Chapter 6
NON-LINEAR REGRESSION MODELS
TAKESHI AMEMIYA*
Stanford University
Contents
1. Introduction 334
2. Single equation-i.i.d. case 336
2.1. Model 336
2.2. Asymptotic properties 337
2.3. Computation 341
2.4. Tests of hypotheses 347
2.5. Confidence regions 352
3. Single equation-non-i.i.d. case 354
3. I. Autocorrelated errors 354
3.2. Heteroscedastic errors 358
4. Multivariate models 359
5. Simultaneous equations models 362
5.1. Non-linear two-stage least squares estimator 362
5.2. Other single equation estimators 370
5.3. Non-linear simultaneous equations 375
5.4. Non-linear three-stage least squares estimator 376
5.5. Non-linear full information maximum likelihood estimator 379
References 385

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