A manager uses a trend equation plus quarterly relatives to predict demand. Quarter relatives are SR1 = 0.9, SR2 = 0.95, SR3 = 1.05, and SR4 = 1.1. The trend equation is: Ft= 11 + 3t. Over the past nine quarters, demand has been as follows: Period, t: 1 2 4 6 8 Demand: 15 17 20 23 25 29 34 36 39 Click here for the Excel Data File Compute the MAD and tracking signal for each period. (Negative values should be indicated by a minus sign. Round your intermediate calculations and final answers to 3 decimal places.) Period, t Demand MAD Tracking signal 1 15 2 17 3 20 4 23 25

A manager uses a trend equation plus quarterly relatives to predict demand. Quarter relatives are SR1 = 0.9, SR2 = 0.95, SR3 = 1.05, and SR4 = 1.1. The trend equation is: Ft= 11 + 3t. Over the past nine quarters, demand has been as follows: Period, t: 1 2 4 6 8 Demand: 15 17 20 23 25 29 34 36 39 Click here for the Excel Data File Compute the MAD and tracking signal for each period. (Negative values should be indicated by a minus sign. Round your intermediate calculations and final answers to 3 decimal places.) Period, t Demand MAD Tracking signal 1 15 2 17 3 20 4 23 25

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter5: Business And Economic Forecasting

Section: Chapter Questions

Problem 7E: The Questor Corporation has experienced the following sales pattern over a 10-year period: Compute...

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Savings-Mart (a chain of discount department stores) sells patio and lawn furniture. Sales are seasonal, with higher sales during the spring and summer quarters and lower sales during the fall and winter quarters. The company developed the following quarterly sales forecasting model: Y t=8.25+0.125t2.75D1t+3.50D3t where Y t=predictedsales(million)inquartert 8.25=quarterlysales(million)whent=0 t=timeperiod(quarter)wherethefourthquarterof2002=0,firstquarterof2003=1,secondquarterof2003=2,... D1t={1forfirst-quarterobservations0otherwiseD2t={1forsecond-quarterobservations0otherwiseD3t={1forthird-quarterobservations0otherwise Forecast Savings-Marts sales of patio and lawn furniture for each quarter of 2010.
The Questor Corporation has experienced the following sales pattern over a 10-year period: Compute the equation of a trend line (similar to Equation 5.4) for these sales data to forecast sales for the next year. (Let 2004=0,2005=1, etc., for the time variable.) What does this equation forecast for sales in the year 2014? Use a first-order exponential smoothing model with a w of 0.9 to forecast sales for the year 2014.
Metropolitan Hospital has estimated its average monthly bed needs as N=1,000+9X where X=timeperiod(months);January2002=0 N=monthlybedneeds Assume that no new hospital additions are expected in the area in the foreseeable future. The following monthly seasonal adjustment factors have been estimated, using data from the past five years: Forecast Metropolitans bed demand for January, April, July, November, and December 2007. If the following actual and forecast values for June bed demands have been recorded, what seasonal adjustment factor would you recommend be used in making future June forecasts?
Suppose that you work for a U.S. senator who is contemplating writing a bill that would put a national sales tax in place. Because the tax would be levied on the sales revenue of retail stores, the senator has asked you to prepare a forecast of retail store sales for year 8, based on data from year 1 through year 7. The data are: (c1p2) Year Retail Store Sales 1 $1,225 2 1,285 3 1,359 4 1,392 5 1,443 6 1,474 7 1,467 54 Chapter One a. Use the first naive forecasting model presented in this chapter to prepare a forecast of retail store sales for each year from 2 through 8. b. Prepare a time-series graph of the actual and forecast values of retail store sales for the entire period. (You will not have a forecast for year 1 or an actual value for year 8.) c. Calculate the root-mean-squared error for your forecast series using the values for year 2 through year 7. 3. Use the second naive forecasting model presented in this chapter to answer parts (a) through (c) of Exercise 2. Use P 0.2 in…
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 Average
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…
Choose one of the following forecasting methods discussed in this chapter: last-value, averaging, moving-average, or exponential smoothing. Identify the conditions when the method is most appropriate to use and give an example of an application of this method.
A firm keeps a record of sales and prices over the past seven months, resulting in the following table: Price (ZMW/ton) Sales (tons) Nov. 1985 7.5 84.5 Dec. 8.0 82.0 Jan. 1986 8.0 84.0 Feb. 7.2 92.0 March 7.0 95.0 April 8.0 92.0 May 8.5 91.5 Use these observations to estimate demand as a linear function of both price and time. Further, utilise this function to estimate demand for the following month, on the assumption that: (a) price remains unchanged, (b) price increases to ZMW9/ton. Hence estimate the price elasticity of demand between these prices and find the price which would maximise sales revenue. Given the nature of the observations, comment on any difficulties in interpreting your results for decision-making purposes.
Name two coincident indicators used in forecasting
13. List the basic trend patterns within the projection methods and determine which is the most commonly used when analyzing demand.
The monthly demand of a company is showed below, please use the static method to forecast the demand for Year 6. Sales Year 1 Year 2 Year 3 Year 4 Year 5 JAN 2,000 3,000 2,000 5,000 5,000 FEB 3,000 4,000 5,000 4,000 2,000 MAR 3,000 3,000 5,000 4,000 3,000 APR 3,000 5,000 3,000 2,000 2,000 MAY 4,000 5,000 4,000 5,000 7,000 JUN 6,000 8,000 6,000 7,000 6,000 JUL 7,000 3,000 7,000 10,000 8,000 AUG 6,000 8,000 10,000 14,000 10,000 SEP 10,000 12,000 15,000 16,000 20,000 OCT 12,000 12,000 15,000 16,000 20,000 NOV 14,000 16,000 18,000 20,000 22,000 DEC 8,000 10,000 8,000 12,000 8,000 Total 78,000 89,000 98,000 115,000 113,000
21. Consider a firm subject to quarter-to-quarter variation in its sales. Suppose that the following equation was estimated using quarterly data for the period 2011–2018 (the time variable goes from 1 to 32). The variables D1, D2, and D3 are, respectively, dummy variables for the first, second, and third quarters (e.g., D1 is equal to 1 in the first quarter and 0 otherwise). Qt =a+bt+c1D1+c2D2+c3D3 The results of the estimation are presented here: a. Calculate the intercept in each of the four quarters. What do these values imply? b. Use this estimated equation to forecast sales in the fourth quarter of 2019.