chapter 5, 7 and 8

    Hi David, I notice that while there are Study Notes encompassing Diebold, Chapters 5, 7 and 8, there are no corresponding PQ sets. Does that mean that this material is not that relevant or that you are working towards producing a PQ set...Please let me know:) Thanks! Jayanthi
  5. Nicole Seaman

    P1.T2.507. White noise

    Learning outcomes: Define, white noise describe independent white noise and normal (Gaussian) white noise. Explain the characteristics of the dynamic structure of white noise. Explain how a lag operator works. Questions: 507.1. In regard to white noise, each of the following statements is true...
  6. Nicole Seaman

    P1.T2.505. Model selection criteria (Diebold)

    Learning outcomes: Define mean squared error (MSE) and explain the implications of MSE in model selection. Explain how to reduce the bias associated with MSE and similar measures. Compare and evaluate model selection criteria, including s^2, the Akaike information criterion (AIC), and the...