Reference no: EM131408184
Consider the data on new orders for computers and electronic products and the M1 money supply for the years 2011 through 2014.
|
Year
|
Month
|
New Orders for Computers and 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
|
|
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
|
1. Using the data for computer and electronic products, develop a three-period MA(3), four-period MA(4), and five-period MA(5) moving average forecasts.
2. 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).
3. Using the same data, develop exponential smoothing forecasts with an alpha (α) of 0.75 and 025. Assume the first month forecast is the same as the actual data.
4. Using the same data, develop a time-series trend forecast using regression analysis.
5. Using the same data, develop a causal model forecast using the M1 Money Supply data as the independent variable.
6. Using either MSE or MAD, determine which forecast is best.
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