I need help with my assignment. Using the data on the currency exchange rates fo

Question

I need help with my assignment.

Using the data on the currency exchange rates for euro, please create the following time series models:

- Autoregressive model. Choose p yourself and explain your choice.

- multiple regression model. Make a pick of seasonality type you will be looking for, and explain your choice. Use the both models to predict the exchange rate for May 1, 2017.

here is the data https://www.dropbox.com/s/58jr8itjzw2b9kd/Rates2016_2017.xls?dl=0

it is full, it is requested to use the data for the euro exchange rates from July 1, 2017 through April 30,2017. BYN is the currency for which the Euro exchange rate is given. e.g. in B2 2.2210 is the amount BYN per 1 Euro.

updated the file with the data https://www.dropbox.com/s/sda8pczze8yw0hr/data.xlsx?dl=0

date euro exchange rate, byn 7/1/16 2.2210 7/2/16 2.2210 7/3/16 2.2210 7/4/16 2.2210 7/5/16 2.2207 7/6/16 2.2354 7/7/16 2.2299 7/8/16 2.2290 7/9/16 2.2279 7/10/16 2.2279 7/11/16 2.2279 7/12/16 2.2190 7/13/16 2.2275 7/14/16 2.2148 7/15/16 2.2151 7/16/16 2.2086 7/17/16 2.2086 7/18/16 2.2086 7/19/16 2.1904 7/20/16 2.1879 7/21/16 2.1770 7/22/16 2.1798 7/23/16 2.1887 7/24/16 2.1887 7/25/16 2.1887 7/26/16 2.1830 7/27/16 2.1954 7/28/16 2.1934 7/29/16 2.2089 7/30/16 2.2141 7/31/16 2.2141 8/1/16 2.2141 8/2/16 2.2102 8/3/16 2.2284 8/4/16 2.2331 8/5/16 2.2191 8/6/16 2.2053 8/7/16 2.2053 8/8/16 2.2053 8/9/16 2.1831 8/10/16 2.1769 8/11/16 2.1902 8/12/16 2.1896 8/13/16 2.1797 8/14/16 2.1797 8/15/16 2.1797 8/16/16 2.1764 8/17/16 2.1828 8/18/16 2.1833 8/19/16 2.1832 8/20/16 2.1853 8/21/16 2.1853 8/22/16 2.1853 8/23/16 2.1832 8/24/16 2.2030 8/25/16 2.2030 8/26/16 2.2068 8/27/16 2.2022 8/28/16 2.2022 8/29/16 2.2022 8/30/16 2.1897 8/31/16 2.1859 9/1/16 2.1851 9/2/16 2.1846 9/3/16 2.2028 9/4/16 2.2028 9/5/16 2.2028 9/6/16 2.1889 9/7/16 2.1895 9/8/16 2.1962 9/9/16 2.1940 9/10/16 2.1953 9/11/16 2.1953 9/12/16 2.1953 9/13/16 2.2030 9/14/16 2.1961 9/15/16 2.1957 9/16/16 2.1972 9/17/16 2.1943 9/18/16 2.1943 9/19/16 2.1943 9/20/16 2.1794 9/21/16 2.1824 9/22/16 2.1694 9/23/16 2.1719 9/24/16 2.1645 9/25/16 2.1645 9/26/16 2.1645 9/27/16 2.1718 9/28/16 2.1683 9/29/16 2.1636 9/30/16 2.1610 10/1/16 2.1551 10/2/16 2.1551 10/3/16 2.1551 10/4/16 2.1513 10/5/16 2.1427 10/6/16 2.1510 10/7/16 2.1451 10/8/16 2.1282 10/9/16 2.1282 10/10/16 2.1282 10/11/16 2.1391 10/12/16 2.1259 10/13/16 2.1187 10/14/16 2.1255 10/15/16 2.1201 10/16/16 2.1201 10/17/16 2.1201 10/18/16 2.1133 10/19/16 2.1129 10/20/16 2.1018 10/21/16 2.0914 10/22/16 2.0768 10/23/16 2.0768 10/24/16 2.0768 10/25/16 2.0704 10/26/16 2.0673 10/27/16 2.0744 10/28/16 2.0834 10/29/16 2.0820 10/30/16 2.0820 10/31/16 2.0820 11/1/16 2.0885 11/2/16 2.0901 11/3/16 2.1078 11/4/16 2.1157 11/5/16 2.1152 11/6/16 2.1152 11/7/16 2.1152 11/8/16 2.1152 11/9/16 2.1064 11/10/16 2.1332 11/11/16 2.0885 11/12/16 2.1139 11/13/16 2.1139 11/14/16 2.1139 11/15/16 2.1120 11/16/16 2.1109 11/17/16 2.0892 11/18/16 2.0937 11/19/16 2.0733 11/20/16 2.0733 11/21/16 2.0733 11/22/16 2.0661 11/23/16 2.0617 11/24/16 2.0720 11/25/16 2.0705 11/26/16 2.0804 11/27/16 2.0804 11/28/16 2.0804 11/29/16 2.0942 11/30/16 2.0902 12/1/16 2.1033 12/2/16 2.0815 12/3/16 2.1019 12/4/16 2.1019 12/5/16 2.1019 12/6/16 2.0897 12/7/16 2.1197 12/8/16 2.1220 12/9/16 2.1262 12/10/16 2.0967 12/11/16 2.0967 12/12/16 2.0967 12/13/16 2.0656 12/14/16 2.0765 12/15/16 2.0817 12/16/16 2.0710 12/17/16 2.0599 12/18/16 2.0599 12/19/16 2.0599 12/20/16 2.0632 12/21/16 2.0484 12/22/16 2.0372 12/23/16 2.0356 12/24/16 2.0338 12/25/16 2.0338 12/26/16 2.0338 12/27/16 2.0378 12/28/16 2.0374 12/29/16 2.0430 12/30/16 2.0450 12/31/16 2.0450 1/1/17 2.0450 1/2/17 2.0450 1/3/17 2.0450 1/4/17 2.0545 1/5/17 2.0534 1/6/17 2.0716 1/7/17 2.0676 1/8/17 2.0676 1/9/17 2.0676 1/10/17 2.0650 1/11/17 2.0810 1/12/17 2.0803 1/13/17 2.0774 1/14/17 2.0740 1/15/17 2.0740 1/16/17 2.0740 1/17/17 2.0699 1/18/17 2.0764 1/19/17 2.0778 1/20/17 2.0719 1/21/17 2.0754 1/22/17 2.0711 1/23/17 2.0711 1/24/17 2.0786 1/25/17 2.0746 1/26/17 2.0676 1/27/17 2.0741 1/28/17 2.0699 1/29/17 2.0699 1/30/17 2.0699 1/31/17 2.0723 2/1/17 2.0703 2/2/17 2.0881 2/3/17 2.0875 2/4/17 2.0724 2/5/17 2.0724 2/6/17 2.0724 2/7/17 2.0557 2/8/17 2.0481 2/9/17 2.0490 2/10/17 2.0437 2/11/17 2.0260 2/12/17 2.0260 2/13/17 2.0260 2/14/17 2.0111 2/15/17 1.9956 2/16/17 1.9716 2/17/17 1.9788 2/18/17 1.9952 2/19/17 1.9952 2/20/17 1.9952 2/21/17 1.9935 2/22/17 1.9833 2/23/17 1.9698 2/24/17 1.9782 2/25/17 1.9875 2/26/17 1.9875 2/27/17 1.9875 2/28/17 1.9893 3/1/17 1.9973 3/2/17 1.9979 3/3/17 2.0003 3/4/17 2.0131 3/5/17 2.0131 3/6/17 2.0131 3/7/17 2.0203 3/8/17 2.0123 3/9/17 2.0123 3/10/17 2.0153 3/11/17 2.0292 3/12/17 2.0292 3/13/17 2.0292 3/14/17 2.0456 3/15/17 2.0371 3/16/17 2.0357 3/17/17 2.0341 3/18/17 2.0356 3/19/17 2.0356 3/20/17 2.0356 3/21/17 2.0253 3/22/17 2.0266 3/23/17 2.0339 3/24/17 2.0326 3/25/17 2.0268 3/26/17 2.0268 3/27/17 2.0268 3/28/17 2.0351 3/29/17 2.0347 3/30/17 2.0242 3/31/17 2.0111 4/1/17 2.0002 4/2/17 2.0002 4/3/17 2.0002 4/4/17 2.0018 4/5/17 2.0085 4/6/17 2.0083 4/7/17 2.0118 4/8/17 2.0131 4/9/17 2.0131 4/10/17 2.0131 4/11/17 2.0134 4/12/17 2.0085 4/13/17 2.0080 4/14/17 2.0087 4/15/17 1.9977 4/16/17 1.9977 4/17/17 1.9977 4/18/17 2.0014 4/19/17 2.0008 4/20/17 2.0162 4/21/17 2.0208 4/22/17 2.0122 4/23/17 2.0122 4/24/17 2.0122 4/25/17 2.0122 4/26/17 2.0122 4/27/17 2.0409 4/28/17 2.0459 4/29/17 2.0414 4/30/17 2.0371

Explanation / Answer

Using Minitab the AR(1) or ARIMA(1,0,0) is given by

Final Estimates of Parameters

Type Coef SE Coef T P
AR 1 0.9993 0.0068 145.92 0.000
Constant 0.001304 0.001966 0.66 0.507
Mean 1.824 2.748

Number of observations: 304
Residuals: SS = 0.0237022 (backforecasts excluded)
MS = 0.0000785 DF = 302

Using Minitab the AR(2) or ARIMA(2,0,0) is given by

Final Estimates of Parameters

Type Coef SE Coef T P
AR 1 0.8904 0.0574 15.52 0.000
AR 2 0.1082 0.0572 1.89 0.060
Constant 0.002921 0.001068 2.73 0.007
Mean 1.9627 0.7179

Number of observations: 304
Residuals: SS = 0.0232785 (backforecasts excluded)
MS = 0.0000773 DF = 301

Since p-value of AR2 is 0.06 which is > 0 .05 therefore we can take AR(1)

Forecasts from period 304

Forecost = 0.9993 * 2.0371 + 0.001304 = 2.03697

From minitab

95% Limits
Period Forecast Lower Upper Actual
305 2.03695 2.01958 2.05431

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