05546nam 2200685Ia 450 991083081170332120230721022809.01-282-27912-297866122791260-470-74891-50-470-74890-7(CKB)1000000000794240(EBL)454349(SSID)ssj0000167289(PQKBManifestationID)11158984(PQKBTitleCode)TC0000167289(PQKBWorkID)10169247(PQKB)11552848(MiAaPQ)EBC454349(OCoLC)441893051(EXLCZ)99100000000079424020090623d2009 uy 0engur|n|---|||||txtccrHandbook of computational econometrics[electronic resource] /edited by David A. Belsley, Erricos KontoghiorghesChichester, West Sussex, U.K. ;Hoboken, N.J. Wileyc20091 online resource (516 p.)Wiley Series in Computational StatisticsDescription based upon print version of record.0-470-74385-9 Includes bibliographical references and index.Handbook of Computational Econometrics; Contents; List of Contributors; Preface; 1 Econometric software; 1.1 Introduction; 1.2 The nature of econometric software; 1.2.1 The characteristics of early econometric software; 1.2.2 The expansive development of econometric software; 1.2.3 Econometric computing and the microcomputer; 1.3 The existing characteristics of econometric software; 1.3.1 Software characteristics: broadening and deepening; 1.3.2 Software characteristics: interface development; 1.3.3 Directives versus constructive commands; 1.3.4 Econometric software design implications1.4 ConclusionAcknowledgments; References; 2 The accuracy of econometric software; 2.1 Introduction; 2.2 Inaccurate econometric results; 2.2.1 Inaccurate simulation results; 2.2.2 Inaccurate GARCH results; 2.2.3 Inaccurate VAR results; 2.3 Entry-level tests; 2.4 Intermediate-level tests; 2.4.1 NIST Statistical Reference Datasets; 2.4.2 Statistical distributions; 2.4.3 Random numbers; 2.5 Conclusions; Acknowledgments; References; 3 Heuristic optimization methods in econometrics; 3.1 Traditional numerical versus heuristic optimization methods; 3.1.1 Optimization in econometrics3.1.2 Optimization heuristics3.1.3 An incomplete collection of applications of optimization heuristics in econometrics; 3.1.4 Structure and instructions for use of the chapter; 3.2 Heuristic optimization; 3.2.1 Basic concepts; 3.2.2 Trajectory methods; 3.2.3 Population-based methods; 3.2.4 Hybrid metaheuristics; 3.3 Stochastics of the solution; 3.3.1 Optimization as stochastic mapping; 3.3.2 Convergence of heuristics; 3.3.3 Convergence of optimization-based estimators; 3.4 General guidelines for the use of optimization heuristics; 3.4.1 Implementation; 3.4.2 Presentation of results3.5 Selected applications3.5.1 Model selection in VAR models; 3.5.2 High breakdown point estimation; 3.6 Conclusions; Acknowledgments; References; 4 Algorithms for minimax and expected value optimization; 4.1 Introduction; 4.2 An interior point algorithm; 4.2.1 Subgradient of (x) and basic iteration; 4.2.2 Primal-dual step size selection; 4.2.3 Choice of c and μ; 4.3 Global optimization of polynomial minimax problems; 4.3.1 The algorithm; 4.4 Expected value optimization; 4.4.1 An algorithm for expected value optimization4.5 Evaluation framework for minimax robust policies and expected value optimizationAcknowledgments; References; 5 Nonparametric estimation; 5.1 Introduction; 5.1.1 Comments on software; 5.2 Density estimation; 5.2.1 Some illustrations; 5.3 Nonparametric regression; 5.3.1 An illustration; 5.3.2 Multiple predictors; 5.3.3 Some illustrations; 5.3.4 Estimating conditional associations; 5.3.5 An illustration; 5.4 Nonparametric inferential techniques; 5.4.1 Some motivating examples; 5.4.2 A bootstrap-t method; 5.4.3 The percentile bootstrap method; 5.4.4 Simple ordinary least squares regression5.4.5 Regression with multiple predictorsHandbook 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. 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