Question: CHAPTER 18 FORECASTING Consider the data on new orders for computers and electronic products and the M1 money supply for the years 2011 through 2014.

CHAPTER 18 FORECASTING Consider the data on new orders for computers and electronic products and the M1 money supply for the years 2011 through 2014. Year New Orders for Computers and Month Electronic Products M-1 Money Supply 2011 1 19222 1855.6 2011 2 20727 1874.7 2011 3 24893 1892.0 2011 4 19375 1897.8 2011 5 20152 1934.3 2011 6 25075 1947.0 2011 7 18615 2001.5 2011 8 21289 2112.9 2011 9 27014 2126.0 2011 10 22179 2137.4 2011 11 20761 2172.0 2011 12 27818 2168.2 2012 1 19447 2202.3 2012 2 23043 2212.2 2012 3 26734 2228.7 2012 4 21897 2245.3 2012 5 22403 2251.0 2012 6 24942 2262.3 2012 7 19365 2314.6 2012 8 20240 2346.5 2012 9 25478 2383.6 1 2 3 4 5 6 2012 10 20790 2415.5 2012 11 20362 2423.2 2012 12 27841 2457.7 2013 1 17393 2467.6 2013 2 18725 2470.4 2013 3 22919 2474.8 2013 4 19560 2511.0 2013 5 20333 2522.0 2013 6 24619 2517.9 2013 7 18065 2545.6 2013 8 18487 2557.3 2013 9 24877 2578.8 2013 10 20410 2620.2 2013 11 20194 2622.2 2013 12 24955 2654.5 Using the data for computer and electronic products, develop a three-period MA(3), four-period MA(4), and fiveperiod MA(5) moving average forecasts. Using the same data, develop a weighted moving average forecast where the weight of the most recent data (t-1) is 0.55, 0.20 for period (t-2), 0.15 for period (t-3), and 0.10 for period (t-4). Using the same data, develop exponential smoothing forecasts with an alpha () of 0.75 and 025. Assume the first month forecast is the same as the actual data. Using the same data, develop a time-series trend forecast using regression analysis. Using the same data, develop a causal model forecast using the M1 Money Supply data as the independent variable. Using either MSE or MAD, determine which forecast is best. Extra Credit - This extra credit is worth an additional 20 points on this assignment. It must be entirely correct. No partial credit will be given. Develop a time series decomposition forecast for the computer and electronic products. You must first determine the seasonal (monthly indexes) and then divide the data by these indices. Then using the deseasonalized data, determine the trend. Finally, you must re-seasonalize the data

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