Esercitazioni matlab: problemi e stime di econometria

Heckmann method. Only in Age coefficient beta_A you can appreciate a significant

change (about 3-4 percentual points).\n') 31

32

Dati di Nerlove per l’esercitazione 3

101 0.082 2 2.09 17.9 183

102 0.661 3 2.05 35.1 174

103 0.99 4 2.05 35.1 171

104 0.315 4 1.83 32.2 166

105 0.197 5 2.12 28.6 233

106 0.098 9 2.12 28.6 195

107 0.949 11 1.98 35.5 206

108 0.675 13 2.05 35.1 150

109 0.525 13 2.19 29.1 155

110 0.501 22 1.72 15 188

111 1.194 25 2.09 17.9 170

112 0.67 25 1.68 39.7 167

113 0.349 35 1.81 22.6 213

114 0.423 39 2.3 23.6 164

115 0.501 43 1.75 42.8 170

116 0.55 63 1.76 10.3 161

117 0.795 68 1.98 35.5 210

118 0.664 81 2.29 28.5 158

119 0.705 84 2.19 29.1 156

120 0.903 73 1.75 42.8 176

121 1.504 99 2.2 36.2 170

122 1.615 101 1.66 33.4 192

123 1.127 119 1.92 22.5 164

124 0.718 120 1.77 21.3 175

125 2.414 122 2.09 17.9 180

126 1.13 130 1.82 38.9 176

127 0.992 138 1.8 20.2 202

128 1.554 149 1.92 22.5 227

129 1.225 196 1.92 29.1 186

201 1.565 197 2.19 29.1 183

202 1.936 209 1.92 22.5 169

203 3.154 214 1.52 27.5 168

204 2.599 220 1.92 22.5 164

205 3.298 234 2.2 36.2 164

206 2.441 235 2.11 24.4 170

207 2.031 253 1.92 22.5 158

208 4.666 279 2.05 35.1 177

209 1.834 290 1.66 33.4 195

210 2.072 290 1.8 20.2 176

211 2.039 295 1.77 21.3 188

212 3.398 299 1.7 26.9 187

213 3.083 324 2.05 35.1 152

214 2.344 333 2.19 29.1 157

215 2.382 338 1.85 24.6 163

216 2.657 353 2.19 29.1 143

217 1.705 353 2.13 10.7 167

218 3.23 416 1.54 26.2 217

219 5.049 420 1.52 27.5 144

220 3.814 456 2.09 30 178

221 4.58 484 1.75 42.8 176

222 4.358 516 2.3 23.6 167

223 4.714 550 2.05 35.1 158

224 4.357 563 2.32 31.9 162

225 3.919 566 2.31 33.5 198

226 3.442 592 1.92 22.5 164

227 4.898 671 2.05 35.1 164

228 3.584 696 1.76 10.3 161

229 5.535 719 1.7 26.9 174

301 4.406 742 2.04 20.7 157

302 4.289 795 2.24 26.5 185

303 6.731 800 1.7 26.9 157

304 6.895 808 1.68 39.7 203

305 5.112 811 2.29 28.5 178

306 5.141 855 2 34.3 183

307 5.72 860 2.31 33.5 168

308 4.691 909 1.45 17.6 196

309 6.832 913 1.7 26.9 166

310 4.813 924 1.76 10.3 172

311 6.754 984 1.7 26.9 158

312 5.127 991 2.09 30 174

313 6.388 1000 1.55 28.2 225

314 4.509 1098 2.11 24.4 168

315 7.185 1109 2.05 35.1 177

316 6.8 1118 2.3 23.6 161

317 7.743 1122 2.19 29.1 162

318 7.968 1137 2.04 20.7 158

319 8.858 1156 2.31 33.5 176

320 8.588 1166 1.7 26.9 183

321 6.449 1170 2.05 35.1 166

322 8.488 1215 2.19 29.1 164

323 8.877 1279 2 34.3 207

324 10.274 1291 2.32 31.9 175

325 6.024 1290 1.55 28.2 225

326 8.258 1331 2.13 30 178

327 13.376 1373 2.2 36.2 157

328 10.69 1420 2.2 36.2 138

329 8.308 1474 1.85 24.6 163

401 6.082 1497 1.76 10.3 168

402 9.284 1545 1.8 20.2 158

403 10.879 1649 2.32 31.9 177

404 8.477 1668 1.8 20.2 170

405 6.877 1782 2.13 10.7 183

406 15.106 1831 1.98 35.5 162

407 8.031 1833 1.76 10.3 177

408 8.082 1838 1.45 17.6 196

409 10.866 1787 2.24 26.5 164

410 8.596 1918 1.69 12.9 158

411 8.673 1930 1.81 22.6 157

412 15.437 2028 2.11 24.4 163

413 8.211 2057 1.76 10.3 161

414 11.982 2084 1.77 21.3 156

415 16.674 2226 2 34.3 217

416 12.62 2304 2.3 23.6 161

417 12.905 2341 2.04 20.7 183

418 11.615 2353 1.69 12.9 167

419 9.321 2367 1.76 10.3 161

420 12.962 2451 2.04 20.7 163

421 16.932 2457 2.2 36.2 170

422 9.648 2507 1.76 10.3 174

423 18.35 2530 2.31 33.5 197

424 17.333 2576 1.92 22.5 162

425 12.015 2607 1.76 10.3 155

426 11.32 2870 1.76 10.3 167

427 22.337 2993 2.31 33.5 176

428 19.035 3202 2.3 23.6 170

429 12.205 3286 1.61 17.8 183

501 17.078 3312 1.68 28.8 190

502 25.528 3498 2.09 30 170

503 24.021 3538 2.09 30 176

504 32.197 3794 2.05 35.1 159

505 26.652 3841 2.29 28.5 157

506 20.164 4014 2.11 24.4 161

507 14.132 4217 1.53 18.1 172

508 21.41 4305 2.11 24.4 203

509 23.244 4494 2.04 20.7 167

510 29.845 4764 2.19 29.1 195

511 32.318 5277 1.92 29.1 161

512 21.988 5283 2.04 20.7 159

513 35.229 5668 2.11 24.4 177

514 17.467 5681 1.76 10.3 157

515 22.828 5819 1.79 18.5 196

516 33.154 6000 2.11 24.4 183

517 32.228 6119 1.54 26.2 189

518 34.168 6136 1.92 22.5 160

519 40.594 7193 2.12 28.6 162

520 33.354 7886 1.61 17.8 178

521 64.542 8419 2.32 31.9 199

522 41.238 8642 2.24 26.5 182

523 47.993 8787 2.31 33.5 190

524 69.878 9484 2.11 24.4 165

525 44.894 9956 1.68 28.8 203

526 67.12 11477 2.24 26.5 151

527 73.05 11796 2.12 28.6 148

528 139.422 14359 2.31 33.5 212

529 119.939 16719 2.3 23.6 162 Dati per l’esercitazione 4 ‐ Logit

0 2.350986 1

1 4.181166 1

1 2.241547 0

0 1.890387 0

0 2.154449 0

0 2.427335 1

1 2.441319 1

1 3.17838 1

1 2.803139 0

1 3.586443 1

0 2.148113 1

1 3.800321 1

0 1.490595 0

1 3.875874 1

0 2.75721 1

1 3.166813 0

0 1.034581 1

0 1.729929 1

1 4.175171 1

1 5.02916 1

1 2.724843 0

1 3.603658 0

1 4.781252 1

1 4.773658 0

0 1.134877 1

0 1.948893 1

1 2.582618 1

1 4.402162 1

0 1.632253 1

0 2.707465 1

1 4.270848 1

1 3.066009 0

1 3.45129 1

1 2.67779 0

1 3.788409 0

1 3.928736 0

1 2.50921 0

1 4.797201 1

1 3.590697 1

1 2.364214 0

1 3.603347 0

0 2.464752 1

1 2.84492 1

1 3.612122 1

0 1.955657 1

0 2.654368 0

0 1.828595 1

0 2.314413 1

1 3.926216 0

0 1.518325 1

1 2.441942 0

1 2.971547 1

0 1.523708 0

1 3.2589 0

0 0.981309 0

1 3.19974 1

1 3.425864 1

1 1.729957 1

0 2.514781 1

1 3.594308 1

1 2.723535 0

0 1.142417 1

1 3.040731 0

1 3.28297 1

1 3.063561 1

0 3.43343 1

1 3.42286 0

1 4.299528 1

0 1.950207 1

0 1.213588 1

1 3.816043 1

0 2.671791 1

1 1.785434 1

1 4.111833 1

0 2.492503 1

1 3.89873 0

1 3.377216 0

1 4.452392 1

0 3.446945 0

0 3.645825 1

1 2.376323 0

1 2.404764 0

1 4.611324 1

1 2.651002 0

1 3.164167 1

0 1.363423 1

1 3.581366 1

1 2.871094 0

1 3.432859 1

1 2.754891 1

0 1.91457 1

1 4.680802 0

1 3.176412 0

0 0.92856 0

1 3.211089 1

0 2.417152 0

1 3.018169 1

1 4.494778 1

0 2.575203 1

1 4.686243 0

1 3.36549 1

0 1.902939 1

1 4.930213 1

1 3.622936 1

1 3.657284 0

0 1.536617 0

1 3.853935 1

1 3.580489 1

0 2.081399 1

1 3.794865 1

1 3.517535 0

1 3.494614 0

0 3.66393 1

0 2.289828 0

1 1.693162 0

0 2.258411 1

0 1.532341 1

1 2.608325 0

1 3.841659 0

1 3.082784 0

1 3.314671 0

1 3.789805 1

0 2.198776 1

0 2.674346 1

1 3.284676 1

1 4.309618 0

0 3.160373 1

0 0.881812 1

1 3.707081 0

0 1.956586 1

1 4.068207 1

1 2.682766 1

1 4.479677 1

1 3.699088 1

0 3.159099 1

0 2.054519 0

0 2.206993 1

0 0.950761 1

0 0.641165 0

0 1.340731 1

0 2.041876 1

1 3.22573 0

1 3.217665 1

0 2.176761 1

0 1.987232 1

1 4.215258 1

1 3.156275 1

1 2.599743 1

1 2.558221 0

1 3.448102 0

0 1.335409 1

1 3.21489 1

1 3.549563 1

1 4.392338 1

1 2.380772 0

1 2.987399 0

1 3.773612 0

1 4.629212 1

1 1.590025 0

0 1.252717 1

1 2.527754 0

1 2.939912 0

1 3.438879 1

1 3.201222 1

1 2.416702 0

1 3.764797 0

1 3.14077 0

1 2.627063 1

1 3.105467 0

1 4.270624 1

1 3.49913 1

1 2.602975 1

0 1.210032 0

0 2.733106 1

1 3.178431 0

1 2.565808 1

1 3.464513 1

1 1.878555 1

1 2.640925 0

1 3.532267 0

0 1.356492 1

1 3.466899 1

1 3.112107 1

1 4.496544 0

1 2.413498 1

0 1.281068 1

1 3.74104 1

1 4.087696 1

1 3.75667 1

1 4.629694 1

0 1.625007 0

0 1.947989 1

1 3.477518 0

1 4.222177 1

1 5.370732 1

1 3.114586 0

1 3.279069 1

1 3.75208 0

0 2.739743 1

1 2.974007 0

1 3.177982 1

0 2.733217 1

1 3.33764 1

0 1.465199 1

1 3.782652 0

1 3.804336 0

0 2.091764 0

1 2.586326 0

1 2.478464 0

0 2.277268 0

0 1.714608 0

0 3.932029 1

1 2.546186 0

1 2.731765 0

1 3.50659 1

1 4.706775 1

1 2.992769 1

1 2.59908 1

1 2.538186 1

1 2.177176 0

1 2.461158 1

1 3.320479 1

1 3.271427 0

1 4.094497 0

1 3.577325 1

0 1.44957 0

1 2.144486 1

0 2.835428 1

1 1.387325 0

0 1.954921 1

0 0.929238 0

1 3.787041 0

1 2.66846 1

0 2.330377 1

0 1.460091 0

0 1.686549 1

0 2.390551 1

1 2.923882 0

1 1.91642 0

1 3.218041 0

1 3.702286 0

1 3.408311 1

0 0.486342 1

1 4.621113 1

0 1.771081 1

0 1.719491 1

1 3.356693 1

1 3.41377 1

1 2.536843 0

1 4.116148 1

1 4.948605 1

1 2.880235 0

1 4.876617 1

1 4.001397 1

0 2.359445 1

1 3.403308 1

1 3.321737 1

1 5.544264 0

0 1.765042 0

1 4.208727 0

1 3.61721 1

1 3.714619 1

1 2.859087 1

0 1.765931 1

0 2.226814 1

1 2.483113 0

1 3.600655 1

1 3.126533 0

1 3.240927 0

0 2.639752 1

1 4.167247 1

1 3.677368 1

1 4.598537 1

0 1.766393 0

1 3.837048 1

0 1.853409 1

1 3.590293 0

0 1.964637 1

1 4.231715 1

1 2.611206 0

1 3.741466 0

1 3.221114 0

1 3.16166 1

1 5.086923 0

1 4.619915 1

0 1.237616 0

1 2.816736 0

0 2.171943 0

0 2.810732 1

1 2.354491 0

1 3.048203 0

1 4.925241 0

0 2.428156 1

0 2.468547 1

0 2.229764 0

1 4.085801 1

0 0.47938 1

1 4.426606 1

1 3.568148 0

1 3.615062 1

0 1.255689 1

1 3.463255 1

0 2.175504 1

0 3.291503 1

0 0.489674 0

1 3.05233 1

1 3.681522 1

0 2.335736 1

1 3.070126 0

0 2.732752 1

1 3.160841 1

0 1.259086 0

1 2.502966 0

1 3.047408 0

0 2.559989 0

0 2.810128 1

1 4.459431 0

1 2.882908 0

1 2.56012 0

1 1.823693 1

0 0.87915 1

1 3.69154 1

0 2.969037 1

1 1.765626 0

0 2.023138 1

1 4.663239 0

1 3.303636 1

1 3.171743 0

1 2.34749 1

1 3.400763 1

1 3.698152 1

1 3.527116 1

1 3.209768 1

1 4.297868 1

1 5.021119 0

1 3.843094 0

0 0.786823 1

1 3.406009 0

1 2.429001 1

1 2.001983 0

0 2.46085 1

1 4.089822 1

1 4.186938 1

0 2.579474 1

1 3.850644 1

1 3.786438 1

1 2.129683 1

1 1.898738 0

1 3.442948 0

1 5.794934 0

0 2.432684 1

0 2.916094 1 Dati per l’esercitazione 5 ‐ Poisson

0 0 1 6.907755 0 0 13.73189 1 0 0 9.528776 1.386294 12 42.87748 0 0 0 1

2 0 1