The detailed assignment information is here. The idea is like Monte Carlo simulation on a simplified money supply model given a set of initial parameters. The original Python source code for the simulation part is at http://code.google.com/p/moneysim/source/browse/trunk/src Library used for numerical and visualization: numpy http://numpy.scipy.org/ matplotlib http://matplotlib.sourceforge.net/ An output chart generated by on run of simulation: and out put data file: step M B Fed_R M/B rr 1 659.05 100.0 32.26 6.59 5.45% 2 653.36 99.73 32.26 6.55 5.05% 3 686.54 100.46 31.19 6.83 5.18% 4 714.42 102.85 33.58 6.95 5.67% 5 722.25 105.67 38.48 6.83 5.39% 6 736.82 106.2 37.37 6.94 5.44% 7 750.31 107.25 38.37 7.0 5.82% 8 746.8 110.76 41.89 6.74 5.36% 9 762.28 108.94 38.55 7.0 5.4% 10 795.63 111.08 39.41 7.16 5.52% 11 807.93 115.37 42.14 7.0 5.34% 12 862.81 118.3 41.45 7.29 5.78% 13 876.82 126.44 47.79 6.93 5.32% 14 920.58 123.31 45.01 7.47 5.34% 15 963.98 125.1 47.37 7.71 5.87% 16 956.53 132.55 54.82 7.22 5.42% 17 960.76 128.33 50.59 7.49 5.6% 18 977.3 129.4 52.27 7.55 5.76% 19 974.74 131.96 54.83 7.39 5.72% 20 987.51 133.69 54.53 7.39 5.79% 21 1045.5 134.39 55.75 7.78 6.14% 22 1085.3 143.12 62.75 7.58 5.54% 23 1139.71 139.4 59.2 8.18 5.86% 24 1188.9 147.76 65.54 8.05 5.62% 25 1240.06 155.08 65.92 8.0 5.26% 26 1297.95 155.32 63.98 8.36 5.78% 27 1311.22 164.38 73.4 7.98 5.25% 28 1316.75 158.85 67.87 8.29 5.58% 29 1370.2 160.47 72.22 8.54 6.04% 30 1424.05 172.38 81.8 8.26 5.6% 31 1465.98 167.09 79.22 8.77 5.86% 32 1538.85 170.64 85.4 9.02 6.28% 33 1613.81 179.7 96.62 8.98 5.7% 34 1716.43 175.82 92.75 9.76 6.01% 35 1793.31 190.05 103.79 9.44 5.8% 36 1866.63 195.67 105.07 9.54 5.81% 37 1862.32 199.98 109.38 9.31 5.67% 38 1881.89 194.6 106.72 9.67 6.01% 39 1977.99 206.44 114.15 9.58 5.88% 40 1956.15 209.02 117.5 9.36 5.8% 41 2004.44 203.66 114.88 9.84 6.02% 42 2053.52 210.8 122.29 9.74 5.7% 43 2126.53 211.06 118.96 10.08 6.08% 44 2279.94 224.97 130.94 10.13 6.18% 45 2484.53 236.58 143.13 10.5 6.65% 46 2528.89 265.21 168.51 9.54 6.35% 47 2593.27 261.95 165.25 9.9 6.51% 48 2585.3 269.93 173.22 9.58 6.36% 49 2689.67 269.5 169.23 9.98 6.83% 50 2670.52 288.65 188.38 9.25 6.7% 51 2841.37 286.07 184.8 9.93 7.18% 52 2879.0 310.54 210.03 9.27 6.65% 53 2934.89 298.86 198.88 9.82 7.23% 54 3036.86 324.03 219.47 9.37 6.96% 55 3087.8 326.13 219.44 9.47 7.15% 56 3176.79 337.23 228.74 9.42 6.46% 57 3308.43 325.34 212.9 10.17 6.84% 58 3418.14 348.53 233.29 9.81 6.21% 59 3576.67 334.54 219.76 10.69 6.33% 60 3563.41 347.79 233.02 10.25 5.99% 61 3665.67 335.52 220.51 10.93 6.09% 62 3811.38 349.82 229.62 10.9 5.87% 63 3810.7 350.5 230.3 10.87 5.64% 64 3887.11 345.35 221.02 11.26 5.81% 65 4089.14 356.57 231.53 11.47 6.38% 66 4359.35 397.33 267.83 10.97 5.87% 67 4585.71 392.49 263.82 11.68 6.21% 68 4556.5 421.7 293.03 10.8 5.61% 69 4625.45 396.99 265.05 11.65 5.79% 70 4615.05 407.39 275.44 11.33 5.49% 71 4629.16 393.28 261.34 11.77 5.9% 72 4740.74 412.45 280.55 11.49 5.82% 73 4869.42 415.71 284.49 11.71 6.4% 74 4918.41 449.42 321.31 10.94 6.03% 75 5106.96 439.8 308.23 11.61 6.41% 76 5076.67 470.08 338.51 10.8 6.27% 77 5083.75 463.01 331.44 10.98 6.68% 78 5253.64 480.72 353.1 10.93 6.59% 79 5245.8 488.56 360.94 10.74 5.94% 80 5299.85 452.78 325.6 11.71 6.42% 81 5439.11 487.46 352.82 11.16 6.05% 82 5537.64 480.38 342.51 11.53 6.43% 83 5718.44 510.45 369.3 11.2 6.0% 84 5933.76 502.04 356.79 11.82 6.33% 85 5983.41 543.33 389.26 11.01 5.72% 86 6244.72 506.96 355.58 12.32 6.17% 87 6342.29 548.22 397.89 11.57 6.0% 88 6607.75 543.36 395.47 12.16 6.51% 89 6557.0 594.1 446.21 11.04 6.15% 90 6837.73 576.48 421.55 11.86 6.44% 91 7042.77 609.73 457.7 11.55 6.11% 92 7349.73 607.69 449.12 12.09 6.43% 93 7424.15 653.14 490.93 11.37 6.22% 94 7529.34 651.31 482.36 11.56 6.62% 95 7592.23 699.53 519.18 10.85 6.44% 96 7553.59 698.11 510.6 10.82 6.12% 97 7582.39 669.31 481.8 11.33 6.18% 98 7942.38 668.61 486.73 11.88 6.19% 99 8047.56 690.78 510.18 11.65 5.72% 100 8078.97 659.37 478.77 12.25 6.28% |
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