Consider the data on new orders for computers and

Question

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.