Consider the data on new orders for computers and electronic

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 same data, develop exponential smoothing forecasts with an alpha () of 0.85 and 0.15. Assume the first month forecast is the same as the actual data

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

Solution

Exponential smoothing forecasts uses a simple formula as given below

Forecasted value of current period = Forecasted value of previous period + alpha times the difference between the actual and forecasted values of previous period

One can use scientific calculator or excel to make use of the above mentioned formula repeatedly to get the forecasts.

				Alpha .85	Alpha .85	Alpha .15	Alpha .15
		Actual	Actual	Forecasted	Forecasted	Forecasted	Forecasted
Year	Month	New Orders for Computers and Electronic Products	M-1 Money Supply	New Orders for Computers and Electronic Products	M-1 Money Supply	New Orders for Computers and Electronic Products	M-1 Money Supply
2011	1	19222	1855.6	19222	1855.6	19222	1855.6
2011	2	20727	1874.7	19222	1855.6	19222	1855.6
2011	3	24893	1892	20501.25	1871.835	19447.75	1858.465
2011	4	19375	1897.8	24234.2375	1888.97525	20264.5375	1863.49525
2011	5	20152	1934.3	20103.88563	1896.476288	20131.10688	1868.640963
2011	6	25075	1947	20144.78284	1928.626443	20134.24084	1878.489818
2011	7	18615	2001.5	24335.46743	1944.243966	20875.35472	1888.766345
2011	8	21289	2112.9	19473.07011	1992.911595	20536.30151	1905.676394
2011	9	27014	2126	21016.61052	2094.901739	20649.20628	1936.759935
2011	10	22179	2137.4	26114.39158	2121.335261	21603.92534	1965.145944
2011	11	20761	2172	22769.30874	2134.990289	21690.18654	1990.984053
2011	12	27818	2168.2	21062.24631	2166.448543	21550.80856	2018.136445
2012	1	19447	2202.3	26804.63695	2167.937282	22490.88727	2040.645978
2012	2	23043	2212.2	20550.64554	2197.145592	22034.30418	2064.894081
2012	3	26734	2228.7	22669.14683	2209.941839	22185.60856	2086.989969
2012	4	21897	2245.3	26124.27202	2225.886276	22867.86727	2108.246474
2012	5	22403	2251	22531.0908	2242.387941	22722.23718	2128.804503
2012	6	24942	2262.3	22422.21362	2249.708191	22674.3516	2147.133827
2012	7	19365	2314.6	24564.03204	2260.411229	23014.49886	2164.408753
2012	8	20240	2346.5	20144.85481	2306.471684	22467.07403	2186.93744
2012	9	25478	2383.6	20225.72822	2340.495753	22133.01293	2210.871824
2012	10	20790	2415.5	24690.15923	2377.134363	22634.76099	2236.781051
2012	11	20362	2423.2	21375.02388	2409.745154	22358.04684	2263.588893
2012	12	27841	2457.7	20513.95358	2421.181773	22058.63982	2287.530559
2013	1	17393	2467.6	26741.94304	2452.222266	22925.99384	2313.055975
2013	2	18725	2470.4	18795.34146	2465.29334	22096.04477	2336.237579
2013	3	22919	2474.8	18735.55122	2469.634001	21590.38805	2356.361942
2013	4	19560	2511	22291.48268	2474.0251	21789.67984	2374.127651
2013	5	20333	2522	19969.7224	2505.453765	21455.22787	2394.658503
2013	6	24619	2517.9	20278.50836	2519.518065	21286.89369	2413.759728
2013	7	18065	2545.6	23967.92625	2518.14271	21786.70963	2429.380769
2013	8	18487	2557.3	18950.43894	2541.481406	21228.45319	2446.813653
2013	9	24877	2578.8	18556.51584	2554.927211	20817.23521	2463.386605
2013	10	20410	2620.2	23928.92738	2575.219082	21426.19993	2480.698614
2013	11	20194	2622.2	20937.83911	2613.452862	21273.76994	2501.623822
2013	12	24955	2654.5	20305.57587	2620.887929	21111.80445	2519.710249