a) Using the country of the United states, collect data on the quarterly (not seasonally adjusted) consumer price index rate for 2001 – 2020. Data is presented in this table below Frequency: Quarterly observation_date USACPIALLQINMEI 2001-01-01 74.1297037765224 2001-04-01 74.9032066616881 2001-07-01 75.0016524834365 2001-10-01 74.7906971511185 2002-01-01 75.0579072387213 2002-04-01 75.8736011903506 2002-07-01 76.1970660332381 2002-10-01 76.4361487431984 2003-01-01 77.2096516283642 2003-04-01 77.4909254047881 2003-07-01 77.8706450029604 2003-10-01 77.8847086917816 2004-01-01 78.5878931328414 2004-04-01 79.7129882385370 2004-07-01 79.9942620149610 2004-10-01 80.4724274348816 2005-01-01 80.9787202324446 2005-04-01 82.0616242716767 2005-07-01 83.0601461779816 2005-10-01 83.4820568426175 2006-01-01 83.9320948848957 2006-04-01 85.3525274558365 2006-07-01 85.8306928757571 2006-10-01 85.0993810570550 2007-01-01 85.9666887466581 2007-04-01 87.6149530765022 2007-07-01 87.8567078873386 2007-10-01 88.4815575816643 2008-01-01 89.4873926061562 2008-04-01 91.4519492975891 2008-07-01 92.5155860831361 2008-10-01 89.8987555041762 2009-01-01 89.4513895627740 2009-04-01 90.3998447368754 2009-07-01 91.0137247539206 2009-10-01 91.1968339823726 2010-01-01 91.5629118023883 2010-04-01 91.9979016976279 2010-07-01 92.0836901994372 2010-10-01 92.3552600305744 2011-01-01 93.5233900240630 2011-04-01 95.1537934691042 2011-07-01 95.5425138281220 2011-10-01 95.3972359225991 2012-01-01 96.1562532082790 2012-04-01 96.9519767217822 2012-07-01 97.1646196967587 2012-10-01 97.1996382819235 2013-01-01 97.7734367858283 2013-04-01 98.3023721223934 2013-07-01 98.6739347810494 2013-10-01 98.3985677539304 2014-01-01 99.1475998205473 2014-04-01 100.3184019149120 2014-07-01 100.4334428894690 2014-10-01 99.6266090617972 2015-01-01 99.0854383159576 2015-04-01 100.2800080444300 2015-07-01 100.5434209360510 2015-10-01 100.0911327035610 2016-01-01 100.1558256721390 2016-04-01 101.3300030518200 2016-07-01 101.6671096728650 2016-10-01 101.8933944259980 2017-01-01 102.6991031585640 2017-04-01 103.2572909678770 2017-07-01 103.6668255863500 2017-10-01 104.0510455649450 2018-01-01 104.9730610040630 2018-04-01 106.0575120490650 2018-07-01 106.4046038891720 2018-10-01 106.3434268428000 2019-01-01 106.6998007175290 2019-04-01 107.9786119420410 2019-07-01 108.2746525917270 2019-10-01 108.5052970883940 2020-01-01 108.9603980586480 2020-04-01 108.3719733183700 2020-07-01 109.5981863466900 2020-10-01 109.8502076503650 (i) Compute the CMAs for the four quarters of 2002 and 2003 manually (Show ALL workings). ( this should be by using the CPI and MA to compute the CMAs) (ii) Use EXCEL to complete the calculation of the CMAs and the seasonal indices for 2001 - 2020. Provide a table with the de-seasonalized input variables. Year Quarter CPI rate Seasonal Index Period De-Seasonalized CPI rate (iii)Using R-Studio, estimate a regression equation to determine the effect of time (period) on the De-Seasonalized CPI rate. Write down the forecast equation. (All codes and regression output should be provided). Assist with all questions

a) Using the country of the United states, collect data on the quarterly (not seasonally adjusted) consumer price index rate for 2001 – 2020. Data is presented in this table below Frequency: Quarterly observation_date USACPIALLQINMEI 2001-01-01 74.1297037765224 2001-04-01 74.9032066616881 2001-07-01 75.0016524834365 2001-10-01 74.7906971511185 2002-01-01 75.0579072387213 2002-04-01 75.8736011903506 2002-07-01 76.1970660332381 2002-10-01 76.4361487431984 2003-01-01 77.2096516283642 2003-04-01 77.4909254047881 2003-07-01 77.8706450029604 2003-10-01 77.8847086917816 2004-01-01 78.5878931328414 2004-04-01 79.7129882385370 2004-07-01 79.9942620149610 2004-10-01 80.4724274348816 2005-01-01 80.9787202324446 2005-04-01 82.0616242716767 2005-07-01 83.0601461779816 2005-10-01 83.4820568426175 2006-01-01 83.9320948848957 2006-04-01 85.3525274558365 2006-07-01 85.8306928757571 2006-10-01 85.0993810570550 2007-01-01 85.9666887466581 2007-04-01 87.6149530765022 2007-07-01 87.8567078873386 2007-10-01 88.4815575816643 2008-01-01 89.4873926061562 2008-04-01 91.4519492975891 2008-07-01 92.5155860831361 2008-10-01 89.8987555041762 2009-01-01 89.4513895627740 2009-04-01 90.3998447368754 2009-07-01 91.0137247539206 2009-10-01 91.1968339823726 2010-01-01 91.5629118023883 2010-04-01 91.9979016976279 2010-07-01 92.0836901994372 2010-10-01 92.3552600305744 2011-01-01 93.5233900240630 2011-04-01 95.1537934691042 2011-07-01 95.5425138281220 2011-10-01 95.3972359225991 2012-01-01 96.1562532082790 2012-04-01 96.9519767217822 2012-07-01 97.1646196967587 2012-10-01 97.1996382819235 2013-01-01 97.7734367858283 2013-04-01 98.3023721223934 2013-07-01 98.6739347810494 2013-10-01 98.3985677539304 2014-01-01 99.1475998205473 2014-04-01 100.3184019149120 2014-07-01 100.4334428894690 2014-10-01 99.6266090617972 2015-01-01 99.0854383159576 2015-04-01 100.2800080444300 2015-07-01 100.5434209360510 2015-10-01 100.0911327035610 2016-01-01 100.1558256721390 2016-04-01 101.3300030518200 2016-07-01 101.6671096728650 2016-10-01 101.8933944259980 2017-01-01 102.6991031585640 2017-04-01 103.2572909678770 2017-07-01 103.6668255863500 2017-10-01 104.0510455649450 2018-01-01 104.9730610040630 2018-04-01 106.0575120490650 2018-07-01 106.4046038891720 2018-10-01 106.3434268428000 2019-01-01 106.6998007175290 2019-04-01 107.9786119420410 2019-07-01 108.2746525917270 2019-10-01 108.5052970883940 2020-01-01 108.9603980586480 2020-04-01 108.3719733183700 2020-07-01 109.5981863466900 2020-10-01 109.8502076503650 (i) Compute the CMAs for the four quarters of 2002 and 2003 manually (Show ALL workings). ( this should be by using the CPI and MA to compute the CMAs) (ii) Use EXCEL to complete the calculation of the CMAs and the seasonal indices for 2001 - 2020. Provide a table with the de-seasonalized input variables. Year Quarter CPI rate Seasonal Index Period De-Seasonalized CPI rate (iii)Using R-Studio, estimate a regression equation to determine the effect of time (period) on the De-Seasonalized CPI rate. Write down the forecast equation. (All codes and regression output should be provided). Assist with all questions

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Related questions

Question

a) Using the country of the United states, collect data on the quarterly (not seasonally
adjusted) consumer price index rate for 2001 – 2020. Data is presented in this table below

Frequency: Quarterly
observation_date	USACPIALLQINMEI
2001-01-01	74.1297037765224
2001-04-01	74.9032066616881
2001-07-01	75.0016524834365
2001-10-01	74.7906971511185
2002-01-01	75.0579072387213
2002-04-01	75.8736011903506
2002-07-01	76.1970660332381
2002-10-01	76.4361487431984
2003-01-01	77.2096516283642
2003-04-01	77.4909254047881
2003-07-01	77.8706450029604
2003-10-01	77.8847086917816
2004-01-01	78.5878931328414
2004-04-01	79.7129882385370
2004-07-01	79.9942620149610
2004-10-01	80.4724274348816
2005-01-01	80.9787202324446
2005-04-01	82.0616242716767
2005-07-01	83.0601461779816
2005-10-01	83.4820568426175
2006-01-01	83.9320948848957
2006-04-01	85.3525274558365
2006-07-01	85.8306928757571
2006-10-01	85.0993810570550
2007-01-01	85.9666887466581
2007-04-01	87.6149530765022
2007-07-01	87.8567078873386
2007-10-01	88.4815575816643
2008-01-01	89.4873926061562
2008-04-01	91.4519492975891
2008-07-01	92.5155860831361
2008-10-01	89.8987555041762
2009-01-01	89.4513895627740
2009-04-01	90.3998447368754
2009-07-01	91.0137247539206
2009-10-01	91.1968339823726
2010-01-01	91.5629118023883
2010-04-01	91.9979016976279
2010-07-01	92.0836901994372
2010-10-01	92.3552600305744
2011-01-01	93.5233900240630
2011-04-01	95.1537934691042
2011-07-01	95.5425138281220
2011-10-01	95.3972359225991
2012-01-01	96.1562532082790
2012-04-01	96.9519767217822
2012-07-01	97.1646196967587
2012-10-01	97.1996382819235
2013-01-01	97.7734367858283
2013-04-01	98.3023721223934
2013-07-01	98.6739347810494
2013-10-01	98.3985677539304
2014-01-01	99.1475998205473
2014-04-01	100.3184019149120
2014-07-01	100.4334428894690
2014-10-01	99.6266090617972
2015-01-01	99.0854383159576
2015-04-01	100.2800080444300
2015-07-01	100.5434209360510
2015-10-01	100.0911327035610
2016-01-01	100.1558256721390
2016-04-01	101.3300030518200
2016-07-01	101.6671096728650
2016-10-01	101.8933944259980
2017-01-01	102.6991031585640
2017-04-01	103.2572909678770
2017-07-01	103.6668255863500
2017-10-01	104.0510455649450
2018-01-01	104.9730610040630
2018-04-01	106.0575120490650
2018-07-01	106.4046038891720
2018-10-01	106.3434268428000
2019-01-01	106.6998007175290
2019-04-01	107.9786119420410
2019-07-01	108.2746525917270
2019-10-01	108.5052970883940
2020-01-01	108.9603980586480
2020-04-01	108.3719733183700
2020-07-01	109.5981863466900
2020-10-01	109.8502076503650

(i) Compute the CMAs for the four quarters of 2002 and 2003 manually (Show ALL
workings). ( this should be by using the CPI and MA to compute the CMAs)
(ii) Use EXCEL to complete the calculation of the CMAs and the seasonal indices for
2001 - 2020. Provide a table with the de-seasonalized input variables.

Year	Quarter	CPI rate	Seasonal Index	Period	De-Seasonalized CPI rate

(iii)Using R-Studio, estimate a regression equation to determine the effect of time
(period) on the De-Seasonalized CPI rate. Write down the forecast equation. (All
codes and regression output should be provided).

Assist with all questions

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