Suljettu

Analyze some Data

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

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.

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 (α) 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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