# Assume you are interested in studying the behaviors of this (Ford) TS, please do the following to…

Under Session 3 there is a dataset named “stockprices.xls,” which contains historical time-series of daily closing stock price of Ford (column E in the spreadsheet). Assume you are interested in studying the behaviors of this (Ford) TS, please do the following to carry out a routine analysis. (For all hypothesis tests, please use significance level of a = 0.05). 1). Obtain a time-series plot of the data (only TS plot of Ford stock prices), and discuss what kind(s) of pattern you can identify from the plot. 2). Using Minitab, obtain the ACF function (plot) of Ford stock prices. 3). Using Bartlett test we discussed in the class, conduct test to see whether there exist significant autocorrelation at lag 1 and lag 6. In so doing, list your null and alternative hypotheses and show your test statistics which lead you to the conclusion. You must provide corresponding outputs from Minitab, from which you obtain estimates of autocorrelation coefficients at different lags. 4). Discuss whether Ford stock price time-series is a stationary time series, is it a white noise time series? (Check whether the definition conditions are violated.) 5). Check (using Portmanteau ?2 test) whether you can use a random walk model to represent the characteristics of Ford stock price TS.

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prices
Tbill_rate
MSFT
SP500
GE
FORD
Day
Month
Year
3.06
2.52
469.10
6.72
8.16
1.00
11.00
93.00
3.12
2.50
468.44
6.69
8.31
2.00
11.00
93.00
3.08
2.45
463.02
6.64
8.24
3.00
11.00
93.00
3.07
2.38
457.49
6.46
7.98
4.00
11.00
93.00
3.07
2.45
459.57
6.49
7.94
5.00
11.00
93.00
3.06
2.45
460.21
6.46
8.02
8.00
11.00
93.00
3.11
2.44
460.33
6.51
7.94
9.00
11.00
93.00
3.11
2.53
463.72
6.52
8.05
10.00
11.00
93.00
3.11
2.55
465.39
6.47
8.07
12.00
11.00
93.00
3.11
2.51
463.75
6.50
8.10
15.00
11.00
93.00
3.09
2.56
466.74
6.65
8.10
16.00
11.00
93.00
3.09
2.52
464.81
6.66
8.07
17.00
11.00
93.00
3.10
2.50
463.62
6.72
8.15
18.00
11.00
93.00
3.11
2.50
462.60
6.77
8.05
19.00
11.00
93.00
3.12
2.40
459.13
6.77
7.77
22.00
11.00
93.00
3.11
2.41
461.03
6.79
7.77
23.00
11.00
93.00
3.11
2.45
462.36
6.74
7.95
24.00
11.00
93.00
3.10
2.46
463.06
6.79
7.98
26.00
11.00
93.00
3.11
2.48
461.90
6.75
8.05
29.00
11.00
93.00
3.14
2.49
461.79
6.78
7.92
30.00
11.00
93.00
3.11
2.54
461.89
6.80
8.16
1.00
12.00
93.00
3.11
2.58
463.11
6.89
8.11
2.00
12.00
93.00
3.11
2.67
464.89
6.90
8.23
3.00
12.00
93.00
3.09
2.64
466.43
6.92
8.20
6.00
12.00
93.00
3.08
2.66
466.76
6.88
8.26
7.00
12.00
93.00
3.08
2.60
466.29
6.92
8.24
8.00
12.00
93.00
3.05
2.53
464.18
6.96
8.28
9.00
12.00
93.00
3.05
2.55
463.93
7.00
8.39
10.00
12.00
93.00
3.04
2.53
465.70
6.96
8.44
13.00
12.00
93.00
3.04
2.49
463.06
7.06
8.28
14.00
12.00
93.00
3.03
2.48
461.84
7.12
8.20
15.00
12.00
93.00
3.02
2.49
463.34
7.13
8.15
16.00
12.00
93.00
3.03
2.51
466.38
7.25
8.16
17.00
12.00
93.00
3.05
2.55
465.85
7.19
8.18
20.00
12.00
93.00
3.07
2.57
465.30
7.26
8.44
21.00
12.00
93.00
3.05
2.53
467.32
7.26
8.55
22.00
12.00
93.00
3.05
2.52
467.38
7.31
8.46
23.00
12.00

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