Advanced Texts in Econometrics – serie

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.

This is a survey of recent developments in the field of cointegration, which links long run components of a pair or of a group of series. It can then be used to discuss some types of equilibrium and to introduce them into time-series models in a fairly uncontroversial way. The idea was introduced in the early 1980s and has generated much interest since then amongst econometricians and macroeconomists. The authors discuss the basic ideas in their introduction, and the final chapters review the most recent developments in the field in a non-technical way that will enable economists with some training in modern econometrics to understand and appreciate these developments.

This book helps economists with the difficult task of constructing econometric models and will be especially useful to those taking courses in applied econometrics who need to learn how to evaluate the validity of the theories and techniques they are taught. The volume contains seventeen papers by the leading authorities in the field, divided into four groups, to each of which the editor provides an introduction. The whole volume is prefaced with an editorial discussion of the controversies of the subject.The methods critically discussed include the traditional ones, such as vector auto-regressions; Bayesian techniques; and the comprehensive modelling strategy advocated by reseachers at the London School of Economics.The papers vary in the degree of sophistication used, but anyone reading the book should gain a sound knowledge of the practical difficulties involved in model specification, evaluation, and interpretation.

This book provides a wide-ranging account of the literature on co-integration and the modelling of integrated processes (those which accumulate the effects of past shocks). Data series which display integrated behaviour are common in economics, although techniques appropriate to analysing such data are of recent origin and there are few existing expositions of the literature. This book focuses on the exploration of relationships among integrated data series and the exploitation of these relationships in dynamic econometric modelling. The concepts of co-integration and error-correction models are fundamental components of the modelling strategy.This area of time-series econometrics has grown in importance over the past decade and is of interest to econometric theorists and applied econometricians alike. By explaining the important concepts informally, but also presenting them formally, the book bridges the gap between purely descriptive and purely theoretical accounts of the literature. The asymptotic theory of integrated processes is described and the tools provided by this theory are used to develop the distributions of estimators and test statistics. Practical modelling advice, and the use of techniques for systems estimation, are also emphasized.A knowledge of econometrics, statistics, and matrix algebra at the level of a final-year undergraduate or first-year undergraduate course in econometrics is sufficient for most of the book. Other mathematical tools are described as they occur.

This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.

This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.

The realization among econometricians and applied economists that seasonal variation in many time series is often larger and less regular than has been supposed, has recently led to an increased interest in seriously modelling seasonality. The relative size of seasonal variation also means that such modelling is of major economic interest. Important developments in modelling seasonality have occurred - the last ten years have seen improvements in the model-based procedures, the discovery of periodic models, seasonal integration and cointegration, and the development of economic theories of seasonality. This volume brings together some leading papers on the existing standard economic theory of seasonality as well as papers which apply newer statistical tools to the modelling of seasonal phenomena. In addition to the topics already mentioned, it presents and discusses the X-11 method of seasonal adjustment and the introduction includes a description and assessment of the most recent developments.

This volume explains recent theoretical developments in the econometric modelling of relationships between different statistical series. The statistical techniques explored analyse relationships between different variables, over time, such as the relationship between variables in a macroeconomy. Examples from Professor Teräsvirta's empirical work are given. Professors Granger and Teräsvirta are leading exponents of techniques of dynamic, multivariate analysis. They illustrate in this volume exploratory ways of using such techniques to provide models of nonlinear relationships between variables. This is an extension of previous work on linear relationships, and on univariate models. These developments will be of use to econometricians wishing to construct and use models of nonlinear, dynamic, multivariate relationships, such as an investment function, or a production function. Particular attention is paid to the case of a single dependent variable modelled by a few explanatory variables and the lagged dependent variable in nonlinear form. The book concentrates on stochastic series, since the existence of unexpected shocks strongly suggests that economic variables are stochastic. Granger and Teräsvirta also discuss the division of these nonlinear relationships into parametric and nonparametric models.

In the last decade, time-series econometrics has made extraordinary developments on unit roots and cointegration. However, this progress has taken divergent directions, and has been subjected to criticism from outside the field. In this book, Professor Hatanaka surveys the field, examines those portions that are useful for macroeconomics, and responds to the criticism. His survey of the literature covers not only econometric methods, but also the application of these methods to macroeconomic studies.The most vigorous criticism has been that unit roots to do not exist in macroeconomic variables, and thus that cointegration analysis is irrelevant to macroeconomics. The judgement of this book is that unit roots are present in macroeconomic variables when we consider periods of 20 to 40 years, but that the critics may be right when periods of 100 years are considered. Fortunately, most of the time series data used for macroeconomic studies cover fall within the shorter time span.Among the numerous methods for unit roots and cointegration, those useful from macroeconomic studies are examined and explained in detail, without overburdening the reader with unnecessary mathematics. Other, less applicable methods are dicussed briefly, and their weaknesses are exposed. Hatanaka has rigourously based his judgements about usefulness on whether the inference is appropriate for the length of the data sets available, and also on whether a proper inference can be made on the sort of propositions that macroeconomists wish to test.This book highlights the relations between cointegration and economic theories, and presents cointegrated regression as a revolution in econometric methods. Its analysis is of relevance to academic and professional or applied econometricians. Step-by-step explanations of concepts and techniques make the book a self-contained text for graduate students.

Major developments in the analysis of non-stationary time series and co-integration are shown in this book. Papers include David Hendry's work on forecasting, Peter Phillip's work on Bayesian models, Svend Hylleberg's work on seasonality, and Adrian Pagan's work on real business cycle models. Other topics covered include an overview of the different estimators of cointegrating relationships, and a new test of cointegration. Applications are shown finding roots in macroeconomic series, testing the Fisher Hypothesis, testing money demand functions, and testing for inflation bubbles. The book provides good coverage of the depth of this literature showing the importance of an understanding of non-stationarity and co-integration. The other contributors are: F. Canova, Mike P. Clements, Francis X. Diebold, Steven N. Durlauf, Neil R. Ericsson, M. Finn, Colin Hargreaves, David Harris, Mark A. Hooker, Brett Inder, Joon-Haeng Lee, Hong-Anh Tran, Gretchen C. Weinbach

This book discusses the nature of exogeneity - a central concept in econometrics - and shows how to test for it through numerous substantive empirical examples. Part I considers what exogeneity is and how it can be tested. Part II contains applications to models of expenditure, money demand, inflation, wages and prices, and exchange rates across both developed and developing countries. Part III extends various tests of constancy and forecast accuracy, which are central to testing super exogeneity. The papers forming the core of this book (from two special issues of the Journal of Policy Modeling) provide a unique and unified perspective on applied econometric modelling in general and on exogeneity tests in particular. The applications are substantive and diverse, with a broad appeal to the applied economist.Contributors: H. Ahumada, G. Bardsen, J. Campos, M. Deutsch, R. F. Engle, Neil R. Ericsson, C. W. J. Granger, B. E. Hansen, David F. Hendry, J. Hunter, S. Johansen, K. Juselius, R. Numoen, Jean-Francois Richard

In the early 1980s, R.F. Engle pioneered the econometric technique of Auto-Regressive Conditional Heteroskedasticity (ARCH), which has subsequently generated a very considerable literature. This collection brings together the leading papers which have shaped ARCH research from its inception to the latest developments. Papers present both theory and financial market analysis, and discuss the key issues in the use of ARCH models to study volatility and correlation:- what model to use- what time intervals to employ - how to model multivariate systems- how to apply the models to price and trade options- how to model volatility spillovers across markets and within the dayFor each of these issues, the selection of a number of papers by different authors allows a variety of viewpoints to emerge. Many applications to financial markets are included, and a new introduction by the editor connects the papers to trace the development of the field. the result is a timely, useful book which will bring graduate students, faculty, and practitioners up to date on this rapidly expanding field of research.

This book provides a comprehensive and unified treatment of finite sample statistics and econometrics, a field that has evolved in the last five decades. Within this framework, this is the first book which discusses the basic analytical tools of finite sample econometrics, and explores their applications to models covered in a first year graduate course in econometrics, including repression functions, dynamic models, forecasting, simultaneous equations models, panel data models, and censored models. Both linear and nonlinear models, as well as models with normal and non-normal errors, are studied.Finite sample results are extremely useful for applied researchers doing proper econometric analysis with small or moderately large sample data. Finite sample econometrics also provides the results for very large (asymptotic) samples. This book provides simple and intuitive presentations of difficult concepts, unified and heuristic developments of methods, and applications to various econometric models. It provides a new perspective on teaching and research in econometrics, statistics, and other applied subjects.

This text provides a comprehensive and unified treatment of finite sample statistics and econometrics, a field that has evolved since the 1950s. Within this framework, the book discusses the basic analytical tools of finite sample econometrics and explores their applications to models covered in a first year graduate course in econometrics, including repression functions, dynamic models, forecasting, simultaneous equations models, panel data models and censored models. Both linear and nonlinear models, as well as models with normal and non-normal errors, are studied. Finite sample results are extremely useful for applied researchers doing proper econometric analysis with small or moderately large sample data. Finite sample econometrics also provides the results for very large (asymptotic) samples. This book provides simple and intuitive presentations of difficult concepts, unified and heuristic developments of methods and applications to various econometric models. It provides a new perspective on teaching and research in econometrics, statistics and other applied subjects.

This book gives a detailed mathematical and statistical analysis of the cointegrated vector autoregresive model. This model had gained popularity because it can at the same time capture the short-run dynamic properties as well as the long-run equilibrium behaviour of many non-stationary time series. It also allows relevant economic questions to be formulated in a consistent statistical framework.Part I of the book is planned so that it can be used by those who want to apply the methods without going into too much detail about the probability theory. The main emphasis is on the derivation of estimators and test statistics through a consistent use of the Guassian likelihood function. It is shown that many different models can be formulated within the framework of the autoregressive model and the interpretation of these models is discussed in detail. In particular, models involving restrictions on the cointegration vectors and the adjustment coefficients are discussed, as well as the role of the constant and linear drift.In Part II, the asymptotic theory is given the slightly more general framework of stationary linear processes with i.i.d. innovations. Some useful mathematical tools are collected in Appendix A, and a brief summary of weak convergence in given in Appendix B.The book is intended to give a relatively self-contained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. The asymptotic theory requires some familiarity with the theory of weak convergence of stochastic processes. The theory is treated in detail with the purpose of giving the reader a working knowledge of the techniques involved.Many exercises are provided. The theoretical analysis is illustrated with the empirical analysis of two sets of economic data. The theory has been developed in close contract with the application and the methods have been implemented in the computer package CATS in RATS as a result of a rcollaboation with Katarina Juselius and Henrik Hansen.

This book provides a self-contained account of periodic models for seasonally observed economic time series with stochastic trends. Two key concepts are periodic integration and periodic cointegration. Periodic integration implies that a seasonally varying differencing filter is required to remove a stochastic trend. Periodic cointegration amounts to allowing cointegration part-term adjustment parameters to vary with the season. The emphasis is on econometric models that explicitly describe seasonal variation and can reasonably be interpreted in terms of economic behaviour. The analysis considers econometric theory, Monte Carlo simulation, and forecasting, and it is illustrated with numerous empirical time series. A key feature of the proposed models is that changing seasonal fluctuations depend on the trend and business cycle fluctuations. In the case of such dependence, it is shown that seasonal adjustment leads to inappropriate results.

This book has become one of the main statistical tools for the analysis of economic and financial data. Designed for both theoreticians and practitioners, this book provides a comprehensive treatment of GMM estimation and inference. All the main statistical results are discussed intuitively and proved formally, and all the inference techniques are illustrated using empirical examples in macroeconomics and finance. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recent important developments in the field. About the SeriesAdvanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

Generalized Method of Moments (GMM) has become one of the main statistical tools for the analysis of economic and financial data. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recent important developments in the field. Providing a comprehensive treatment of GMM estimation and inference, it is designed as a resource for both the theory and practice of GMM: it discusses and proves formally all the main statistical results, and illustrates all inference techniques using empirical examples in macroeconomics and finance.Building from the instrumental variables estimator in static linear models, it presents the asymptotic statistical theory of GMM in nonlinear dynamic models. Within this framework it covers classical results on estimation and inference techniques, such as the overidentifying restrictions test and tests of structural stability, and reviews the finite sample performance of these inference methods. And it discusses in detail recent developments on covariance matrix estimation, the impact of model misspecification, moment selection, the use of the bootstrap, and weak instrument asymptotics.

This workbook is a companion to the textbook Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, also published by Oxford University Press. The workbook contains exercises and solutions concerned with the theory of cointegration in the vector autoregressive model. The main text has been used for courses on Cointegration, and many of the exercises have been posed as either training exercises or exam questions. Many of them are challenging and summarize results published in the literature. Each chapter starts with a brief summary of the content of the corresponding chapter in the main text, which introduces the notation and the most important results.

This workbook is a companion to the textbook Likelihood-Based Inference in Cointegrated Vector Autoregressive Models, also published by Oxford University Press. The workbook contains exercises and solutions concerned with the theory of cointegration in the vector autoregressive model. The main text has been used for courses on Cointegration, and many of the exercises have been posed as either training exercises or exam questions. Many of them are challenging and summarize results published in the literature. Each chapter starts with a brief summary of the content of the corresponding chapter in the main text, which introduces the notation and the most important results.

An insightful and up-to-date study of the use of periodic models in the description and forecasting of economic data. Incorporating recent developments in the field, the authors investigate such areas as seasonal time series; periodic time series models; periodic integration; and periodic cointegration. The analysis from the inclusion of many new empirical examples and results. Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

This book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness for out-of-sample forecasting. Providing an up-to-date survey of the recent developments in periodic time series, the book presents a large number of empirical results.The first part of the book deals with model selection, diagnostic checking and forecasting of univariate periodic autoregressive models. Tests for periodic integration, are discussed, and an extensive discussion of the role of deterministic regressors in testing for periodic integration and in forecasting is provided. The second part discusses multivariate periodic autoregressive models. It provides an overview of periodic cointegration models, as these are the most relevant. This overview contains single-equation type tests and a full-system approach based on generalized method of moments.All methods are illustrated with extensive examples, and the book will be of interest to advanced graduate students and researchers in econometrics, as well as practitioners looking for an understanding of how to approach seasonal data.

This book is intended for researchers and graduate students of econometrics.

This book, by one of the world's leading experts on dynamic panel data, presents a modern review of some of the main topics in panel data econometrics. The author concentrates on linear models, and emphasizes the roles of heterogeneity and dynamics in panel data modelling. The book combines methods and applications, so will appeal to both the academic and practitioner markets.The book is divided in four parts. Part I concerns static models, and deals with the problem of unobserved heterogeneity and how the availability of panel data helps to solve it, error component models, and error in variables in panel data. Part II looks at time series models with error components. Its chapters deal with the problem of distinguishing between unobserved heterogeneity and individual dynamics in short panels, modelling strategies of time effects, moving average models, inference from covariance structures, the specification and estimation of autoregressive models with heterogeneous intercepts, and the impact of assumptions about initial conditions and heteroskedacity on estimation.Part III examines dynamics and predeterminedness. Its two chapters consider alternative approaches to estimation from small and large T perspectives, looking at models with both strictly exogenous and lagged dependent variables allowing for autocorrelation of unknown form, models in which the errors are mean independent of current and lagged values of certain conditioning variables but not with their future values.Together Parts II and III provide a synthesis, and unified perspective, of a vast literature that has had a significant impact on recent econometric practice. Part IV reviews the main results in the theory of generalized method of moments estimation and optimal instrumental variables.

Macroeconometric models, in many ways the flagships of the economist's profession in the 1960s, came under increasing attack from both theoretical economist and practitioners in the late 1970s. Critics referred to their lack of microeconomic theoretical foundations, ad hoc models of expectations, lack of identification, neglect of dynamics and non-stationarity, and poor forecasting properties. By the start of the 1990s, the status of macroeconometric models had declined markedly, and had fallen completely out of, and with, academic economics. Nevertheless, unlike the dinosaurs to which they often have been likened, macroeconometric models have never completely disappeared from the scene.This book describes how and why the discipline of macroeconometric modelling continues to play a role for economic policymaking by adapting to changing demands, in response, for instance, to new policy regimes like inflation targeting. Model builders have adopted new insights from economic theory and taken advantage of the methodological and conceptual advances within time series econometrics over the last twenty years.The modelling of wages and prices takes a central part in the book as the authors interpret and evaluate the last forty years of international research experience in the light of the Norwegian 'main course' model of inflation in a small open economy. The preferred model is a dynamic model of incomplete competition, which is evaluated against alternatives as diverse as the Phillips curve, Nickell-Layard wage curves, the New Keynesian Phillips curve, and monetary inflation models on data from the Euro area, the UK, and Norway.The wage price core model is built into a small econometric model for Norway to analyse the transmission mechanism and to evaluate monetary policy rules. The final chapter explores the main sources of forecast failure likely to occur in a practical modelling situation, using the large-scale nodel RIMINI and the inflation models of earlier chapters as case studies.

Macroeconometric models, in many ways the flagships of the economist's profession in the 1960s, came under increasing attack from both theoretical economist and practitioners in the late 1970s. Critics referred to their lack of microeconomic theoretical foundations, ad hoc models of expectations, lack of identification, neglect of dynamics and non-stationarity, and poor forecasting properties. By the start of the 1990s, the status of macroeconometric models had declined markedly, and had fallen completely out of, and with, academic economics. Nevertheless, unlike the dinosaurs to which they often have been likened, macroeconometric models have never completely disappeared from the scene. This book describes how and why the discipline of macroeconometric modelling continues to play a role for economic policymaking by adapting to changing demands, in response, for instance, to new policy regimes like inflation targeting. Model builders have adopted new insights from economic theory and taken advantage of the methodological and conceptual advances within time series econometrics over the last twenty years. The modelling of wages and prices takes a central part in the book as the authors interpret and evaluate the last forty years of international research experience in the light of the Norwegian 'main course' model of inflation in a small open economy. The preferred model is a dynamic model of incomplete competition, which is evaluated against alternatives as diverse as the Phillips curve, Nickell-Layard wage curves, the New Keynesian Phillips curve, and monetary inflation models on data from the Euro area, the UK, and Norway. The wage price core model is built into a small econometric model for Norway to analyse the transmission mechanism and to evaluate monetary policy rules. The final chapter explores the main sources of forecast failure likely to occur in a practical modelling situation, using the large-scale nodel RIMINI and the inflation models of earlier chapters as case studies.

Neil Shephard has brought together a set of classic and central papers that have contributed to our understanding of financial volatility. They cover stocks, bonds and currencies and range from 1973 up to 2001. Shephard, a leading researcher in the field, provides a substantial introduction in which he discusses all major issues involved.

Long memory processes constitute a broad class of models for stationary and nonstationary time series data in economics, finance, and other fields. Their key feature is persistence, with high correlation between events that are remote in time. A single 'memory' parameter economically indexes this persistence, as part of a rich parametric or nonparametric structure for the process. Unit root processes can be covered, along with processes that are stationary but with stronger persistence than autoregressive moving averages, these latter being included in a broader class which describes both short memory and negative memory. Long memory processes have in recent years attracted considerable interest from both theoretical and empirical researchers in time series and econometrics. This book of readings collects articles on a variety of topics in long memory time series including modelling and statistical inference for stationary processes, stochastic volatility models, nonstationary processes, and regression and fractional cointegration models.Some of the articles are highly theoretical, others contain a mix of theory and methods, and an effort has been made to include empirical applications of the main approaches covered. A review article introduces the other articles but also attempts a broader survey, traces the history of the subject, and includes a bibliography.

In many disciplines of science it is vital to know the effect of a 'treatment' on a response variable of interest; the effect being known as the 'treatment effect'. Here, the treatment can be a drug, an education program or an economic policy, and the response variable can be an illness, academic achievement or GDP. Once the effect is found, it is possible to intervene to adjust the treatment and attain a desired level of the response variable.A basic way to measure the treatment effect is to compare two groups, one of which received the treatment and the other did not. If the two groups are homogenous in all aspects other than their treatment status, then the difference between their response outcomes is the desired treatment effect. But if they differ in some aspects in addition to the treatment status, the difference in the response outcomes may be due to the combined influence of more than one factor. In non-experimental data where the treatment is not randomly assigned but self-selected, the subjects tend to differ in observed or unobserved characteristics. It is therefore imperative that the comparison be carried out with subjects similar in their characteristics. This book explains how this problem can be overcome so the attributable effect of the treatment can be found.This book brings to the fore recent advances in econometrics for treatment effects. The purpose of this book is to put together various economic treatments effect models in a coherent fashion, make it clear which can be parameters of interest, and show how they can be identified and estimated under weak assumptions. The emphasis throughout the book is on semi- and non-parametric estimation methods, but traditional parametric approaches are also discussed. This book is ideally suited to researchers and graduate students with a basic knowledge of econometrics.