David F. Hendry – författare

This Handbook provides up-to-date coverage of both new developments and well-established fields in the sphere of economic forecasting. The chapters are written by world experts in their respective fields, and provide authoritative yet accessible accounts of the key concepts, subject matter and techniques in a number of diverse but related areas. It covers the ways in which the availability of ever more plentiful data and computational power have been used in forecasting, either in terms of the frequency of observations, the number of variables, or the use of multiple data vintages. Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained; the volume provides explanations and critiques of these developments. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models, as well as models for handling data observed at mixed frequencies, high-frequency data, multiple data vintages, and methods for forecasting when there are structural breaks, and how breaks might be forecast. Also covered are areas which are less commonly associated with economic forecasting, such as climate change, health economics, long-horizon growth forecasting, and political elections. Econometric forecasting has important contributions to make in these areas, as well as their developments informing the mainstream. In the early 21st century, climate change and the forecasting of health expenditures and population are topics of pressing importance.

This book confronts the practical problems of modelling aggregate time series data, in a systematic and intergrated framework.The main problem in econometric modelling of time series is discovering sustainable and interpretable relationships between observed economic variables. The primary aim of this book is to develop an operational econometric approach which allows constructive modelling. Professor Hendry deals with methodological issues (model discovery, data mining, and progressive research strategies); with major tools for modelling (recursive methods, encompassing, super exogeneity, invariance tests); and with practical problems (collinearity, heteroscedasticity, and measurement errors). He also includes an extensive study of US money demand.The book is self-contained, with the technical background covered in appendices. It is thus suitable for first year graduate students, and includes solved examples and exercises to facilitate its use in teaching.

"Econometrics: Alchemy or Science?" analyses the effectiveness and validity of applying econometric methods to economic time series. The methodological dispute is long-standing, and no claim can be made for a single valid method, but recent results on the theory and practice of model selection bid fair to resolve many of the contentious issues. The book presents criticisms and evaluations of competing approaches, based on theoretical economic and econometric analyses, empirical applications, and Monte Carlo simulations, which interact to determine best practice. It explains the evolution of an approach to econometric modelling founded in careful statistical analyses of the available data, using economic theory to guide the general model specification. From a strong foundation in the theory of reduction, via a range of applied and simulation studies, it demonstrates that general-to-specific procedures have excellent properties. The book is divided into four Parts: Routes and Route Maps; Empirical Modelling Strategies; Formalization; and Retrospect and Prospect. A short preamble to each chapter sketches the salient themes, links to earlier and later developments, and the lessons learnt or missed at the time. A sequence of detailed empirical studies of consumers' expenditure and money demand illustrate most facets of the approach. Material new to this revised edition describes recent major advances in computer-automated model selection, embodied in the powerful new software program PcGets, which establish the operational success of the modelling strategy.

Nonlinear Econometric Modeling in Time Series presents the more recent literature on nonlinear time series. Specific topics covered with respect to nonlinearity include cointegration tests, risk-related asymmetries, structural breaks and outliers, Bayesian analysis with a threshold, consistency and asymptotic normality, asymptotic inference and error-correction models. With a world-class panel of contributors, this volume addresses topics with major applications for fields such as foreign-exchange markets and interest rate analysis. Eleventh in this series of international symposia, this volume is also part of the European Conference Series in Quantitative Economics and Econometrics (EC)2.

In this compelling 1995 book, David Hendry and Mary Morgan bring together the classic papers of the pioneer econometricians. Together, these papers form the foundations of econometric thought. They are essential reading for anyone seeking to understand the aims, method and methodology of econometrics and the development of this statistical approach in economics. However, because they are technically straightforward, the book is also accessible to students and non-specialists. An editorial commentary places the readings in their historical context and indicates the continuing relevance of these early, yet highly sophisticated, works for current econometric analysis. While this book provides a companion volume to Mary Morgan's acclaimed The History of Econometric Ideas, the editors' commentary both adds to that earlier volume and also provides a stand-alone and synthetic account of the development of econometrics.

In this compelling 1995 book, David Hendry and Mary Morgan bring together the classic papers of the pioneer econometricians. Together, these papers form the foundations of econometric thought. They are essential reading for anyone seeking to understand the aims, method and methodology of econometrics and the development of this statistical approach in economics. However, because they are technically straightforward, the book is also accessible to students and non-specialists. An editorial commentary places the readings in their historical context and indicates the continuing relevance of these early, yet highly sophisticated, works for current econometric analysis. While this book provides a companion volume to Mary Morgan's acclaimed The History of Econometric Ideas, the editors' commentary both adds to that earlier volume and also provides a stand-alone and synthetic account of the development of econometrics.

Nonlinear Econometric Modeling in Time Series presents the more recent literature on nonlinear time series. Specific topics covered with respect to nonlinearity include cointegration tests, risk-related asymmetries, structural breaks and outliers, Bayesian analysis with a threshold, consistency and asymptotic normality, asymptotic inference and error-correction models. With a world-class panel of contributors, this volume addresses topics with major applications for fields such as foreign-exchange markets and interest rate analysis. Eleventh in this series of international symposia, this volume is also part of the European Conference Series in Quantitative Economics and Econometrics (EC)2.

Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied. Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students.Throughout, data illustrate and motivate the approach, and are available for computer-based teaching. Technical issues from probability theory and statistical theory are introduced only as needed. Nevertheless, the approach is rigorous, emphasizing the coherent formulation, estimation, and evaluation of econometric models relevant for empirical research.

A Companion to Economic Forecasting provides an accessible and comprehensive account of recent developments in economic forecasting. Each of the chapters has been specially written by an expert in the field, bringing together in a single volume a range of contrasting approaches and views. Uniquely surveying forecasting in a single volume, the Companion provides a comprehensive account of the leading approaches and modeling strategies that are routinely employed.

Economists have long sought to develop quantitative models of economic behaviour, which blend economic theory with data evidence. Econometric modelling of economic time series has strived to achieve this by seeking to discover sustainable and interpretable relationships. This important two-volume collection focuses on a central method used in selecting such models, namely simplification of an initially general model that adequately characterizes the empirical evidence within the investigators' theoretical framework. The volumes feature a wealth of evidence that has accrued over the last five years displaying its excellent abilities for model selection, based on Monte Carlo studies of automatic algorithms. These also throw light on several major methodological issues, and prompt many new ideas, which are discussed. The collection will be valuable to all empirical economists and econometricians.

This open access book focuses on the concepts, tools and techniques needed to successfully model ever-changing time-series data. It emphasizes the need for general models to account for the complexities of the modern world and how these can be applied to a range of issues facing Earth, from modelling volcanic eruptions, carbon dioxide emissions and global temperatures, to modelling unemployment rates, wage inflation and population growth.Except where otherwise noted, this book is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0.