Dynamic Nonlinear Econometric Models

1. Front Matter

   Pages i-xi

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2. Introduction

   • Benedikt M. Pötscher, Ingmar R. Prucha

   Pages 1-7

3. Models, Data Generating Processes, and Estimators

   • Benedikt M. Pötscher, Ingmar R. Prucha

   Pages 9-13

4. Basic Structure of the Classical Consistency Proof

   • Benedikt M. Pötscher, Ingmar R. Prucha

   Pages 15-21

5. Further Comments on Consistency Proofs

   • Benedikt M. Pötscher, Ingmar R. Prucha

   Pages 23-35

6. Uniform Laws of Large Numbers

   • Benedikt M. Pötscher, Ingmar R. Prucha

   Pages 37-44

7. Approximation Concepts and Limit Theorems

   • Benedikt M. Pötscher, Ingmar R. Prucha

   Pages 45-77

8. Consistency: Catalogues of Assumptions

   • Benedikt M. Pötscher, Ingmar R. Prucha

   Pages 79-82

9. Basic Structure of the Asymptotic Normality Proof

   • Benedikt M. Pötscher, Ingmar R. Prucha

   Pages 83-93

10. Asymptotic Normality under Nonstandard Conditions

    • Benedikt M. Pötscher, Ingmar R. Prucha

    Pages 95-98

11. Central Limit Theorems

    • Benedikt M. Pötscher, Ingmar R. Prucha

    Pages 99-103

12. Asymptotic Normality: Catalogues of Assumptions

    • Benedikt M. Pötscher, Ingmar R. Prucha

    Pages 105-120

13. Heteroskedasticity and Autocorrelation Robust Estimation of Variance Covariance Matrices

    • Benedikt M. Pötscher, Ingmar R. Prucha

    Pages 121-136

14. Consistent Variance Covariance Matrix Estimation: Catalogues of Assumptions

    • Benedikt M. Pötscher, Ingmar R. Prucha

    Pages 137-143

15. Quasi Maximum Likelihood Estimation of Dynamic Nonlinear Simultaneous Systems

    • Benedikt M. Pötscher, Ingmar R. Prucha

    Pages 145-169

16. Concluding Remarks

    • Benedikt M. Pötscher, Ingmar R. Prucha

    Pages 171-173

17. Back Matter

    Pages 175-312

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Many relationships in economics, and also in other fields, are both dynamic and nonlinear. A major advance in econometrics over the last fifteen years has been the development of a theory of estimation and inference for dy­ namic nonlinear models. This advance was accompanied by improvements in computer technology that facilitate the practical implementation of such estimation methods. In two articles in Econometric Reviews, i.e., Pötscher and Prucha {1991a,b), we provided -an expository discussion of the basic structure of the asymptotic theory of M-estimators in dynamic nonlinear models and a review of the literature up to the beginning of this decade. Among others, the class of M-estimators contains least mean distance estimators (includ­ ing maximum likelihood estimators) and generalized method of moment estimators. The present book expands and revises the discussion in those articles. It is geared towards the professional econometrician or statistician. Besides reviewing the literature we also presented in the above men­ tioned articles a number of then new results. One example is a consis­ tency result for the case where the identifiable uniqueness condition fails.

• Covariance matrix
• Estimator
• Likelihood
• Variance
• correlation

• Department of Statistics, Operations Research and Computer Science, University of Vienna, Vienna, Austria

  Benedikt M. Pötscher

• Department of Economics, University of Maryland at College Park, College Park, USA

  Ingmar R. Prucha

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