Exam 16: Time-Series Forecasting

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The cyclical component of a time series

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SCENARIO 16-15-B You are the CEO of a diary company. The total milk production (in gallons) from your company over the past 30 years are presented below and also contained in the Excel file SCENARIO 16- 15-B.XLSX. Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Milk 150201 193718 212520 214553 237507 248069 241824 234627 252049 252029 Prod 263449 260689 247900 260059 268197 249477 246216 265236 256364 241705 245932 243529 241551 247697 248454 241974 235823 243517 238490 248606 You want to predict your company's future total milk production using the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model. -Referring to Scenario 16-15-B, construct a scatter plot (i.e., a time-series plot) with year on the horizontal X-axis.

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SCENARIO 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year. Month Complaints January 36 February 45 March 81 April 90 May 108 June 144 -Referring to Scenario 16-3, if this series is smoothed using exponential smoothing with a smoothing constant of 1/3, what would be the third value?

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SCENARIO 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows. Year Cases of Wine 2005 270 2006 356 2007 398 2008 456 2009 358 2010 500 2011 410 2012 376 -Referring to Scenario 16-4, exponentially smooth the wine sales with a weight or smoothing constant of 0.4.

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SCENARIO 16-5 The number of passengers arriving at San Francisco on the Amtrak cross-country express on 6 successive Mondays were: 60, 72, 96, 84, 36, and 48. -Referring to Scenario 16-5, the number of arrivals will be smoothed with a 3-term moving average. The first smoothed value will be __________.

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SCENARIO 16-13 Given below is the monthly time series data for U.S. retail sales of building materials over a specific year. Month Retail Sales 1 6,594 2 6,610 3 8,174 4 9,513 5 10,595 6 10,415 7 9,949 8 9,810 9 9,637 10 9,732 11 9,214 12 9,201 The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1st month is 0:  Linear trend model: \text { Linear trend model: } Coefficients Standard Error t Stat P-value Intercept 7950.7564 617.6342 12.8729 0.0000 Coded Month 212.6503 95.1145 2.2357 0.0494  Quadratic trend model: \text { Quadratic trend model: }  SCENARIO 16-13 Given below is the monthly time series data for U.S. retail sales of building materials over a specific year.  \begin{array} { | c | c | }  \hline \text { Month } & \text { Retail Sales } \\ \hline 1 & 6,594 \\ \hline 2 & 6,610 \\ \hline 3 & 8,174 \\ \hline 4 & 9,513 \\ \hline 5 & 10,595 \\ \hline 6 & 10,415 \\ \hline 7 & 9,949 \\ \hline 8 & 9,810 \\ \hline 9 & 9,637 \\ \hline 10 & 9,732 \\ \hline 11 & 9,214 \\ \hline 12 & 9,201 \\ \hline \end{array}  The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1st month is 0:  \text { Linear trend model: }   \begin{array}{lrrrr}  & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \text { P-value } \\ \hline \text { Intercept } & 7950.7564 & 617.6342 & 12.8729 & 0.0000 \\ \text { Coded Month } & 212.6503 & 95.1145 & 2.2357 & 0.0494 \end{array}    \text { Quadratic trend model: }       \text { Exponential trend model: }   \begin{array}{lrrrr} \hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \text { P-value } \\ \hline \text { Intercept } & 3.8912 & 0.0315 & 123.3674 & 0.0000 \\ \text { Coded Month } & 0.0116 & 0.0049 & 2.3957 & 0.0376 \end{array}     \text { First-order autoregressive: }   \begin{array}{lrrrr}  & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & {\text { P-value }} \\ \hline \text { Intercept } & 3132.0951 & 1287.2899 & 2.4331 & 0.0378 \\ \text { YLag1 } & 0.6823 & 0.1398 & 4.8812 & 0.0009 \\ \hline \end{array}    -Referring to Scenario 16-13, what is the exponentially smoothed value for the  12 ^ { \text {th } }  month using a smoothing coefficient of W = 0.25 if the exponentially smooth value for the  10 ^ { \mathrm { th } }  and  11 ^ { \text {th } }  month are 9,477.7776 and 9,411.8332, respectively?  Exponential trend model: \text { Exponential trend model: } Coefficients Standard Error t Stat P-value Intercept 3.8912 0.0315 123.3674 0.0000 Coded Month 0.0116 0.0049 2.3957 0.0376  First-order autoregressive: \text { First-order autoregressive: } Coefficients Standard Error t Stat P-value Intercept 3132.0951 1287.2899 2.4331 0.0378 YLag1 0.6823 0.1398 4.8812 0.0009 -Referring to Scenario 16-13, what is the exponentially smoothed value for the 12th 12 ^ { \text {th } } month using a smoothing coefficient of W = 0.25 if the exponentially smooth value for the 10th10 ^ { \mathrm { th } } and 11th 11 ^ { \text {th } } month are 9,477.7776 and 9,411.8332, respectively?

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SCENARIO 16-5 The number of passengers arriving at San Francisco on the Amtrak cross-country express on 6 successive Mondays were: 60, 72, 96, 84, 36, and 48. -Referring to Scenario 16-5, the number of arrivals will be exponentially smoothed with a smoothing constant of 0.1. Then the forecast for the seventh Monday will be __________.

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Microsoft Excel was used to obtain the following quadratic trend equation:  Sales =10010X+15X2\text { Sales } = 100 - 10 X + 15 X ^ { 2 } . The data used was from 2004 through 2013 coded 0 to 9. The forecast for 2014 is __________.

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SCENARIO 16-3 The following table contains the number of complaints received in a department store for the first 6 months of last year. Month Complaints January 36 February 45 March 81 April 90 May 108 June 144 -Referring to Scenario 16-3, if a three-month moving average is used to smooth this series, what would be the last calculated value?

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SCENARIO 16-15-B You are the CEO of a diary company. The total milk production (in gallons) from your company over the past 30 years are presented below and also contained in the Excel file SCENARIO 16- 15-B.XLSX. Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Milk 150201 193718 212520 214553 237507 248069 241824 234627 252049 252029 Prod 263449 260689 247900 260059 268197 249477 246216 265236 256364 241705 245932 243529 241551 247697 248454 241974 235823 243517 238490 248606 You want to predict your company's future total milk production using the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model. -Referring to Scenario 16-15-B, what is the value of the t test statistic for testing the appropriateness of the third-order autoregressive model?

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SCENARIO 16-4 The number of cases of merlot wine sold by a Paso Robles winery in an 8-year period follows. Year Cases of Wine 2005 270 2006 356 2007 398 2008 456 2009 358 2010 500 2011 410 2012 376 -Referring to Scenario 16-4, exponential smoothing with a weight or smoothing constant of 0.2 will be used to forecast wine sales. The forecast for 2013 is __________.

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SCENARIO 16-15-A You are the CEO of a diary company. The total milk production (in gallons) from your company over the past 30 years are presented below and also contained in the Excel file SCENARIO 16- 15-A.XLSX. Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Milk 150201 172719 171357 157121 155727 152974 153443 158548 162614 164210 Prod 159127 153866 165992 177843 167477 163821 161700 170348 174105 185103 184670 173385 159695 173641 165706 171164 168706 150684 179314 163802 You want to predict your company's future total milk production using the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model. -Referring to Scenario 16-15-A, what is the exponentially smoothed value for 1997 using a smoothing coefficient of W = 0.5?

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A first-order autoregressive model for stock sales is:  Sales i=800+1.2( Sales )i1\text { Sales } _ { i } = 800 + 1.2 \left( \text { Sales } )_ { i - 1 } \right. \text {. } . If sales in 2012 is 6,000, the forecast of sales for 2013 is __________.

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SCENARIO 16-14 A contractor developed a multiplicative time-series model to forecast the number of contracts in future quarters, using quarterly data on number of contracts during the 3-year period from 2011 to -Referring to Scenario 16-14, to obtain a forecast for the fourth quarter of 2014 using the model, which of the following sets of values should be used in the regression equation? a) X=15,Q1=0,Q2=0,Q3=0X = 15 , Q _ { 1 } = 0 , Q _ { 2 } = 0 , Q _ { 3 } = 0 b) X=15,Q1=1,Q2=0,Q3=0X = 15 , Q _ { 1 } = 1 , Q _ { 2 } = 0 , Q _ { 3 } = 0 c) X=16,Q1=0,Q2=0,Q3=0X = 16 , Q _ { 1 } = 0 , Q _ { 2 } = 0 , Q _ { 3 } = 0 d) X=16,Q1=1,Q2=0,Q3=0X = 16 , Q _ { 1 } = 1 , Q _ { 2 } = 0 , Q _ { 3 } = 0

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SCENARIO 16-8 The manager of a marketing consulting firm has been examining his company's yearly profits. He believes that these profits have been showing a quadratic trend since 1994. He uses Microsoft Excel to obtain the partial output below. The dependent variable is profit (in thousands of dollars), while the independent variables are coded years and squared of coded years, where 1994 is coded as 0, 1995 is coded as 1, etc. SCENARIO 16-8 The manager of a marketing consulting firm has been examining his company's yearly profits. He believes that these profits have been showing a quadratic trend since 1994. He uses Microsoft Excel to obtain the partial output below. The dependent variable is profit (in thousands of dollars), while the independent variables are coded years and squared of coded years, where 1994 is coded as 0, 1995 is coded as 1, etc.   -Referring to Scenario 16-8, the fitted value for 1994 is __________. -Referring to Scenario 16-8, the fitted value for 1994 is __________.

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SCENARIO 16-15-A You are the CEO of a diary company. The total milk production (in gallons) from your company over the past 30 years are presented below and also contained in the Excel file SCENARIO 16- 15-A.XLSX. Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Milk 150201 172719 171357 157121 155727 152974 153443 158548 162614 164210 Prod 159127 153866 165992 177843 167477 163821 161700 170348 174105 185103 184670 173385 159695 173641 165706 171164 168706 150684 179314 163802 You want to predict your company's future total milk production using the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model. -Referring to Scenario 16-15-A, what is your forecast for 2016 using the exponential-trend model?

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SCENARIO 16-13 Given below is the monthly time series data for U.S. retail sales of building materials over a specific year. Month Retail Sales 1 6,594 2 6,610 3 8,174 4 9,513 5 10,595 6 10,415 7 9,949 8 9,810 9 9,637 10 9,732 11 9,214 12 9,201 The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1st month is 0:  Linear trend model: \text { Linear trend model: } Coefficients Standard Error t Stat P-value Intercept 7950.7564 617.6342 12.8729 0.0000 Coded Month 212.6503 95.1145 2.2357 0.0494  Quadratic trend model: \text { Quadratic trend model: }  SCENARIO 16-13 Given below is the monthly time series data for U.S. retail sales of building materials over a specific year.  \begin{array} { | c | c | }  \hline \text { Month } & \text { Retail Sales } \\ \hline 1 & 6,594 \\ \hline 2 & 6,610 \\ \hline 3 & 8,174 \\ \hline 4 & 9,513 \\ \hline 5 & 10,595 \\ \hline 6 & 10,415 \\ \hline 7 & 9,949 \\ \hline 8 & 9,810 \\ \hline 9 & 9,637 \\ \hline 10 & 9,732 \\ \hline 11 & 9,214 \\ \hline 12 & 9,201 \\ \hline \end{array}  The results of the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model are presented below in which the coded month for the 1st month is 0:  \text { Linear trend model: }   \begin{array}{lrrrr}  & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \text { P-value } \\ \hline \text { Intercept } & 7950.7564 & 617.6342 & 12.8729 & 0.0000 \\ \text { Coded Month } & 212.6503 & 95.1145 & 2.2357 & 0.0494 \end{array}    \text { Quadratic trend model: }       \text { Exponential trend model: }   \begin{array}{lrrrr} \hline & \text { Coefficients } & \text { Standard Error } & \text { t Stat } & \text { P-value } \\ \hline \text { Intercept } & 3.8912 & 0.0315 & 123.3674 & 0.0000 \\ \text { Coded Month } & 0.0116 & 0.0049 & 2.3957 & 0.0376 \end{array}     \text { First-order autoregressive: }   \begin{array}{lrrrr}  & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & {\text { P-value }} \\ \hline \text { Intercept } & 3132.0951 & 1287.2899 & 2.4331 & 0.0378 \\ \text { YLag1 } & 0.6823 & 0.1398 & 4.8812 & 0.0009 \\ \hline \end{array}    -Referring to Scenario 16-13, what is the exponentially smoothed value for the  12 ^ { \text {th } }  month using a smoothing coefficient of W = 0.5 if the exponentially smooth value for the  10 ^ { \text {th } }  and 11th month are 9,746.3672 and 9,480.1836, respectively?  Exponential trend model: \text { Exponential trend model: } Coefficients Standard Error t Stat P-value Intercept 3.8912 0.0315 123.3674 0.0000 Coded Month 0.0116 0.0049 2.3957 0.0376  First-order autoregressive: \text { First-order autoregressive: } Coefficients Standard Error t Stat P-value Intercept 3132.0951 1287.2899 2.4331 0.0378 YLag1 0.6823 0.1398 4.8812 0.0009 -Referring to Scenario 16-13, what is the exponentially smoothed value for the 12th 12 ^ { \text {th } } month using a smoothing coefficient of W = 0.5 if the exponentially smooth value for the 10th 10 ^ { \text {th } } and 11th month are 9,746.3672 and 9,480.1836, respectively?

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SCENARIO 16-15-A You are the CEO of a diary company. The total milk production (in gallons) from your company over the past 30 years are presented below and also contained in the Excel file SCENARIO 16- 15-A.XLSX. Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Milk 150201 172719 171357 157121 155727 152974 153443 158548 162614 164210 Prod 159127 153866 165992 177843 167477 163821 161700 170348 174105 185103 184670 173385 159695 173641 165706 171164 168706 150684 179314 163802 You want to predict your company's future total milk production using the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model. -Referring to Scenario 16-15-A, what is the exponentially smoothed value for 1986 using a smoothing coefficient of W = 0.25?

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SCENARIO 16-15-B You are the CEO of a diary company. The total milk production (in gallons) from your company over the past 30 years are presented below and also contained in the Excel file SCENARIO 16- 15-B.XLSX. Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Milk 150201 193718 212520 214553 237507 248069 241824 234627 252049 252029 Prod 263449 260689 247900 260059 268197 249477 246216 265236 256364 241705 245932 243529 241551 247697 248454 241974 235823 243517 238490 248606 You want to predict your company's future total milk production using the linear trend, quadratic trend, exponential trend, first-order autoregressive, second-order autoregressive and third-order autoregressive model. -Referring to Scenario 16-15-B, you can reject the null hypothesis for testing the appropriateness of the third-order autoregressive model at the 5% level of significance.

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The method of least squares is used on time-series data for

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