2) Suppose FFRATE is raised to 1.5% in period t = 5 and then returned to its original level of 1% for t = 6,7,8,9. Use the above equation to forecast INVGWTH for periods t = 5,6,7,8,9. Relate the changes in your forecasts to the values of the coefficients. What are the delay multipliers?

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Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? Is there an indication of a seasonal pattern? b. Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. c. Compute the quarterly forecasts for next year.

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The variance ratio (VR) can be used to determine whether returns satisfy the efficient market hypothesis (EMH). Let n = 5 and r5,t be the five period log return at time t, where t = 1, 2,..., T. 1. Express (r5,t — Ã5) in terms of deviations of the relevant one period log returns from their respective means. Denote 75 to be the mean of the five period log return.

Under the stop-loss reinsurance model, suppose the total claim follows exponential distribution with mean 1/λ. For given premium P, (a) Derive the retention level M; (b) Calculate the sum of shared variances Var(X*) + Var.X  

The Simple Exponential Smoothing (SES) forecast with an alpha of 25 for period 4 is: (round to 2 decimal places) Absolute Absolute Percent Error Squared Error Naive X(Period) Y(Sales) Forecast Error Error 14 15 3 15 41 10 15 12 Your Answer:

Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2. 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. Compute the quarterly forecasts for next year based on the model you developed in part (b). Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for quarter 1 in year 1, t = 2 for quarter 2 in year 1, … t = 12 for quarter 4 in year 3. Compute the quarterly forecasts for next year based on the model you developed in part (d). Is the model you developed in part (b) or the model you developed in part (d) more effective? Justify your answer.

Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Develop a three-week moving average for this time series. Compute MSE and a forecast for week 8. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 8. Compare the three-week moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE? Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.2.

options are: regression moving average weighted moving average correlation exponential smoothing