Карта сайта Мастерской Своего Дела
- 07-2015 The Concept of an Artificial Regression
- 07-2015 The Gauss-Newton Regression
- 07-2015 Hypothesis Testing with Artificial Regressions
- 07-2015 The OPG Regression
- 07-2015 An Artificial Regression for GMM Estimation
- 07-2015 Artificial Regressions and HETEROsKEDASTiciTy
- 07-2015 Double-Length Regressions
- 07-2015 An Artificial Regression for Binary Response Models
- 07-2015 General Hypothesis. Testing
- 07-2015 Some Test Principles Suggested in the Statistics Literature
- 07-2015 Neyman-Pearson generalized lemma and its applications
- 07-2015 The Neyman-Pearson lemma and the Durbin-Watson test
- 07-2015 Detecting harmful collinearity
- 07-2015 What to Do?
- 07-2015 Methods for introducing exact nonsample information
- 07-2015 Methods for introducing inexact nonsample information
- 07-2015 Estimation methods designed specifically for collinear data
- 07-2015 Artificial orthogonalization
- 07-2015 Nonlinear Models
- 07-2015 Collinearity in nonlinear regression models
- 07-2015 Collinearity in maximum likelihood estimation
- 07-2015 Closing Remarks
- 07-2015 Nonnested Hypothesis. Testing: An Overview
- 07-2015 Examples of Nonnested Models
- 07-2015 Model Selection Versus Hypothesis Testing
- 07-2015 Alternative Approaches to Testing Nonnested Hypotheses
- 07-2015 Motivation for nonnested statistics
- 07-2015 The Cox procedure
- 07-2015 The comprehensive approach
- 07-2015 The encompassing approach
- 07-2015 Power and finite sample properties
- 07-2015 Measures of Closeness and Vuong's Approach
- 07-2015 Practical Problems
- 07-2015 Resampling the likelihood ratio statistic: bootstrap methods
- 07-2015 Spatial Econometrics
- 07-2015 Spatial autocorrelation
- 07-2015 Spatial stochastic process models
- 07-2015 Direct representation
- 07-2015 Aymptotics in spatial stochastic processes
- 07-2015 Spatial Regression Models
- 07-2015 Spatial dependence in panel data models
- 07-2015 Spatial dependence in models for qualitative data
- 07-2015 Estimation
- 07-2015 Spatial two-stage least squares
- 07-2015 Method of moments estimators
- 07-2015 Specification Tests
- 07-2015 Implementation Issues
- 07-2015 Essentials of Count. Data Regression
- 07-2015 Poisson Regression
- 07-2015 Interpretation of regression coefficients
- 07-2015 Truncation and censoring
- 07-2015 Overdispersion
- 07-2015 Other Parametric Count Regression Models
- 07-2015 Continuous mixture models
- 07-2015 Finite mixture models
- 07-2015 Modified count models
- 07-2015 Discrete choice models
- 07-2015 Partially Parametric Models
- 07-2015 Least squares estimation
- 07-2015 Semiparametric models
- 07-2015 Time Series, Multivariate and Panel Data
- 07-2015 Multivariate data
- 07-2015 Practical Considerations
- 07-2015 Further Reading
- 07-2015 Panel Data Models
- 07-2015 Linear Models
- 07-2015 Dynamic Models
- 07-2015 Sample Attrition and Sample Selection
- 07-2015 Qualitative Response. Models
- 07-2015 Binary and Multinomial Response Models
- 07-2015 Panel Data with Qualitative Variables
- 07-2015 Semiparametric Estimation
- 07-2015 Simulation Methods
- 07-2015 Self-Selection
- 07-2015 Sample Selection Bias
- 07-2015 Some conventional sample selection models
- 07-2015 Parametric Estimation
- 07-2015 Polychotomous choice sample selection models
- 07-2015 Simulation estimation
- 07-2015 Estimation of simultaneous equation sample selection model
- 07-2015 Misspecification and tests
- 07-2015 Semiparametric and Nonparametric Approaches
- 07-2015 Semiparametric efficiency bound and semiparametric MLE
- 07-2015 Semiparametric IV estimation and conditional moments restrictions
- 07-2015 Estimation of the intercept
- 07-2015 Sample selection models with a tobit selection rule
- 07-2015 Identification and estimation of counterfactual outcomes
- 07-2015 Random Coefficient. Models
- 07-2015 Some First-Generation RCMs
- 07-2015 Second-Generation RCMs
- 07-2015 Criteria for Choosing Concomitants in RCMs
- 07-2015 An Empirical Example
- 07-2015 Serial Correlation
- 07-2015 The Box-Jenkins class of models
- 07-2015 Serial correlation in the disturbances of the linear regression model
- 07-2015 Maximum likelihood estimation
- 07-2015 Maximum marginal likelihood estimation
- 07-2015 Hypothesis Testing
- 07-2015 Testing disturbances in the dynamic linear regression model
- 07-2015 Model Selection
- 07-2015 Heteroskedasticity
- 07-2015 Sampling Theory Inference with Known Covariance Matrix
- 07-2015 Sampling Theory Estimation and Inference with Unknown Covariance Matrix
- 07-2015 Testing for heteroskedasticity
- 07-2015 Other extensions
- 07-2015 Concluding Remarks
- 07-2015 Seemingly Unrelated. Regression
- 07-2015 Basic Model
- 07-2015 Stochastic Specification
- 07-2015 Testing linear restrictions
- 07-2015 Diagnostic testing
- 07-2015 Other Developments
- 07-2015 Computational matters
- 07-2015 Bayesian methods
- 07-2015 Improved estimation
- 07-2015 Simultaneous. Equation Model. Estimators: Statistical. Properties and. Practical Implications
- 07-2015 Limited-Information Estimators of Structural Parameters
- 07-2015 Full Information Methods
- 07-2015 Large sample properties of limited information estimators
- 07-2015 Large sample properties of full information estimators
- 07-2015 Structure of Limited Information Estimators as Regression Functions
- 07-2015 Finite Sample Properties of Estimators
- 07-2015 Practical Implications
- 07-2015 Identification in Parametric Models
- 07-2015 Basic Concepts
- 07-2015 The Jacobian Matrix Criterion
- 07-2015 Prior Information
- 07-2015 Handling the Rank in Practice
- 07-2015 The Classical Simultaneous Equations Model
- 07-2015 Measurement Error. and Latent Variables
- 07-2015 The Linear Regression Model with Measurement Error
- 07-2015 Inconsistency and bias of the OLS estimators
- 07-2015 Bounds on the parameters
- 07-2015 Solutions to the Measurement Error Problem
- 07-2015 Instrumental variables
- 07-2015 Panel data
- 07-2015 Latent Variable Models
- 07-2015 Factor analysis
- 07-2015 MIMIC and reduced rank regression
- 07-2015 General linear structural equation models
- 07-2015 Diagnostic Testing in Cross Section Contexts
- 07-2015 Diagnostic testing in nonlinear models of conditional means
- 07-2015 Diagnostic Testing in Time Series Contexts
- 07-2015 Omnibus tests on the errors in time series regression models
- 07-2015 Basic Elements of. Asymptotic Theory
- 07-2015 Modes of Convergence for Sequences of Random Vectors
- 07-2015 Convergence in distribution
- 07-2015 Convergence properties and transformations
- 07-2015 Orders of magnitude
- 07-2015 Independent processes
Страницы:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646
