ml-finance-python
python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
05_logistic_regression_macro_data.ipynb
(171685B)
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 1,
6 "metadata": {
7 "ExecuteTime": {
8 "end_time": "2018-09-11T00:00:46.475222Z",
9 "start_time": "2018-09-11T00:00:46.376415Z"
10 }
11 },
12 "outputs": [],
13 "source": [
14 "import pandas as pd\n",
15 "import statsmodels.api as sm\n",
16 "import matplotlib.pyplot as plt"
17 ]
18 },
19 {
20 "cell_type": "code",
21 "execution_count": 2,
22 "metadata": {
23 "ExecuteTime": {
24 "end_time": "2018-09-11T00:00:49.678008Z",
25 "start_time": "2018-09-11T00:00:49.657404Z"
26 }
27 },
28 "outputs": [],
29 "source": [
30 "%matplotlib inline\n",
31 "plt.style.use('fivethirtyeight')"
32 ]
33 },
34 {
35 "cell_type": "markdown",
36 "metadata": {},
37 "source": [
38 "## Data Set"
39 ]
40 },
41 {
42 "cell_type": "markdown",
43 "metadata": {},
44 "source": [
45 "| Variable | Description | Transformation |\n",
46 "|------------|----------------------------------------------|--------------------|\n",
47 "| realgdp | Real gross domestic product | Annual Growth Rate |\n",
48 "| realcons | Real personal consumption expenditures | Annual Growth Rate |\n",
49 "| realinv | Real gross private domestic investment | Annual Growth Rate |\n",
50 "| realgovt | Real federal expenditures & gross investment | Annual Growth Rate |\n",
51 "| realdpi | Real private disposable income | Annual Growth Rate |\n",
52 "| m1 | M1 nominal money stock | Annual Growth Rate |\n",
53 "| tbilrate | Monthly 3 treasury bill rate | Level |\n",
54 "| unemp | Seasonally adjusted unemployment rate (%) | Level |\n",
55 "| infl | Inflation rate | Level |\n",
56 "| realint | Real interest rate | Level |"
57 ]
58 },
59 {
60 "cell_type": "code",
61 "execution_count": 25,
62 "metadata": {
63 "ExecuteTime": {
64 "end_time": "2018-09-11T00:19:08.023207Z",
65 "start_time": "2018-09-11T00:19:07.997822Z"
66 }
67 },
68 "outputs": [
69 {
70 "name": "stdout",
71 "output_type": "stream",
72 "text": [
73 "<class 'pandas.core.frame.DataFrame'>\n",
74 "RangeIndex: 203 entries, 0 to 202\n",
75 "Data columns (total 14 columns):\n",
76 "year 203 non-null float64\n",
77 "quarter 203 non-null float64\n",
78 "realgdp 203 non-null float64\n",
79 "realcons 203 non-null float64\n",
80 "realinv 203 non-null float64\n",
81 "realgovt 203 non-null float64\n",
82 "realdpi 203 non-null float64\n",
83 "cpi 203 non-null float64\n",
84 "m1 203 non-null float64\n",
85 "tbilrate 203 non-null float64\n",
86 "unemp 203 non-null float64\n",
87 "pop 203 non-null float64\n",
88 "infl 203 non-null float64\n",
89 "realint 203 non-null float64\n",
90 "dtypes: float64(14)\n",
91 "memory usage: 22.3 KB\n"
92 ]
93 }
94 ],
95 "source": [
96 "data = pd.DataFrame(sm.datasets.macrodata.load().data)\n",
97 "data.info()"
98 ]
99 },
100 {
101 "cell_type": "code",
102 "execution_count": 26,
103 "metadata": {
104 "ExecuteTime": {
105 "end_time": "2018-09-11T00:19:08.236682Z",
106 "start_time": "2018-09-11T00:19:08.210938Z"
107 }
108 },
109 "outputs": [
110 {
111 "data": {
112 "text/html": [
113 "<div>\n",
114 "<style scoped>\n",
115 " .dataframe tbody tr th:only-of-type {\n",
116 " vertical-align: middle;\n",
117 " }\n",
118 "\n",
119 " .dataframe tbody tr th {\n",
120 " vertical-align: top;\n",
121 " }\n",
122 "\n",
123 " .dataframe thead th {\n",
124 " text-align: right;\n",
125 " }\n",
126 "</style>\n",
127 "<table border=\"1\" class=\"dataframe\">\n",
128 " <thead>\n",
129 " <tr style=\"text-align: right;\">\n",
130 " <th></th>\n",
131 " <th>year</th>\n",
132 " <th>quarter</th>\n",
133 " <th>realgdp</th>\n",
134 " <th>realcons</th>\n",
135 " <th>realinv</th>\n",
136 " <th>realgovt</th>\n",
137 " <th>realdpi</th>\n",
138 " <th>cpi</th>\n",
139 " <th>m1</th>\n",
140 " <th>tbilrate</th>\n",
141 " <th>unemp</th>\n",
142 " <th>pop</th>\n",
143 " <th>infl</th>\n",
144 " <th>realint</th>\n",
145 " </tr>\n",
146 " </thead>\n",
147 " <tbody>\n",
148 " <tr>\n",
149 " <th>0</th>\n",
150 " <td>1959.0</td>\n",
151 " <td>1.0</td>\n",
152 " <td>2710.349</td>\n",
153 " <td>1707.4</td>\n",
154 " <td>286.898</td>\n",
155 " <td>470.045</td>\n",
156 " <td>1886.9</td>\n",
157 " <td>28.98</td>\n",
158 " <td>139.7</td>\n",
159 " <td>2.82</td>\n",
160 " <td>5.8</td>\n",
161 " <td>177.146</td>\n",
162 " <td>0.00</td>\n",
163 " <td>0.00</td>\n",
164 " </tr>\n",
165 " <tr>\n",
166 " <th>1</th>\n",
167 " <td>1959.0</td>\n",
168 " <td>2.0</td>\n",
169 " <td>2778.801</td>\n",
170 " <td>1733.7</td>\n",
171 " <td>310.859</td>\n",
172 " <td>481.301</td>\n",
173 " <td>1919.7</td>\n",
174 " <td>29.15</td>\n",
175 " <td>141.7</td>\n",
176 " <td>3.08</td>\n",
177 " <td>5.1</td>\n",
178 " <td>177.830</td>\n",
179 " <td>2.34</td>\n",
180 " <td>0.74</td>\n",
181 " </tr>\n",
182 " <tr>\n",
183 " <th>2</th>\n",
184 " <td>1959.0</td>\n",
185 " <td>3.0</td>\n",
186 " <td>2775.488</td>\n",
187 " <td>1751.8</td>\n",
188 " <td>289.226</td>\n",
189 " <td>491.260</td>\n",
190 " <td>1916.4</td>\n",
191 " <td>29.35</td>\n",
192 " <td>140.5</td>\n",
193 " <td>3.82</td>\n",
194 " <td>5.3</td>\n",
195 " <td>178.657</td>\n",
196 " <td>2.74</td>\n",
197 " <td>1.09</td>\n",
198 " </tr>\n",
199 " <tr>\n",
200 " <th>3</th>\n",
201 " <td>1959.0</td>\n",
202 " <td>4.0</td>\n",
203 " <td>2785.204</td>\n",
204 " <td>1753.7</td>\n",
205 " <td>299.356</td>\n",
206 " <td>484.052</td>\n",
207 " <td>1931.3</td>\n",
208 " <td>29.37</td>\n",
209 " <td>140.0</td>\n",
210 " <td>4.33</td>\n",
211 " <td>5.6</td>\n",
212 " <td>179.386</td>\n",
213 " <td>0.27</td>\n",
214 " <td>4.06</td>\n",
215 " </tr>\n",
216 " <tr>\n",
217 " <th>4</th>\n",
218 " <td>1960.0</td>\n",
219 " <td>1.0</td>\n",
220 " <td>2847.699</td>\n",
221 " <td>1770.5</td>\n",
222 " <td>331.722</td>\n",
223 " <td>462.199</td>\n",
224 " <td>1955.5</td>\n",
225 " <td>29.54</td>\n",
226 " <td>139.6</td>\n",
227 " <td>3.50</td>\n",
228 " <td>5.2</td>\n",
229 " <td>180.007</td>\n",
230 " <td>2.31</td>\n",
231 " <td>1.19</td>\n",
232 " </tr>\n",
233 " </tbody>\n",
234 "</table>\n",
235 "</div>"
236 ],
237 "text/plain": [
238 " year quarter realgdp realcons realinv realgovt realdpi cpi \\\n",
239 "0 1959.0 1.0 2710.349 1707.4 286.898 470.045 1886.9 28.98 \n",
240 "1 1959.0 2.0 2778.801 1733.7 310.859 481.301 1919.7 29.15 \n",
241 "2 1959.0 3.0 2775.488 1751.8 289.226 491.260 1916.4 29.35 \n",
242 "3 1959.0 4.0 2785.204 1753.7 299.356 484.052 1931.3 29.37 \n",
243 "4 1960.0 1.0 2847.699 1770.5 331.722 462.199 1955.5 29.54 \n",
244 "\n",
245 " m1 tbilrate unemp pop infl realint \n",
246 "0 139.7 2.82 5.8 177.146 0.00 0.00 \n",
247 "1 141.7 3.08 5.1 177.830 2.34 0.74 \n",
248 "2 140.5 3.82 5.3 178.657 2.74 1.09 \n",
249 "3 140.0 4.33 5.6 179.386 0.27 4.06 \n",
250 "4 139.6 3.50 5.2 180.007 2.31 1.19 "
251 ]
252 },
253 "execution_count": 26,
254 "metadata": {},
255 "output_type": "execute_result"
256 }
257 ],
258 "source": [
259 "data.head()"
260 ]
261 },
262 {
263 "cell_type": "markdown",
264 "metadata": {},
265 "source": [
266 "## Data Prep"
267 ]
268 },
269 {
270 "cell_type": "markdown",
271 "metadata": {},
272 "source": [
273 "To obtain a binary target variable, we compute the 20-quarter rolling average of the annual growth rate of quarterly real GDP. We then assign 1 if current growth exceeds the moving average and 0 otherwise. Finally, we shift the indicator variables to align next quarter's outcome with the current quarter."
274 ]
275 },
276 {
277 "cell_type": "code",
278 "execution_count": 27,
279 "metadata": {
280 "ExecuteTime": {
281 "end_time": "2018-09-11T00:19:13.382273Z",
282 "start_time": "2018-09-11T00:19:13.378105Z"
283 }
284 },
285 "outputs": [],
286 "source": [
287 "data['growth_rate'] = data.realgdp.pct_change(4)\n",
288 "data['target'] = (data.growth_rate > data.growth_rate.rolling(20).mean()).astype(int).shift(-1)\n",
289 "data.quarter = data.quarter.astype(int)"
290 ]
291 },
292 {
293 "cell_type": "code",
294 "execution_count": 28,
295 "metadata": {
296 "ExecuteTime": {
297 "end_time": "2018-09-11T00:19:13.850353Z",
298 "start_time": "2018-09-11T00:19:13.842291Z"
299 }
300 },
301 "outputs": [
302 {
303 "data": {
304 "text/plain": [
305 "0.0 112\n",
306 "1.0 90\n",
307 "Name: target, dtype: int64"
308 ]
309 },
310 "execution_count": 28,
311 "metadata": {},
312 "output_type": "execute_result"
313 }
314 ],
315 "source": [
316 "data.target.value_counts()"
317 ]
318 },
319 {
320 "cell_type": "code",
321 "execution_count": 30,
322 "metadata": {
323 "ExecuteTime": {
324 "end_time": "2018-09-11T00:19:33.400843Z",
325 "start_time": "2018-09-11T00:19:33.364941Z"
326 }
327 },
328 "outputs": [
329 {
330 "data": {
331 "text/html": [
332 "<div>\n",
333 "<style scoped>\n",
334 " .dataframe tbody tr th:only-of-type {\n",
335 " vertical-align: middle;\n",
336 " }\n",
337 "\n",
338 " .dataframe tbody tr th {\n",
339 " vertical-align: top;\n",
340 " }\n",
341 "\n",
342 " .dataframe thead th {\n",
343 " text-align: right;\n",
344 " }\n",
345 "</style>\n",
346 "<table border=\"1\" class=\"dataframe\">\n",
347 " <thead>\n",
348 " <tr style=\"text-align: right;\">\n",
349 " <th></th>\n",
350 " <th>year</th>\n",
351 " <th>quarter</th>\n",
352 " <th>realgdp</th>\n",
353 " <th>realcons</th>\n",
354 " <th>realinv</th>\n",
355 " <th>realgovt</th>\n",
356 " <th>realdpi</th>\n",
357 " <th>cpi</th>\n",
358 " <th>m1</th>\n",
359 " <th>tbilrate</th>\n",
360 " <th>unemp</th>\n",
361 " <th>pop</th>\n",
362 " <th>infl</th>\n",
363 " <th>realint</th>\n",
364 " <th>growth_rate</th>\n",
365 " <th>target</th>\n",
366 " </tr>\n",
367 " </thead>\n",
368 " <tbody>\n",
369 " <tr>\n",
370 " <th>198</th>\n",
371 " <td>2008.0</td>\n",
372 " <td>3</td>\n",
373 " <td>13324.600</td>\n",
374 " <td>9267.7</td>\n",
375 " <td>1990.693</td>\n",
376 " <td>991.551</td>\n",
377 " <td>9838.3</td>\n",
378 " <td>216.889</td>\n",
379 " <td>1474.7</td>\n",
380 " <td>1.17</td>\n",
381 " <td>6.0</td>\n",
382 " <td>305.270</td>\n",
383 " <td>-3.16</td>\n",
384 " <td>4.33</td>\n",
385 " <td>0.000262</td>\n",
386 " <td>0.0</td>\n",
387 " </tr>\n",
388 " <tr>\n",
389 " <th>199</th>\n",
390 " <td>2008.0</td>\n",
391 " <td>4</td>\n",
392 " <td>13141.920</td>\n",
393 " <td>9195.3</td>\n",
394 " <td>1857.661</td>\n",
395 " <td>1007.273</td>\n",
396 " <td>9920.4</td>\n",
397 " <td>212.174</td>\n",
398 " <td>1576.5</td>\n",
399 " <td>0.12</td>\n",
400 " <td>6.9</td>\n",
401 " <td>305.952</td>\n",
402 " <td>-8.79</td>\n",
403 " <td>8.91</td>\n",
404 " <td>-0.018619</td>\n",
405 " <td>0.0</td>\n",
406 " </tr>\n",
407 " <tr>\n",
408 " <th>200</th>\n",
409 " <td>2009.0</td>\n",
410 " <td>1</td>\n",
411 " <td>12925.410</td>\n",
412 " <td>9209.2</td>\n",
413 " <td>1558.494</td>\n",
414 " <td>996.287</td>\n",
415 " <td>9926.4</td>\n",
416 " <td>212.671</td>\n",
417 " <td>1592.8</td>\n",
418 " <td>0.22</td>\n",
419 " <td>8.1</td>\n",
420 " <td>306.547</td>\n",
421 " <td>0.94</td>\n",
422 " <td>-0.71</td>\n",
423 " <td>-0.033026</td>\n",
424 " <td>0.0</td>\n",
425 " </tr>\n",
426 " <tr>\n",
427 " <th>201</th>\n",
428 " <td>2009.0</td>\n",
429 " <td>2</td>\n",
430 " <td>12901.504</td>\n",
431 " <td>9189.0</td>\n",
432 " <td>1456.678</td>\n",
433 " <td>1023.528</td>\n",
434 " <td>10077.5</td>\n",
435 " <td>214.469</td>\n",
436 " <td>1653.6</td>\n",
437 " <td>0.18</td>\n",
438 " <td>9.2</td>\n",
439 " <td>307.226</td>\n",
440 " <td>3.37</td>\n",
441 " <td>-3.19</td>\n",
442 " <td>-0.038297</td>\n",
443 " <td>0.0</td>\n",
444 " </tr>\n",
445 " <tr>\n",
446 " <th>202</th>\n",
447 " <td>2009.0</td>\n",
448 " <td>3</td>\n",
449 " <td>12990.341</td>\n",
450 " <td>9256.0</td>\n",
451 " <td>1486.398</td>\n",
452 " <td>1044.088</td>\n",
453 " <td>10040.6</td>\n",
454 " <td>216.385</td>\n",
455 " <td>1673.9</td>\n",
456 " <td>0.12</td>\n",
457 " <td>9.6</td>\n",
458 " <td>308.013</td>\n",
459 " <td>3.56</td>\n",
460 " <td>-3.44</td>\n",
461 " <td>-0.025086</td>\n",
462 " <td>NaN</td>\n",
463 " </tr>\n",
464 " </tbody>\n",
465 "</table>\n",
466 "</div>"
467 ],
468 "text/plain": [
469 " year quarter realgdp realcons realinv realgovt realdpi \\\n",
470 "198 2008.0 3 13324.600 9267.7 1990.693 991.551 9838.3 \n",
471 "199 2008.0 4 13141.920 9195.3 1857.661 1007.273 9920.4 \n",
472 "200 2009.0 1 12925.410 9209.2 1558.494 996.287 9926.4 \n",
473 "201 2009.0 2 12901.504 9189.0 1456.678 1023.528 10077.5 \n",
474 "202 2009.0 3 12990.341 9256.0 1486.398 1044.088 10040.6 \n",
475 "\n",
476 " cpi m1 tbilrate unemp pop infl realint growth_rate \\\n",
477 "198 216.889 1474.7 1.17 6.0 305.270 -3.16 4.33 0.000262 \n",
478 "199 212.174 1576.5 0.12 6.9 305.952 -8.79 8.91 -0.018619 \n",
479 "200 212.671 1592.8 0.22 8.1 306.547 0.94 -0.71 -0.033026 \n",
480 "201 214.469 1653.6 0.18 9.2 307.226 3.37 -3.19 -0.038297 \n",
481 "202 216.385 1673.9 0.12 9.6 308.013 3.56 -3.44 -0.025086 \n",
482 "\n",
483 " target \n",
484 "198 0.0 \n",
485 "199 0.0 \n",
486 "200 0.0 \n",
487 "201 0.0 \n",
488 "202 NaN "
489 ]
490 },
491 "execution_count": 30,
492 "metadata": {},
493 "output_type": "execute_result"
494 }
495 ],
496 "source": [
497 "data.tail()"
498 ]
499 },
500 {
501 "cell_type": "code",
502 "execution_count": 31,
503 "metadata": {
504 "ExecuteTime": {
505 "end_time": "2018-09-11T00:19:45.020789Z",
506 "start_time": "2018-09-11T00:19:45.015620Z"
507 }
508 },
509 "outputs": [],
510 "source": [
511 "pct_cols = ['realcons', 'realinv', 'realgovt', 'realdpi', 'm1']\n",
512 "drop_cols = ['year', 'realgdp', 'pop', 'cpi', 'growth_rate']\n",
513 "data.loc[:, pct_cols] = data.loc[:, pct_cols].pct_change(4)"
514 ]
515 },
516 {
517 "cell_type": "code",
518 "execution_count": 32,
519 "metadata": {
520 "ExecuteTime": {
521 "end_time": "2018-09-11T00:19:45.533751Z",
522 "start_time": "2018-09-11T00:19:45.514393Z"
523 }
524 },
525 "outputs": [],
526 "source": [
527 "data = pd.get_dummies(data.drop(drop_cols, axis=1), columns=['quarter'], drop_first=True).dropna()"
528 ]
529 },
530 {
531 "cell_type": "code",
532 "execution_count": 37,
533 "metadata": {
534 "ExecuteTime": {
535 "end_time": "2018-09-11T00:27:40.868588Z",
536 "start_time": "2018-09-11T00:27:40.832772Z"
537 }
538 },
539 "outputs": [
540 {
541 "data": {
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557 "<table border=\"1\" class=\"dataframe\">\n",
558 " <thead>\n",
559 " <tr style=\"text-align: right;\">\n",
560 " <th></th>\n",
561 " <th>realcons</th>\n",
562 " <th>realinv</th>\n",
563 " <th>realgovt</th>\n",
564 " <th>realdpi</th>\n",
565 " <th>m1</th>\n",
566 " <th>tbilrate</th>\n",
567 " <th>unemp</th>\n",
568 " <th>infl</th>\n",
569 " <th>realint</th>\n",
570 " <th>target</th>\n",
571 " <th>quarter_2</th>\n",
572 " <th>quarter_3</th>\n",
573 " <th>quarter_4</th>\n",
574 " </tr>\n",
575 " </thead>\n",
576 " <tbody>\n",
577 " <tr>\n",
578 " <th>4</th>\n",
579 " <td>0.036957</td>\n",
580 " <td>0.156237</td>\n",
581 " <td>-0.016692</td>\n",
582 " <td>0.036356</td>\n",
583 " <td>-0.000716</td>\n",
584 " <td>3.50</td>\n",
585 " <td>5.2</td>\n",
586 " <td>2.31</td>\n",
587 " <td>1.19</td>\n",
588 " <td>0.0</td>\n",
589 " <td>0</td>\n",
590 " <td>0</td>\n",
591 " <td>0</td>\n",
592 " </tr>\n",
593 " <tr>\n",
594 " <th>5</th>\n",
595 " <td>0.034147</td>\n",
596 " <td>-0.040877</td>\n",
597 " <td>-0.043426</td>\n",
598 " <td>0.024170</td>\n",
599 " <td>-0.010586</td>\n",
600 " <td>2.68</td>\n",
601 " <td>5.2</td>\n",
602 " <td>0.14</td>\n",
603 " <td>2.55</td>\n",
604 " <td>0.0</td>\n",
605 " <td>1</td>\n",
606 " <td>0</td>\n",
607 " <td>0</td>\n",
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609 " <tr>\n",
610 " <th>6</th>\n",
611 " <td>0.019409</td>\n",
612 " <td>0.024718</td>\n",
613 " <td>-0.033758</td>\n",
614 " <td>0.026821</td>\n",
615 " <td>0.002847</td>\n",
616 " <td>2.36</td>\n",
617 " <td>5.6</td>\n",
618 " <td>2.70</td>\n",
619 " <td>-0.34</td>\n",
620 " <td>0.0</td>\n",
621 " <td>0</td>\n",
622 " <td>1</td>\n",
623 " <td>0</td>\n",
624 " </tr>\n",
625 " <tr>\n",
626 " <th>7</th>\n",
627 " <td>0.019673</td>\n",
628 " <td>-0.132257</td>\n",
629 " <td>-0.015738</td>\n",
630 " <td>0.018278</td>\n",
631 " <td>0.007857</td>\n",
632 " <td>2.29</td>\n",
633 " <td>6.3</td>\n",
634 " <td>1.21</td>\n",
635 " <td>1.08</td>\n",
636 " <td>0.0</td>\n",
637 " <td>0</td>\n",
638 " <td>0</td>\n",
639 " <td>1</td>\n",
640 " </tr>\n",
641 " <tr>\n",
642 " <th>8</th>\n",
643 " <td>0.009715</td>\n",
644 " <td>-0.196903</td>\n",
645 " <td>0.029544</td>\n",
646 " <td>0.014830</td>\n",
647 " <td>0.017908</td>\n",
648 " <td>2.37</td>\n",
649 " <td>6.8</td>\n",
650 " <td>-0.40</td>\n",
651 " <td>2.77</td>\n",
652 " <td>0.0</td>\n",
653 " <td>0</td>\n",
654 " <td>0</td>\n",
655 " <td>0</td>\n",
656 " </tr>\n",
657 " </tbody>\n",
658 "</table>\n",
659 "</div>"
660 ],
661 "text/plain": [
662 " realcons realinv realgovt realdpi m1 tbilrate unemp infl \\\n",
663 "4 0.036957 0.156237 -0.016692 0.036356 -0.000716 3.50 5.2 2.31 \n",
664 "5 0.034147 -0.040877 -0.043426 0.024170 -0.010586 2.68 5.2 0.14 \n",
665 "6 0.019409 0.024718 -0.033758 0.026821 0.002847 2.36 5.6 2.70 \n",
666 "7 0.019673 -0.132257 -0.015738 0.018278 0.007857 2.29 6.3 1.21 \n",
667 "8 0.009715 -0.196903 0.029544 0.014830 0.017908 2.37 6.8 -0.40 \n",
668 "\n",
669 " realint target quarter_2 quarter_3 quarter_4 \n",
670 "4 1.19 0.0 0 0 0 \n",
671 "5 2.55 0.0 1 0 0 \n",
672 "6 -0.34 0.0 0 1 0 \n",
673 "7 1.08 0.0 0 0 1 \n",
674 "8 2.77 0.0 0 0 0 "
675 ]
676 },
677 "execution_count": 37,
678 "metadata": {},
679 "output_type": "execute_result"
680 }
681 ],
682 "source": [
683 "data.head()"
684 ]
685 },
686 {
687 "cell_type": "code",
688 "execution_count": 38,
689 "metadata": {
690 "ExecuteTime": {
691 "end_time": "2018-09-11T00:27:41.721540Z",
692 "start_time": "2018-09-11T00:27:41.702945Z"
693 }
694 },
695 "outputs": [
696 {
697 "name": "stdout",
698 "output_type": "stream",
699 "text": [
700 "<class 'pandas.core.frame.DataFrame'>\n",
701 "Int64Index: 198 entries, 4 to 201\n",
702 "Data columns (total 13 columns):\n",
703 "realcons 198 non-null float64\n",
704 "realinv 198 non-null float64\n",
705 "realgovt 198 non-null float64\n",
706 "realdpi 198 non-null float64\n",
707 "m1 198 non-null float64\n",
708 "tbilrate 198 non-null float64\n",
709 "unemp 198 non-null float64\n",
710 "infl 198 non-null float64\n",
711 "realint 198 non-null float64\n",
712 "target 198 non-null float64\n",
713 "quarter_2 198 non-null uint8\n",
714 "quarter_3 198 non-null uint8\n",
715 "quarter_4 198 non-null uint8\n",
716 "dtypes: float64(10), uint8(3)\n",
717 "memory usage: 17.6 KB\n"
718 ]
719 }
720 ],
721 "source": [
722 "data.info()"
723 ]
724 },
725 {
726 "cell_type": "markdown",
727 "metadata": {},
728 "source": [
729 "We use an intercept and convert the quarter values to dummy variables and train the logistic regression model as follows:"
730 ]
731 },
732 {
733 "cell_type": "markdown",
734 "metadata": {},
735 "source": [
736 "This produces the following summary for our model with 198 observations and 13 variables, including intercept:\n",
737 "The summary indicates that the model has been trained using maximum likelihood and provides the maximized value of the log-likelihood function at -67.9."
738 ]
739 },
740 {
741 "cell_type": "code",
742 "execution_count": 39,
743 "metadata": {
744 "ExecuteTime": {
745 "end_time": "2018-09-11T00:27:47.165219Z",
746 "start_time": "2018-09-11T00:27:47.097723Z"
747 }
748 },
749 "outputs": [
750 {
751 "name": "stdout",
752 "output_type": "stream",
753 "text": [
754 "Optimization terminated successfully.\n",
755 " Current function value: 0.342965\n",
756 " Iterations 8\n"
757 ]
758 },
759 {
760 "data": {
761 "text/html": [
762 "<table class=\"simpletable\">\n",
763 "<caption>Logit Regression Results</caption>\n",
764 "<tr>\n",
765 " <th>Dep. Variable:</th> <td>target</td> <th> No. Observations: </th> <td> 198</td> \n",
766 "</tr>\n",
767 "<tr>\n",
768 " <th>Model:</th> <td>Logit</td> <th> Df Residuals: </th> <td> 185</td> \n",
769 "</tr>\n",
770 "<tr>\n",
771 " <th>Method:</th> <td>MLE</td> <th> Df Model: </th> <td> 12</td> \n",
772 "</tr>\n",
773 "<tr>\n",
774 " <th>Date:</th> <td>Mon, 10 Sep 2018</td> <th> Pseudo R-squ.: </th> <td>0.5022</td> \n",
775 "</tr>\n",
776 "<tr>\n",
777 " <th>Time:</th> <td>20:27:47</td> <th> Log-Likelihood: </th> <td> -67.907</td> \n",
778 "</tr>\n",
779 "<tr>\n",
780 " <th>converged:</th> <td>True</td> <th> LL-Null: </th> <td> -136.42</td> \n",
781 "</tr>\n",
782 "<tr>\n",
783 " <th> </th> <td> </td> <th> LLR p-value: </th> <td>2.375e-23</td>\n",
784 "</tr>\n",
785 "</table>\n",
786 "<table class=\"simpletable\">\n",
787 "<tr>\n",
788 " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
789 "</tr>\n",
790 "<tr>\n",
791 " <th>const</th> <td> -8.5881</td> <td> 1.908</td> <td> -4.502</td> <td> 0.000</td> <td> -12.327</td> <td> -4.849</td>\n",
792 "</tr>\n",
793 "<tr>\n",
794 " <th>realcons</th> <td> 130.1446</td> <td> 26.633</td> <td> 4.887</td> <td> 0.000</td> <td> 77.945</td> <td> 182.344</td>\n",
795 "</tr>\n",
796 "<tr>\n",
797 " <th>realinv</th> <td> 18.8414</td> <td> 4.053</td> <td> 4.648</td> <td> 0.000</td> <td> 10.897</td> <td> 26.786</td>\n",
798 "</tr>\n",
799 "<tr>\n",
800 " <th>realgovt</th> <td> -19.0318</td> <td> 6.010</td> <td> -3.166</td> <td> 0.002</td> <td> -30.812</td> <td> -7.252</td>\n",
801 "</tr>\n",
802 "<tr>\n",
803 " <th>realdpi</th> <td> -52.2473</td> <td> 19.912</td> <td> -2.624</td> <td> 0.009</td> <td> -91.275</td> <td> -13.220</td>\n",
804 "</tr>\n",
805 "<tr>\n",
806 " <th>m1</th> <td> -1.3462</td> <td> 6.177</td> <td> -0.218</td> <td> 0.827</td> <td> -13.453</td> <td> 10.761</td>\n",
807 "</tr>\n",
808 "<tr>\n",
809 " <th>tbilrate</th> <td> 60.8607</td> <td> 44.350</td> <td> 1.372</td> <td> 0.170</td> <td> -26.063</td> <td> 147.784</td>\n",
810 "</tr>\n",
811 "<tr>\n",
812 " <th>unemp</th> <td> 0.9487</td> <td> 0.249</td> <td> 3.818</td> <td> 0.000</td> <td> 0.462</td> <td> 1.436</td>\n",
813 "</tr>\n",
814 "<tr>\n",
815 " <th>infl</th> <td> -60.9647</td> <td> 44.362</td> <td> -1.374</td> <td> 0.169</td> <td> -147.913</td> <td> 25.984</td>\n",
816 "</tr>\n",
817 "<tr>\n",
818 " <th>realint</th> <td> -61.0453</td> <td> 44.359</td> <td> -1.376</td> <td> 0.169</td> <td> -147.987</td> <td> 25.896</td>\n",
819 "</tr>\n",
820 "<tr>\n",
821 " <th>quarter_2</th> <td> 0.1128</td> <td> 0.618</td> <td> 0.182</td> <td> 0.855</td> <td> -1.099</td> <td> 1.325</td>\n",
822 "</tr>\n",
823 "<tr>\n",
824 " <th>quarter_3</th> <td> -0.1991</td> <td> 0.609</td> <td> -0.327</td> <td> 0.744</td> <td> -1.393</td> <td> 0.995</td>\n",
825 "</tr>\n",
826 "<tr>\n",
827 " <th>quarter_4</th> <td> 0.0007</td> <td> 0.608</td> <td> 0.001</td> <td> 0.999</td> <td> -1.191</td> <td> 1.192</td>\n",
828 "</tr>\n",
829 "</table>"
830 ],
831 "text/plain": [
832 "<class 'statsmodels.iolib.summary.Summary'>\n",
833 "\"\"\"\n",
834 " Logit Regression Results \n",
835 "==============================================================================\n",
836 "Dep. Variable: target No. Observations: 198\n",
837 "Model: Logit Df Residuals: 185\n",
838 "Method: MLE Df Model: 12\n",
839 "Date: Mon, 10 Sep 2018 Pseudo R-squ.: 0.5022\n",
840 "Time: 20:27:47 Log-Likelihood: -67.907\n",
841 "converged: True LL-Null: -136.42\n",
842 " LLR p-value: 2.375e-23\n",
843 "==============================================================================\n",
844 " coef std err z P>|z| [0.025 0.975]\n",
845 "------------------------------------------------------------------------------\n",
846 "const -8.5881 1.908 -4.502 0.000 -12.327 -4.849\n",
847 "realcons 130.1446 26.633 4.887 0.000 77.945 182.344\n",
848 "realinv 18.8414 4.053 4.648 0.000 10.897 26.786\n",
849 "realgovt -19.0318 6.010 -3.166 0.002 -30.812 -7.252\n",
850 "realdpi -52.2473 19.912 -2.624 0.009 -91.275 -13.220\n",
851 "m1 -1.3462 6.177 -0.218 0.827 -13.453 10.761\n",
852 "tbilrate 60.8607 44.350 1.372 0.170 -26.063 147.784\n",
853 "unemp 0.9487 0.249 3.818 0.000 0.462 1.436\n",
854 "infl -60.9647 44.362 -1.374 0.169 -147.913 25.984\n",
855 "realint -61.0453 44.359 -1.376 0.169 -147.987 25.896\n",
856 "quarter_2 0.1128 0.618 0.182 0.855 -1.099 1.325\n",
857 "quarter_3 -0.1991 0.609 -0.327 0.744 -1.393 0.995\n",
858 "quarter_4 0.0007 0.608 0.001 0.999 -1.191 1.192\n",
859 "==============================================================================\n",
860 "\"\"\""
861 ]
862 },
863 "execution_count": 39,
864 "metadata": {},
865 "output_type": "execute_result"
866 }
867 ],
868 "source": [
869 "data = pd.get_dummies(data.drop(drop_cols, axis=1), columns=['quarter'], drop_first=True).dropna()\n",
870 "model = sm.Logit(data.target, sm.add_constant(data.drop('target', axis=1)))\n",
871 "result = model.fit()\n",
872 "result.summary()"
873 ]
874 },
875 {
876 "cell_type": "markdown",
877 "metadata": {},
878 "source": [
879 "The LL-Null value of -136.42 is the result of the maximized log-likelihood function when only an intercept is included. It forms the basis for the pseudo-R2 statistic and the Log-Likelihood Ratio (LLR) test. \n",
880 "The pseudo-R2 statistic is a substitute for the familiar R2 available under least squares. It is computed based on the ratio of the maximized log-likelihood function for the null model m0 and the full model m1 as follows:\n",
881 "The values vary from 0 (when the model does not improve the likelihood) to 1 where the model fits perfectly and the log-likelihood is maximized at 0. Consequently, higher values indicate a better fit.\n"
882 ]
883 },
884 {
885 "cell_type": "code",
886 "execution_count": 40,
887 "metadata": {
888 "ExecuteTime": {
889 "end_time": "2018-09-11T00:27:55.301259Z",
890 "start_time": "2018-09-11T00:27:53.760224Z"
891 }
892 },
893 "outputs": [
894 {
895 "data": {
896 "image/png": 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\n",
897 "text/plain": [
898 "<Figure size 864x504 with 1 Axes>"
899 ]
900 },
901 "metadata": {},
902 "output_type": "display_data"
903 }
904 ],
905 "source": [
906 "plt.rc('figure', figsize=(12, 7))\n",
907 "plt.text(0.01, 0.05, str(result.summary()), {'fontsize': 14}, fontproperties = 'monospace')\n",
908 "plt.axis('off')\n",
909 "plt.tight_layout()\n",
910 "plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.1)\n",
911 "plt.savefig('logistic_example.png', bbox_inches='tight', dpi=300);"
912 ]
913 },
914 {
915 "cell_type": "code",
916 "execution_count": null,
917 "metadata": {},
918 "outputs": [],
919 "source": []
920 }
921 ],
922 "metadata": {
923 "kernelspec": {
924 "display_name": "Python 3",
925 "language": "python",
926 "name": "python3"
927 },
928 "language_info": {
929 "codemirror_mode": {
930 "name": "ipython",
931 "version": 3
932 },
933 "file_extension": ".py",
934 "mimetype": "text/x-python",
935 "name": "python",
936 "nbconvert_exporter": "python",
937 "pygments_lexer": "ipython3",
938 "version": "3.7.0"
939 },
940 "toc": {
941 "base_numbering": 1,
942 "nav_menu": {},
943 "number_sections": true,
944 "sideBar": true,
945 "skip_h1_title": false,
946 "title_cell": "Table of Contents",
947 "title_sidebar": "Contents",
948 "toc_cell": false,
949 "toc_position": {},
950 "toc_section_display": true,
951 "toc_window_display": false
952 }
953 },
954 "nbformat": 4,
955 "nbformat_minor": 2
956 }