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.

Handbook of Computational Economics summarizes recent advances in economic thought, revealing some of the potential offered by modern computational methods. With computational power increasing in hardware and algorithms, many economists are closing the gap between economic practice and the frontiers of computational mathematics. In their efforts to accelerate the incorporation of computational power into mainstream research, contributors to this volume update the improvements in algorithms that have sharpened econometric tools, solution methods for dynamic optimization and equilibrium models, and applications to public finance, macroeconomics, and auctions. They also cover the switch to massive parallelism in the creation of more powerful computers, with advances in the development of high-power and high-throughput computing. Much more can be done to expand the value of computational modeling in economics. In conjunction with volume one (1996) and volume two (2006), this volume offers a remarkable picture of the recent development of economics as a science as well as an exciting preview of its future potential. - Samples different styles and approaches, reflecting the breadth of computational economics as practiced today - Focuses on problems with few well-developed solutions in the literature of other disciplines - Emphasizes the potential for increasing the value of computational modeling in economics

The explosive growth in computational power over the past several decades offers new tools and opportunities for economists. This handbook volume surveys recent research on Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting agents. Empirical referents for "agents" in ACE models can range from individuals or social groups with learning capabilities to physical world features with no cognitive function. Topics covered include: learning; empirical validation; network economics; social dynamics; financial markets; innovation and technological change; organizations; market design; automated markets and trading agents; political economy; social-ecological systems; computational laboratory development; and general methodological issues.*Every volume contains contributions from leading researchers*Each Handbook presents an accurate, self-contained survey of a particular topic *The series provides comprehensive and accessible surveys

The Oxford Handbook of Computational Economics and Finance provides a survey of both the foundations of and recent advances in the frontiers of analysis and action. It is both historically and interdisciplinarily rich and also tightly connected to the rise of digital society. It begins with the conventional view of computational economics, including recent algorithmic development in computing rational expectations, volatility, and general equilibrium. It then moves from traditional computing in economics and finance to recent developments in natural computing, including applications of nature-inspired intelligence, genetic programming, swarm intelligence, and fuzzy logic. Also examined are recent developments of network and agent-based computing in economics. How these approaches are applied is examined in chapters on such subjects as trading robots and automated markets. The last part deals with the epistemology of simulation in its trinity form with the integration of simulation, computation, and dynamics. Distinctive is the focus on natural computationalism and the examination of the implications of intelligent machines for the future of computational economics and finance. Not merely individual robots, but whole integrated systems are extending their "immigration" to the world of Homo sapiens, or symbiogenesis.

Handbook of computational economics. - v. 1

Reflecting current technological capacities and analytical trends, Computational Methods in Statistics and Econometrics showcases Monte Carlo and nonparametric statistical methods for models, simulations, analyses, and interpretations of statistical and econometric data. The author explores applications of Monte Carlo methods in Bayesian estimation, state space modeling, and bias correction of ordinary least squares in autoregressive models. The book offers straightforward explanations of mathematical concepts, hundreds of figures and tables, and a range of empirical examples. A CD-ROM packaged with the book contains all of the source codes used in the text.

The book is motivated by the disruptions introduced by the financial crisis and the many attempts that have followed to propose new ideas and remedies. Assembling contributions by authors from a variety of backgrounds, this collection illustrates the potentials resulting from the marriage of financial economics, complexity theory and an out-of-equilibrium view of the economic world. Challenging the traditional hypotheses that lie behind financial market functioning, new evidence is provided about the hidden factors fuelling bubbles, the impact of agents’ heterogeneity, the importance of endogeneity in the information transmission mechanism, the dynamics of herding, the sources of volatility, the portfolio optimization techniques, the financial innovation and the trend identification in a nonlinear time-series framework. Presenting the advances made in financial market analysis, and putting emphasis on nonlinear dynamics, this book suggests interdisciplinary methodologies for the study of well-known stylised facts and financial abnormalities. This book was originally published as a special issue of The European Journal of Finance.

Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.

The economics literature on industry dynamics contains a wide array of empirical works identifying a set of stylized facts. There have been several attempts at constructing analytical models to explain some of these regularities. These attempts are highly stylized and limited in scope to keep the analyses tractable. A general model of industry evolution capable of generating firm and industry behaviour that can match the data is needed. This book endeavours to explain many well-documented aspects of the evolution of industries over time. It uses an agent-based computational model in which artificial industries are created and grown to maturity in silico. While the firms in the model are assumed to have bounded rationality, they are nevertheless adaptive in the sense that their experience-based R&D efforts allow them to search for improved technologies. Given a technological environment subject to persistent and unexpected external shocks, the computationally generated industry remains in a perennial state of flux. The main objective of this study is to identify patterns that exist in the movements of firms as the industry evolves over time along the steady state in which the measured behaviour of the firms and the industry stochastically fluctuate around steady means. The computational model developed in this book is able to replicate many of the stylized facts from the empirical industrial organization literature, particularly as the facts pertain to the dynamics of firm entry and exit. Furthermore, the model allows examination of cross-industry variations in entry and exit patterns by systematically varying the characteristics of the market and the technological environment within which the computationally generated industry evolves. The model demonstrates that the computational approach based on boundedly rational agents in a dynamic setting can be useful and effective in carrying out both positive and normative economic analysis.

To harness the full power of computer technology, economists need to use a broad range of mathematical techniques. In this book, Kenneth Judd presents techniques from the numerical analysis and applied mathematics literatures and shows how to use them in economic analyses. The book is divided into five parts. Part I provides a general introduction. Part II presents basics from numerical analysis on R^n, including linear equations, iterative methods, optimization, nonlinear equations, approximation methods, numerical integration and differentiation, and Monte Carlo methods. Part III covers methods for dynamic problems, including finite difference methods, projection methods, and numerical dynamic programming. Part IV covers perturbation and asymptotic solution methods. Finally, Part V covers applications to dynamic equilibrium analysis, including solution methods for perfect foresight models and rational expectation models. A website contains supplementary material including programs and answers to exercises.