Title | Handbook of Computational Econometrics |
---|---|

Tags | Econometrics |

Language | English |

File Size | 4.0 MB |

Total Pages | 516 |

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 implications 1.4 Conclusion Acknowledgments 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 econometrics 3.1.2 Optimization heuristics 3.1.3 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 results 3.5 Selected applications 3.5.1 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 Φ(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 optimization 4.5 Evaluation framework for minimax robust policies and expected value optimization Acknowledgments 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 regression 5.4.5 Regression with multiple predictors References 6 Bootstrap hypothesis testing 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 6.5.1 Resampling and the pairs bootstrap 6.5.2 The residual bootstrap 6.5.3 The wild bootstrap 6.5.4 Bootstrap DGPs for multivariate regression models 6.5.5 Bootstrap DGPs for dependent data 6.6 Multiple test statistics 6.6.1 Tests for structural change 6.6.2 Point-optimal tests 6.6.3 Non-nested hypothesis tests 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 7.1 Introduction 7.2 A primer on Bayesian inference 7.2.1 Motivation for Bayesian inference 7.2.2 Bayes’ theorem as a learning device 7.2.3 Model evaluation and model selection 7.2.4 Comparison of Bayesian inference and frequentist approach 7.3 A primer on simulation methods 7.3.1 Motivation for using simulation techniques 7.3.2 Direct sampling methods 7.3.3 Indirect sampling methods yielding independent draws 7.3.4 Markov chain Monte Carlo: indirect sampling methods yielding dependent draws 7.4 Some recently developed simulation methods 7.4.1 Adaptive radial-based direction sampling 7.4.2 Adaptive mixtures of t distributions 7.5 Concluding remarks Acknowledgments References 8 Econometric analysis with vector autoregressive models 8.1 Introduction 8.1.1 Integrated variables 8.1.2 Structure of the chapter 8.1.3 Terminology and notation 8.2 VAR processes 8.2.1 The levels VAR representation 8.2.2 The VECM representation 8.2.3 Structural forms 8.3 Estimation of VAR models 8.3.1 Estimation of unrestricted VARs 8.3.2 Estimation of VECMs 8.3.3 Estimation with linear restrictions 8.3.4 Bayesian estimation of VARs 8.4 Model specification 8.4.1 Choosing the lag order 8.4.2 Choosing the cointegrating rank of a VECM 8.5 Model checking 8.5.1 Tests for residual autocorrelation 8.5.2 Tests for non-normality 8.5.3 ARCH tests 8.5.4 Stability analysis 8.6 Forecasting 8.6.1 Known processes 8.6.2 Estimated processes 8.7 Causality analysis 8.7.1 Intuition and theory 8.7.2 Testing for Granger-causality 8.8 Structural VARs and impulse response analysis 8.8.1 Levels VARs 8.8.2 Structural VECMs 8.8.3 Estimating impulse responses 8.8.4 Forecast error variance decompositions 8.9 Conclusions and extensions Acknowledgments References 9 Statistical signal extraction and filtering: a partial survey 9.1 Introduction: the semantics of filtering 9.2 Linear and circular convolutions 9.2.1 Kernel smoothing 9.3 Local polynomial regression 9.4 The concepts of the frequency domain 9.4.1 The periodogram 9.4.2 Filtering and the frequency domain 9.4.3 Aliasing and the Shannon–Nyquist sampling theorem 9.4.4 The processes underlying the data 9.5 The classical Wiener–Kolmogorov theory 9.6 Matrix formulations 9.6.1 Toeplitz matrices 9.6.2 Circulant matrices 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 9.11.1 The smoothing algorithms 9.11.2 Equivalent and alternative procedures References 10 Concepts of and tools for nonlinear time-series modelling 10.1 Introduction 10.2 Nonlinear data generating processes and linear models 10.2.1 Linear and nonlinear processes 10.2.2 Linear representation of nonlinear processes 10.3 Testing linearity 10.3.1 Weak white noise and strong white noise testing 10.3.2 Testing linearity against a specific nonlinear model 10.3.3 Testing linearity when the model is not identified under the null 10.4 Probabilistic tools 10.4.1 A strict stationarity condition 10.4.2 Second-order stationarity and existence of moments 10.4.3 Mixing coefficients 10.4.4 Geometric ergodicity and mixing properties 10.5 Identification, estimation and model adequacy checking 10.5.1 Consistency of the QMLE 10.5.2 Asymptotic distribution of the QMLE 10.5.3 Identification and model adequacy 10.6 Forecasting with nonlinear models 10.6.1 Forecast generation 10.6.2 Interval and density forecasts 10.6.3 Volatility forecasting 10.6.4 Forecast combination 10.7 Algorithmic aspects 10.7.1 MCMC methods 10.7.2 Optimization algorithms for models with several latent processes 10.8 Conclusion Acknowledgments References 11 Network economics 11.1 Introduction 11.2 Variational inequalities 11.2.1 Systems of equations 11.2.2 Optimization problems 11.2.3 Complementarity problems 11.2.4 Fixed point problems 11.3 Transportation networks: user optimization versus system optimization 11.3.1 Transportation network equilibrium with travel disutility functions 11.3.2 Elastic demand transportation network problems with known travel demand functions 11.3.3 Fixed demand transportation network problems 11.3.4 The system-optimized problem 11.4 Spatial price equilibria 11.4.1 The quantity model 11.4.2 The price model 11.5 General economic equilibrium 11.6 Oligopolistic market equilibria 11.6.1 The classical oligopoly problem 11.6.2 A spatial oligopoly model 11.7 Variational inequalities and projected dynamical systems 11.7.1 Background 11.7.2 The projected dynamical system 11.8 Dynamic transportation networks 11.8.1 The path choice adjustment process 11.8.2 Stability analysis 11.8.3 Discrete-time algorithms 11.8.4 A dynamic spatial price model 11.9 Supernetworks: applications to telecommuting decision making and teleshopping decision making 11.10 Supply chain networks and other applications Acknowledgments References Index

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