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Handbook of Computational Econometrics. Wiley Series in Computational Statistics

  • ID: 2171666
  • Book
  • August 2009
  • Region: Global
  • 514 Pages
  • John Wiley and Sons Ltd
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Handbook of Computational Econometrics examines the state of the art of computational econometrics and provides exemplary studies dealing with computational issues arising from a wide spectrum of econometric fields including such topics as bootstrapping, the evaluation of econometric software, and algorithms for control, optimization, and estimation. Each topic is fully introduced before proceeding to a more in–depth examination of the relevant methodologies and valuable illustrations.

This book:

- Provides self–contained treatments of issues in computational econometrics with illustrations and invaluable bibliographies.
- Brings together contributions from leading researchers.
- Develops the techniques needed to carry out computational econometrics.
- Features network studies, non–parametric estimation, optimization techniques, Bayesian estimation and inference, testing methods, time–series analysis, linear and nonlinear methods, VAR analysis, bootstrapping developments, signal extraction, software history and evaluation.

This book will appeal to econometricians, financial statisticians, econometric researchers and students of econometrics at both graduate and advanced undergraduate levels.
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List of Contributors.


1 Econometric software (Charles G. Renfro).

1.1 Introduction.

1.2 The nature of econometric software.

1.3 The existing characteristics of econometric software.

1.4 Conclusion.



2 The accuracy of econometric software (Bruce D. McCullough ).

2.1 Introduction.

2.2 Inaccurate econometric results.

2.3 Entry–level tests.

2.4 Intermediate–level tests.

2.5 Conclusions.



3 Heuristic optimization methods in econometrics (Manfred Gilli and Peter Winker).

3.1 Traditional numerical versus heuristic optimization methods.

3.2 Heuristic optimization.

3.3 Stochastics of the solution.

3.4 General guidelines for the use of optimization heuristics.

3.5 Selected applications.

3.6 Conclusions.



4 Algorithms for minimax and expected value optimization (Panos Parpas and BerçRustem).

4.1 Introduction.

4.2 An interior point algorithm.

4.3 Global optimization of polynomial minimax problems.

4.4 Expected value optimization.

4.5 Evaluation framework for minimax robust policies and expected value optimization.



5 Nonparametric estimation (Rand R. Wilcox).

5.1 Introduction.

5.2 Density estimation.

5.3 Nonparametric regression.

5.4 Nonparametric inferential techniques.


6 Bootstrap hypothesis testing (James G. MacKinnon).

6.1 Introduction.

6.2 Bootstrap and Monte Carlo tests.

6.3 Finite–sample properties of bootstrap tests.

6.4 Double bootstrap and fast double bootstrap tests.

6.5 Bootstrap data generating processes.

6.6 Multiple test statistics.

6.7 Finite–sample properties of bootstrap supF tests.

6.8 Conclusion.



7 Simulation–based Bayesian econometric inference: principles and some recent computational advances (Lennart F. Hoogerheide, Herman K. van Dijk and Rutger D. van Oest).

7.1 Introduction.

7.2 A primer on Bayesian inference.

7.3 A primer on simulation methods.

7.4 Some recently developed simulation methods. 

7.5 Concluding remarks.



8 Econometric analysis with vector autoregressive models (Helmut Lütkepohl).

8.1 Introduction.

8.2 VAR processes.

8.3 Estimation of VAR models.

8.4 Model specification.

8.5 Model checking.

8.6 Forecasting.

8.7 Causality analysis.

8.8 Structural VARs and impulse response analysis.

8.9 Conclusions and extensions.



9 Statistical signal extraction and filtering: a partial survey (D. Stephen G. Pollock).

9.1 Introduction: the semantics of filtering.

9.2 Linear and circular convolutions.

9.3 Local polynomial regression.

9.4 The concepts of the frequency domain.

9.5 The classical Wiener Kolmogorov theory.

9.6 Matrix formulations.

9.7 Wiener Kolmogorov filtering of short stationary sequences.

9.8 Filtering nonstationary sequences.

9.9 Filtering in the frequency domain.

9.10 Structural time–series models.

9.11 The Kalman filter and the smoothing algorithm.


10 Concepts of and tools for nonlinear time–series modelling (Alessandra Amendola and Christian Francq).

10.1 Introduction.

10.2 Nonlinear data generating processes and linear models.

10.3 Testing linearity.

10.4 Probabilistic tools.

10.5 Identification, estimation and model adequacy checking.

10.6 Forecasting with nonlinear models.

10.7 Algorithmic aspects.

10.8 Conclusion.



11 Network economics (Anna Nagurney).

11.1 Introduction.

11.2 Variational inequalities.

11.3 Transportation networks: user optimization versus system optimization.

11.4 Spatial price equilibria.

11.5 General economic equilibrium.

11.6 Oligopolistic market equilibria.

11.7 Variational inequalities and projected dynamical systems.

11.8 Dynamic transportation networks.

11.9 Supernetworks: applications to telecommuting decision making and teleshopping decision making.

11.10 Supply chain networks and other applications.




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David A. Belsley
Erricos Kontoghiorghes
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