Dear All,   I am simply trying to test whether the return of dependent variable increases/decreases after the sample end date (December 2001). For example, my dependent variable is L_S (long-short portfolio) of stock i at time t and the independent variable is post sample dummy for stock i at time t. Post sample dummy is equal to one if the date is after 2001, else zero. L_S(it) = alphai + beta1 post sampel dummy(it) + e(it)   Can you please help me how to run the regression test for changes in returns relative to the sample end date (December 2001)? I have provided sample data too. data have; infile cards expandtabs truncover; input stock date : yymmn6. L_S post_sample ; format Date yymmn6.; cards; 14 199901 14.0238 0 15 199901 11.9454 0 16 199901 9.0066 0 14 199902 1.5165 0 15 199902 -1.9619 0 16 199902 0.3936 0 14 199903 1.9127 0 15 199903 -0.2142 0 16 199903 -6.9887 0 14 199904 1.9835 0 15 199904 1.1102 0 16 199904 1.1719 0 14 199905 1.771 0 15 199905 3.2528 0 16 199905 -0.7609 0 14 199906 0.2883 0 15 199906 1.174 0 16 199906 -0.3383 0 14 199907 3.1988 0 15 199907 2.2924 0 16 199907 -2.6332 0 14 199908 0.3939 0 15 199908 -0.1062 0 16 199908 -2.1077 0 14 199909 2.2333 0 15 199909 2.3855 0 16 199909 -0.9922 0 14 199910 0.0906 0 15 199910 -1.7595 0 16 199910 1.8265 0 14 199911 12.0945 0 15 199911 13.1003 0 16 199911 11.7012 0 14 199912 13.1348 0 15 199912 13.0311 0 16 199912 5.6075 0 14 200001 16.5104 0 15 200001 13.4878 0 16 200001 1.6201 0 14 200002 24.751 0 15 200002 22.8749 0 16 200002 9.1362 0 14 200003 -7.1384 0 15 200003 -5.9835 0 16 200003 -7.9094 0 14 200004 -13.1302 0 15 200004 -12.6789 0 16 200004 -12.3907 0 14 200005 -7.179 0 15 200005 -8.9204 0 16 200005 -12.4663 0 14 200006 9.8039 0 15 200006 10.4251 0 16 200006 4.0955 0 14 200007 -6.7036 0 15 200007 -6.3405 0 16 200007 -2.261 0 14 200008 1.7284 0 15 200008 2.8166 0 16 200008 -0.1966 0 14 200009 -6.1185 0 15 200009 -6.2332 0 16 200009 -8.9549 0 14 200010 -9.5955 0 15 200010 -8.6117 0 16 200010 -7.8711 0 14 200011 -15.9693 0 15 200011 -14.1019 0 16 200011 -17.3939 0 14 200012 -11.7599 0 15 200012 -11.0496 0 16 200012 -16.0614 0 14 200101 28.428 1 15 200101 24.3161 1 16 200101 24.8696 1 14 200102 -10.0572 1 15 200102 -9.8009 1 16 200102 -14.7445 1 14 200103 -8.1778 1 15 200103 -6.6534 1 16 200103 -12.8975 1 14 200104 3.5659 1 15 200104 4.775 1 16 200104 7.0148 1 14 200105 3.0865 1 15 200105 3.0205 1 16 200105 2.6813 1 14 200106 -3.0407 1 15 200106 -1.0529 1 16 200106 6.5331 1 14 200107 -7.8688 1 15 200107 -6.0558 1 16 200107 -6.5671 1 14 200108 -5.2509 1 15 200108 -4.9021 1 16 200108 -4.7708 1 14 200109 -6.6907 1 15 200109 -7.7812 1 16 200109 -6.3164 1 14 200110 8.0976 1 15 200110 10.2051 1 16 200110 6.8036 1 14 200111 4.6386 1 15 200111 4.0607 1 16 200111 9.7786 1 14 200112 2.4793 1 15 200112 3.2357 1 16 200112 6.4167 1 14 200201 0.3871 1 15 200201 0.1048 1 16 200201 -3.0061 1 14 200202 -7.5435 1 15 200202 -7.1932 1 16 200202 -10.9996 1 14 200203 1.1006 1 15 200203 3.6903 1 16 200203 -2.3129 1 14 200204 -6.706 1 15 200204 -4.6423 1 16 200204 -7.2183 1 14 200205 -3.319 1 15 200205 -3.5954 1 16 200205 -4.587 1 14 200206 -5.9677 1 15 200206 -6.8817 1 16 200206 -4.1095 1 14 200207 -4.3103 1 15 200207 -5.4936 1 16 200207 -1.6184 1 14 200208 -0.783 1 15 200208 -1.9367 1 16 200208 -3.7291 1 14 200209 -6.8038 1 15 200209 -4.4041 1 16 200209 -4.2089 1 14 200210 4.7228 1 15 200210 4.2958 1 16 200210 6.1159 1 14 200211 14.8031 1 15 200211 11.6538 1 16 200211 9.2207 1 14 200212 -7.44 1 15 200212 -5.6201 1 16 200212 -6.7057 1 14 200301 2.7663 1 15 200301 1.9519 1 16 200301 0.2942 1 14 200302 -0.5867 1 15 200302 -0.6244 1 16 200302 -3.116 1 14 200303 0.8218 1 15 200303 0.8057 1 16 200303 0.8502 1 14 200304 5.9883 1 15 200304 4.1283 1 16 200304 8.4368 1 14 200305 13.8637 1 15 200305 11.1063 1 16 200305 4.3414 1 14 200306 4.0383 1 15 200306 4.8102 1 16 200306 -0.4886 1 14 200307 4.8519 1 15 200307 4.2237 1 16 200307 3.1779 1 14 200308 2.7659 1 15 200308 3.2586 1 16 200308 -0.6638 1 14 200309 5.2582 1 15 200309 3.2569 1 16 200309 -0.4464 1 14 200310 2.961 1 15 200310 3.1561 1 16 200310 1.5347 1 14 200311 0.5451 1 15 200311 1.0336 1 16 200311 -4.545 1 14 200312 0.6783 1 15 200312 -0.7552 1 16 200312 -5.7656 1 run; Thanks in advance for your help.   Best Regards,   Cheema ... View more

Hi everyone,   Can you please help me to form a composite index based on six variables as provided in the SAS data.   First Step: We standardise these variables to have zero mean and one variance.   Second Step: we have to estimate the first principal component of the six variables and their lags. This gives us a first-stage index with 12 loadings, one for each of the current and lagged variables.   Third Step: We compute the correlation between the first-stage index (obtained from the second step) and the current and lagged values of each of the variables.   Final step: we define composite index as the first principal component of the correlation matrix of six variables—each respective proxy’s lead or lag, whichever has higher correlation with the first-stage index—rescaling the coefficients so that the index has unit variance.    It should provide this answer Indext =− 0.241CEFDt +0.242TURNt−1 +0.253NIPOt +0.257RIPOt−1 +0.112St −0.283PD−ND t−1. data have; infile cards expandtabs truncover; input date pdnd nipo ripo cefd s turn ; cards; 1962 31.23 298 -0.41 3.88 0.16 0.1404 1963 29.24 83 4.27 9.38 0.11 0.1421 1964 31.16 97 4.92 14.30 0.22 0.1315 1965 12.36 146 11.67 13.90 0.14 0.1440 1966 2.86 84 10.68 15.70 0.14 0.1999 1967 -27.20 100 40.52 2.10 0.11 0.2224 1968 -33.79 368 54.11 -10.91 0.21 0.2274 1969 -11.81 781 13.44 -7.58 0.31 0.1972 1970 14.13 358 0.98 0.35 0.22 0.1805 1971 13.54 391 19.87 13.35 0.29 0.2105 1972 20.46 562 12.56 16.01 0.31 0.1988 1973 28.24 105 -0.06 16.48 0.33 0.1855 1974 19.53 9 0.67 16.66 0.16 0.1497 1975 16.71 14 -1.67 21.53 0.20 0.1958 1976 18.62 35 2.73 19.64 0.21 0.2018 1977 8.04 35 22.04 14.40 0.22 0.1871 1978 -0.23 50 24.42 23.53 0.22 0.2332 1979 -13.98 81 24.82 19.93 0.22 0.2648 1980 -22.89 238 49.60 13.53 0.28 0.3466 1981 -22.81 450 16.87 12.74 0.36 0.3262 1982 -25.62 222 19.30 2.44 0.36 0.4370 1983 -21.76 883 21.15 2.93 0.43 0.5060 1984 -15.95 552 11.51 1.15 0.17 0.4984 1985 -13.08 507 12.30 3.49 0.18 0.5415 1986 -11.08 953 10.40 0.92 0.16 0.6328 1987 -7.66 630 10.61 12.7 0.15 0.7318 1988 -4.16 227 9.81 13.7 0.11 0.5643 1989 -7.56 204 12.53 8.38 0.10 0.5485 1990 -1.49 172 14.61 7.49 0.08 0.4633 1991 -13.49 367 14.02 4.55 0.15 0.4639 1992 -12.19 509 12.34 1.2 0.15 0.4637 1993 -15.20 627 15.16 5.26 0.14 0.5305 1994 -9.21 568 13.46 12.14 0.12 0.5421 1995 -8.45 566 20.48 11.59 0.12 0.5886 1996 -11.02 845 16.90 11.06 0.20 0.6135 1997 -4.90 602 13.74 5.27 0.14 0.6871 1998 -5.39 344 19.90 12.14 0.12 0.7151 1999 -27.98 505 69.53 11.21 0.14 0.7631 2000 -15.83 397 56.43 6.17 0.14 0.9110 2001 -3.11 84 13.36 3.58 0.09 0.8892 2002 12.01 73 7.75 6.09 0.08 0.9627 2003 -0.05 76 11.58 4 0.07 0.9338 2004 -11.08 213 12.02 -0.1 0.08 0.9416 2005 -11.87 194 9.95 3.53 0.05 1.0551 run; Thanks a lot for your help.    Best,   Cheema  ... View more