In the trend model Tt = b0 + b1*Timet, suppose that b1 > 0. Is the series expected to grow or decline in future periods? Does this mean that the series grow or decline with certainty in every period?
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- In the trend model Tt = b0 + b1*Timet, suppose that b1 > 0.
- Is the series expected to grow or decline in future periods?
- Does this mean that the series grow or decline with certainty in every period?
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- Define Nonstationarity? Explain the two most important types of nonstationarity in economic time series data?A local moving company has collected data on the number of moves they have been asked to perform over the past two years. Moving is highly seasonal, so the owner/operator, who is both burly and highly educated, decides to apply the multiplicative seasonal method to forecast the number of customers for the coming year. The equation for the trend line of yearly sales is Ft = 16 + 60t. Please forecast demand for each quarter in Year 3. (Round the forecasts to whole numbers and show all calculations). Complete the table below and forecast the sales of Year 3 by quarter. Year 1 Year 2 Year 3 Quarter Demand Seasonal Index Quarter Demand Seasonal Index Average Seasonal Index Forecast 1 20 1 27 2 40 2 45 3 45 3 55 4 31 4 41 Total AverageWhich of the following is NOT TRUE about trend analysis? a) In trend analysis every item is expressed as an indexb) To compute index values, one of the years/ periods should be chosen as a base yearc) The purpose of trend analysis is to detect the trend of each item as of the previous yeard) The index values are computed by diving each item by the base year figure
- Explain the term "Autocorrelation" in relation to Time Series data?A researcher has a sample of 6 annual observations {94, 104, 102, 99, 111 and 107} for the CPI in country Z for the period 2015 to 2020, and wants to forecast CPI for the years 2021, 2022 and 2023. The researcher uses 3 different forecasting models: A, B and C. Model A is an AR(1) model with no drift and with an estimated autoregressive coefficient = 0.7. Model B is a MA(1) model with no constant and with an estimated MA coefficient = -0.4 (note the minus !). Model C is a random walk model with no drift. The error terms over the 2015-2020 period were estimated to have the values: {3, -1, 2, 4, -3, 1}. a. Compute the 2021, 2022 and 2023 forecasted values for the consumer price index based on the three models. Show the formulas and the details of your calculations, and explain all the related symbols. b. Suppose that the actual values of the CPI over the 2021, 2022 and 2023 were {108, 114, 105}. Calculate the Root mean square error of the three model forecasts over the 2021-2023…Mr. John operates a medium size business that sells tires. He buys most of his tires from a company that is located in South America. Mr. John believes that he is stocking too much tires so he decided to look into the situation. He wants to use the Economic Order Quantity (EOQ) model to manage his stock of tires. In order to use this model, he must first of all forecast the annual demand for his tires. Using a numerical example, demonstrate to Mr. John how he can use the manual trend projection method of forecasting to forecast demand for the next two years.
- The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Jan Feb Mar AprMonth 1 2 3 4Number of Accidents 30 40 70 105 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): y = ? + ?xExponential smoothing gives more weight to the _______________ observations and less to the _______________ observations. Multiple Choice smaller, larger recent, older older, recent larger, smallerEstimate the double-log (log linear) time trend model for log cruise ship arrivals against log time. Estimate a linear time trend model of cruise ship arrivals against time. Calculate the root mean square error between the predicted and actual value of cruise ship arrivals. Is the root mean square error greater for the double log non-linear time trend model or for the linear time trend model?