For my project, I have financial data, which is missing on weekends.
A regression I am using for example is:
Which is the return on (t+2) for FTSE data on my x variable UKIS (which is a search index), EPU (Economic Policy Uncertainty index) and 5 lags of rmrf (UK market excess returns). I am encountering a problem of no observations as there is missing values in the financial data.
I am attempting to create a business calendar where I have formatted the data to
on my master file, so the lags and forward operators do not take the missing values. However this does not seem to work.
Data:
A regression I am using for example is:
Code:
reg F2.FTSE100 UKIS EPU rmrf L1.rmrf L2.rmrf L3.rmrf L4.rmrf l5.rmrf no observations r(2000);
I am attempting to create a business calendar where I have formatted the data to
Code:
format %tbsimple:NN/DD/CCYY date
Data:
Code:
input int date double FTSE100 float UKIS double(EPU rmrf) 16253 4424.72 . 75.14 -.00833516 16254 4407.4 -.2180105 30.11 -.00400119 16255 . .3280379 46.34 . 16256 . -.1164731 41.63 . 16257 4403.28 -.5526805 276.08 -.00167145 16258 4370.74 .25532183 167.46 -.00767937 16259 4358.37 .381967 257.54 -.00251354 16260 4381.11 -.3521871 27.22 .00334938 16261 4393.18 .1600098 0 .00217685 16262 . .016313126 103.71 . 16263 . -.11036982 250.09 . 16264 4359.97 -.024156913 204.16 -.00654372 16265 4357.74 .1126608 296.38 -.0001302 16266 4372.55 -.1054983 113.19 .00177553 16267 4340.71 .16306344 54.77 -.00682801 16268 4339.23 .067925915 60.37 .00029166 16269 . .06046037 81.68 . 16270 . -.19248436 118.62 . 16271 4321.1 .08850786 40.17 -.00396609 16272 4339.37 .014100888 113.13 .00276665 16273 4377.3 .2632147 324.83 .00808184 16274 4306.25 -.344536 187.1 -.01562203 16275 4326.34 .2210378 0 .00353599 16276 . .05127193 221.74 . 16277 . -.26678687 120.89 . 16278 4287.04 .2210185 109.95 -.00932341 16279 4324.86 -.0252831 91.8 .00760169 16280 4356.25 -.17771873 70.81 .00628228 16281 4418.7 .05900541 90.4 .01296797 16282 4413.08 .05729766 187.06 -.00102673 16283 . -.022344263 192.76 . 16284 . -.02866241 76.2 . 16285 4415.73 .17142993 221.81 .00017013 16286 4429.68 -.14051646 101.81 .00264746 16287 4408.07 -.25682688 296.88 -.0049213 16288 4413.37 .1975079 211.1 .00093802 16289 4337.88 -.0490886 174.4 -.01626284 16290 . .1140014 105.11 . 16291 . .05550234 100.87 . 16292 4314.4 -.1717474 106.9 -.00601042 16293 4350.88 .2153931 128.45 .00781587 16294 4312.16 -.19072935 46.66 -.00483969 16295 4328.11 .27155307 223.24 .00312112 16296 4301.53 -.034338232 81.92 -.00561349 16297 . -.25229073 126.45 . 16298 . .03926425 214.99 . 16299 4350.17 .001884974 51.98 .00977664 16300 4358.68 -.0753112 100.24 .00211764

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