Muutke küpsiste eelistusi

Handbook of Computational Econometrics [Other digital carrier]

Edited by (Newtonville, MA, USA), Edited by
  • Formaat: Other digital carrier, 514 pages, kõrgus x laius x paksus: 253x175x33 mm, kaal: 1010 g
  • Ilmumisaeg: 19-Aug-2009
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 0470748915
  • ISBN-13: 9780470748916
Teised raamatud teemal:
Handbook of Computational Econometrics
  • Formaat: Other digital carrier, 514 pages, kõrgus x laius x paksus: 253x175x33 mm, kaal: 1010 g
  • Ilmumisaeg: 19-Aug-2009
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 0470748915
  • ISBN-13: 9780470748916
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470748916.html
Märksõnad:
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.
List of Contributors. Preface. 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.
Acknowledgments. References. 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.
Acknowledgments. References. 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.
Acknowledgments. References. 4 Algorithms for minimax and expected value
optimization (Panos Parpas and BercRustem). 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. Acknowledgments. References. 5
Nonparametric estimation (Rand R. Wilcox). 5.1 Introduction. 5.2 Density
estimation. 5.3 Nonparametric regression. 5.4 Nonparametric inferential
techniques. References. 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.
Acknowledgments. References. 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.
Acknowledgments. References. 8 Econometric analysis with vector
autoregressive models (Helmut Lutkepohl). 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. Acknowledgments.
References. 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. References. 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. Acknowledgments.
References. 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. Acknowledgments. References. Index.