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Table of Contents

                            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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