This is the data for the questions. I copied it from an excel file. You have 162

Question

This is the data for the questions. I copied it from an excel file. You have 162 monthly values of retails sales of hardware stores (HW) in the US.

a) Forecast HW for October 2015 using a 3-month prior moving average technique.

b) Compute root mean square error for the in-sample forecast using the same technique as in part (a) above.

c) Find the 2-decimal point smoothing constant which gives teh best fit for (based on the root mean square error criterion) the in-sample forecast using the exponential smoothing technique.

d) How does roo mean square error for the exponential smoothing technique compare to its value for the 3-period prior moving average technique.

e) Forecast HW in July 2015 using the exponential smoothing technique using the smoothing constant you found in part c.

f) Estimate and report a linear trend component for the HW time series using the ordinary least squares (OLS) technique.

g) Compare the trend value of your series for September 2008 with its aactual value int hat month. What favtors might account for the difference betweent he trend value and the actual value of HW for September 2008.

h) Compute a seasonal index using a 12 month centered moving average fo the HW series. Based on your results, would you describe hardware as a seasonal business? Explain.

i) Do an in-sample forecast on Books sales using the multiplicative time series technique (assume the cyclical component is equal to 1 for every month).

j) Use the information contained in the following table to perform a forecast of HW for November and December 2015 using the multiplicative time series technique (Note: you will need to compute a trend comonent for these months using the equation you obtained in a.

t YR MO HW 1 2002 1 1154 2 2002 2 1094 3 2002 3 1292 4 2002 4 1534 5 2002 5 1685 6 2002 6 1600 7 2002 7 1555 8 2002 8 1467 9 2002 9 1322 10 2002 10 1403 11 2002 11 1398 12 2002 12 1458 13 2003 1 1185 14 2003 2 1110 15 2003 3 1338 16 2003 4 1492 17 2003 5 1747 18 2003 6 1670 19 2003 7 1622 20 2003 8 1544 21 2003 9 1492 22 2003 10 1507 23 2003 11 1443 24 2003 12 1524 25 2004 1 1197 26 2004 2 1138 27 2004 3 1421 28 2004 4 1636 29 2004 5 1801 30 2004 6 1736 31 2004 7 1689 32 2004 8 1571 33 2004 9 1535 34 2004 10 1496 35 2004 11 1489 36 2004 12 1591 37 2005 1 1247 38 2005 2 1173 39 2005 3 1445 40 2005 4 1691 41 2005 5 1805 42 2005 6 1776 43 2005 7 1615 44 2005 8 1613 45 2005 9 1575 46 2005 10 1632 47 2005 11 1606 48 2005 12 1703 49 2006 1 1343 50 2006 2 1279 51 2006 3 1554 52 2006 4 1755 53 2006 5 2004 54 2006 6 1877 55 2006 7 1752 56 2006 8 1726 57 2006 9 1598 58 2006 10 1654 59 2006 11 1691 60 2006 12 1750 61 2007 1 1451 62 2007 2 1380 63 2007 3 1710 64 2007 4 1777 65 2007 5 2092 66 2007 6 1965 67 2007 7 1726 68 2007 8 1735 69 2007 9 1589 70 2007 10 1667 71 2007 11 1678 72 2007 12 1739 73 2008 1 1420 74 2008 2 1340 75 2008 3 1539 76 2008 4 1806 77 2008 5 2083 78 2008 6 1968 79 2008 7 1797 80 2008 8 1698 81 2008 9 1611 82 2008 10 1695 83 2008 11 1610 84 2008 12 1677 85 2009 1 1360 86 2009 2 1244 87 2009 3 1517 88 2009 4 1753 89 2009 5 1985 90 2009 6 1788 91 2009 7 1627 92 2009 8 1545 93 2009 9 1480 94 2009 10 1573 95 2009 11 1485 96 2009 12 1649 97 2010 1 1304 98 2010 2 1259 99 2010 3 1553 100 2010 4 1777 101 2010 5 1875 102 2010 6 1761 103 2010 7 1658 104 2010 8 1544 105 2010 9 1494 106 2010 10 1564 107 2010 11 1672 108 2010 12 1741 109 2011 1 1355 110 2011 2 1272 111 2011 3 1539 112 2011 4 1796 113 2011 5 2093 114 2011 6 1977 115 2011 7 1817 116 2011 8 1842 117 2011 9 1688 118 2011 10 1774 119 2011 11 1768 120 2011 12 1800 121 2012 1 1491 122 2012 2 1432 123 2012 3 1799 124 2012 4 2023 125 2012 5 2330 126 2012 6 2035 127 2012 7 1810 128 2012 8 1771 129 2012 9 1606 130 2012 10 1804 131 2012 11 1780 132 2012 12 1753 133 2013 1 1479 134 2013 2 1402 135 2013 3 1595 136 2013 4 1825 137 2013 5 1965 138 2013 6 1844 139 2013 7 1738 140 2013 8 1726 141 2013 9 1604 142 2013 10 1744 143 2013 11 1680 144 2013 12 1730 145 2014 1 1565 146 2014 2 1497 147 2014 3 1757 148 2014 4 1877 149 2014 5 2075 150 2014 6 1925 151 2014 7 1904 152 2014 8 1848 153 2014 9 1797 154 2014 10 1956 155 2014 11 1850 156 2014 12 1931 157 2015 1 1756 158 2015 2 1686 159 2015 3 1994 160 2015 4 2162 161 2015 5 2276 162 2015 6 2144

Explanation / Answer

Are you looking for a holt-winters forecast? I am not sure as to what do you mean by HW time-series

However, this video may help if you are looking for holt-winters method.

https://www.youtube.com/watch?v=qpiWJaeJPtA

If you have further doubts or need clarifications, feel free to ask.

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