Computational Corporate Finance Calculations

y x a b c 26.2241386 1 7.26737266 52.8147054 1.98276181 1 0.01525926 0.00023284 34.2005614 1 8.33033235 69.394437 2.57062484 1 1.39805292 1.95455196 1.98993353 1 0.08575701 0.00735427 44.8538976 1 9.56083865 91.4096357 3.18464712 1 1.88207648 3.54221187 16.0275651 1 5.60563982 31.4231978 36.3739272 1 8.59553819 73.8832768 6.96650419 1 3.46934416 12.0363489 6.59195558 1 3.3475753 11.2062604 25.2003237 1 7.12088382 50.7069863 47.5479969 1 9.85106967 97.0435737 2.91695666 1 1.69408246 2.86991538 10.8364237 1 4.51643422 20.398178 1.99047589 1 0.63661611 0.40528007 11.1212135 1 4.58296457 21.0035642 4.62875627 1 2.61421552 6.8341228 7.18882824 1 3.53495895 12.4959348 23.4438035 1 6.85628834 47.0086898 1.95254163 1 0.25971252 0.06745059 1.97133851 1 0.09979553 0.00995915 12.8782943 1 4.97116001 24.7124318 35.953215 1 8.54609821 73.0357946 9.44059754 1 4.16974395 17.3867646 1.97268607 1 0.51362651 0.2638122 2.00520138 1 0.05310221 0.00281984 18.596781 1 6.06769005 36.8168626 32.283961 1 8.08526872 65.3715702 12.8469173 1 4.96871853 24.6881638 2.33307082 1 1.15024262 1.32305809 1.9449117 1 0.19013031 0.03614954 a b c 1.99767339 -0.30074057 0.50014972 Run the Macro by hitting ALT+F8 then plot the coefficients against N You will see that the first couple of coefficients sets will not be well identified, which makes sense because we are, for example, As you take more and more data into the computation the coefficients converge to their true valuesPrediction squared resid sum of squared residuals takes N squared residuals N 26.2273399 1.0248E-05 1.0248E-05 1 1.99320077 0.00010897 0.00011922 2 34.200013 3.0076E-07 0.00011952 <-takes the first 3 residuals into the sum 3 2.55479078 0.00025072 0.00037024 4 1.97556101 0.00020657 0.00057681 5 44.8408453 0.00017036 0.00074717 6 3.20329293 0.00034767 0.00109484 <-takes the first 7 residuals into the sum 7 16.0281338 3.2333E-07 0.00109516 and so forth 8 36.3653468 7.3623E-05 0.00116878 9 6.97427741 6.0423E-05 0.00122921 10 6.59572973 1.4244E-05 0.00124345 11 25.2172199 0.00028548 0.00152893 12 47.5713736 0.00054647 0.0020754 13 2.92358145 4.3888E-05 0.00211929 14 10.8415415 2.6192E-05 0.00214548 15 2.00891781 0.0003401 0.00248559 16 11.1243169 9.6308E-06 0.00249522 17 4.62955735 6.4174E-07 0.00249586 18 7.18440615 1.9555E-05 0.00251542 19 23.4470925 1.0818E-05 0.00252623 20 1.9533027 5.7921E-07 0.00252681 21 1.97264189 1.6988E-06 0.00252851 22 12.8625598 0.00024757 0.00277609 23 35.9563474 9.812E-06 0.0027859 24 9.43964773 9.0215E-07 0.0027868 25 1.97515066 6.0742E-06 0.00279287 26 1.98311375 0.00048786 0.00328074 27 18.5868165 9.9292E-05 0.00338003 28 32.2616778 0.00049654 0.00387657 29 12.8511564 1.7971E-05 0.00389454 30 2.31347591 0.00038396 0.0042785 31 1.95857367 0.00018665 0.00446515 32 identified, which makes sense because we are, for example, fitting 2 datapoints with 3 parameters converge to their true values26.2241386 7.26737266 1.98276181 0.01525926 34.2005614 8.33033235 2.57062484 1.39805292 1.98993353 0.08575701 44.8538976 9.56083865 3.18464712 1.88207648 16.0275651 5.60563982 36.3739272 8.59553819 6.96650419 3.46934416 6.59195558 3.3475753 25.2003237 7.12088382 47.5479969 9.85106967 2.91695666 1.69408246 10.8364237 4.51643422 1.99047589 0.63661611 11.1212135 4.58296457 4.62875627 2.61421552 7.18882824 3.53495895 23.4438035 6.85628834 1.95254163 0.25971252 1.97133851 0.09979553 12.8782943 4.97116001 35.953215 8.54609821 9.44059754 4.16974395 1.97268607 0.51362651 2.00520138 0.05310221 18.596781 6.06769005 32.283961 8.08526872 12.8469173 4.96871853 2.33307082 1.15024262 1.9449117 0.19013031