Using library astsa in RStudio provide r code for the following: Provide R code. Generate a simple
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Using library astsa in RStudio provide r code for the following: Provide R code.
Generate a simple random walk data. Apply first-order differencing. Did it remove non-stationarity? Apply second-order differencing. Did it remove non-stationarity?
Consider the immigration data from BB. Stationary? If not stationary then try to stationarize the data.
Here's the immigration data:
yr,count 1820,8385 1821,9127 1822,6911 1823,6354 1824,7912 1825,10199 1826,10837 1827,18875 1828,27382 1829,22520 1830,23322 1831,22633 1832,60482 1833,58640 1834,65365 1835,45374 1836,76242 1837,79340 1838,38914 1839,68069 1840,84066 1841,80289 1842,104565 1843,52496 1844,78615 1845,114371 1846,154416 1847,234968 1848,226527 1849,297024 1850,369980 1851,379466 1852,371603 1853,368645 1854,427833 1855,200877 1856,200436 1857,251306 1858,123126 1859,121282 1860,153640 1861,91918 1862,91985 1863,176282 1864,193418 1865,248120 1866,318568 1867,315722 1868,138840 1869,352768 1870,387203 1871,321350 1872,404806 1873,459803 1874,313339 1875,227498 1876,169986 1877,141857 1878,138469 1879,177826 1880,457257 1881,669431 1882,788992 1883,603322 1884,518592 1885,395346 1886,334203 1887,490109 1888,546889 1889,444427 1890,455302 1891,560319 1892,579663 1893,439730 1894,285631 1895,258536 1896,343267 1897,230832 1898,229299 1899,311715 1900,448572 1901,487918 1902,648743 1903,857046 1904,812870 1905,1026499 1906,1100735 1907,1285349 1908,782870 1909,751786 1910,1041570 1911,878587 1912,838172 1913,1197892 1914,1218480 1915,326700 1916,298826 1917,295403 1918,110618 1919,141132 1920,430001 1921,805228 1922,309556 1923,522919 1924,706896 1925,294314 1926,304488 1927,335175 1928,307255 1929,279678 1930,241700 1931,97139 1932,35576 1933,23068 1934,29470 1935,34956 1936,36329 1937,50244 1938,67895 1939,82998 1940,70756 1941,51776 1942,28781 1943,23725 1944,28551 1945,38119 1946,108721 1947,147292 1948,170570 1949,188317 1950,249187 1951,205717 1952,265520 1953,170434 1954,208177 1955,237790 1956,321625 1957,326867 1958,253265 1959,260686 1960,265398 1961,271344 1962,283763 1963,306260 1964,292248 1965,296697 1966,323040 1967,361972 1968,454448 1969,358579 1970,373326 1971,370478 1972,384685 1973,398515 1974,393919 1975,385378 1976,499093 1977,458755 1978,589810 1979,394244 1980,524295 1981,595014 1982,533624 1983,550052 1984,541811 1985,568149 1986,600027 1987,599889 1988,641346 1989,1090172 1990,1535872 1991,1826595 1992,973445 1993,903916 1994,803993 1995,720177 1996,915560 1997,797847 1998,653206 1999,644787 2000,841002 2001,1058902 2002,1059356 2003,703542 2004,957883 2005,1122257 2006,1266129 2007,1052415 2008,1107126 2009,1130818 2010,1042625 2011,1062040 2012,1031631 2013,990553 2014,1016518 2015,1051031 2016,1183505
Consider the Monthly Australian Beer Consumption data from BB. Decompose the data and interpret. What are the seasonal effects for January and July? What is the de-seasonalized forecast if the production on September 1995 is 140?
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Related Book For 
Business Analytics Methods Models and Decisions
ISBN: 978-0321997821
2nd edition
Authors: James R. Evans
Posted Date:
