TAILIEUCHUNG - NON-LINEAR REGRESSION MODELS

This is a survey of non-linear regression models, with an emphasis on the theory of estimation and hypothesis testing rather than computation and applications, although there will be some discussion of the last two topics. For a general discussion of computation the reader is referred to Chapter 12 of this Handbook by Quandt. My aim is to present the gist of major results; therefore, I will sometimes omit proofs and less significant assumptions. For those, the reader must consult the original sources | Chapter 6 NON-LINEAR REGRESSION MODELS TAKESHI AMEMIYA Stanford University Contents 1. Introduction 334 2. Single . case 336 . Model 336 . Asymptotic properties 337 . Computation 341 . Tests of hypotheses 347 . Confidence regions 352 3. Single . case 354 . Autocorrelated errors 354 . Heteroscedastic errors 358 4. Multivariate models 359 5. Simultaneous equations models 362 . Non-linear two-stage least squares estimator 362 . Other single equation estimators 370 . Non-linear simultaneous equations 375 . Non-linear three-stage least squares estimator 376 . Non-linear full information maximum likelihood estimator 379 References 385 This work was supported by National Science Foundation Grant SES-7912965 at the Institute for Mathematical Studies in the Social Sciences Stanford University. The author is indebted to the following people for valuable comments R. C. Fair A. R. Gallant Z. Griliches M. D. Intriligator T. E. MaCurdy J. L. Powell R. E. Quandt N. E. Savin and H. White. Handbook of Econometrics Volume I Edited by Z. Griliches and . Intriligator North-Holland Publishing Company 1983 334 T. Amemiya 1. Introduction This is a survey of non-linear regression models with an emphasis on the theory of estimation and hypothesis testing rather than computation and applications although there will be some discussion of the last two topics. For a general discussion of computation the reader is referred to Chapter 12 of this Handbook by Quandt. My aim is to present the gist of major results therefore I will sometimes omit proofs and less significant assumptions. For those the reader must consult the original sources. The advent of advanced computer technology has made it possible for the econometrician to estimate an increasing number of non-linear regression models in recent years. Non-linearity arises in many diverse ways in econometric applications. Perhaps the simplest and best known case of .

• Vật lý
• Asymptotic properties
• Tests of hypotheses
• Confidence regions
• Autocorrelated errors
• equation estimators
• simultaneous equations
• Lyapunov exponents
• Asymptotic stability
• Positive homogeneous
• Homogeneous lattices
• Local Lyapunov exponents
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