Economics and Finance

This book contains an up-to-date coverage of the last twenty years of 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 considers the econometric analysis of both stationary and non‐stationary processes, which may be linked by equilibrium relationships. It provides a wide‐ranging account of the main tools, techniques, models, concepts, and distributions involved in the modelling of integrated processes (i.e. those that accumulate the effects of past shocks). Since the focus is on equilibrium concepts, including co‐integration and error‐correction, the analysis begins with a discussion of the application of these concepts to stationary empirical models. Later chapters show how integrated processes can be reduced to this case by suitable transformations that take advantage of co‐integrating (equilibrium) relationships. The concepts of co‐integration and error‐correction models are shown to be fundamental in this modelling strategy. Practical modelling advice and empirical illustrations are provided.

Knowledge of econometrics, statistics, and matrix algebra at the level of a final‐year undergraduate or first‐year graduate course in econometrics is sufficient for most of the book. Other mathematical tools are described as they arise.

One of the most important, and visible, things economists do is to forecast what will happen in the economy in the future. Each year, a number of different groups in the United States use their own econometric models to forecast what will happen to the economy in the coming year. Some economic forecasts are more accurate than others. This book consists of chapters comparing the different models now being used. It is organized topically rather than by model. The contributors include: Roger Brimmer, Ray Fair, Bert Hickman, F. Gerard Adams, and Albert Ando. The editor provides an introduction to the volume.

This book provides a presentation of the standard statistical techniques used by labour economists. It emphasizes both the input and the output of empirical analysis and covers five major topics concerning econometric methods used in labour economics: regression and related methods, choice modelling, selectivity issues, duration analysis, and policy evaluation techniques. Each of these is presented in terms of model specification, possible estimation problems, diagnostic checking, and interpretation of the output. The book aims to provide guidance to practitioners on how to use the techniques and how to make sense of the results that are produced. It covers methods that are considered to be ‘standard’ tools in labour economics, but which are often given only a brief and highly technical treatment in econometrics textbooks.

This collection of published papers records the development of an approach to econometric modelling that has reached a highly successful stage. The methodology of modelling ‘observational data’, as opposed to experimental data, which can be replicated, is analysed to highlight the fundamental flaws in various approaches, and the possibilities of others. Criteria for model adequacy are formulated (congruence and encompassing), and alternative approaches to building empirical models are compared on their ability to deliver such models. A typology of models elucidates their properties, and a taxonomy of information sources clarifies testing. Estimation is summarized by an estimator generating equation. The value of exploring the development path is to reveal by attempted applications why many widely used approaches are inadequate. The outcome is to demonstrate the viability of a general‐to‐specific approach that commences from a specification deemed more than adequate to characterize the evidence, and simplifies to a parsimonious representation that captures the main factors. By artificial Monte Carlo simulations on experiments designed by others, the success of that approach is established, leading to automatic model selection by software that can outperform practitioners.

This book presents a comprehensive and unified treatment of finite sample theory, and its application to estimators and test statistics used in various econometric models. Time series, cross section, and panel data models are considered. The results are explored for linear and nonlinear models, as well as models with normal and nonnormal errors. The book contains seven chapters. Chapter 1 presents an introduction to finite sample econometrics. Chapter 2 gives methods of obtaining the moments of econometric statistics. Chapter 3 provides methods for analysing distributions. Finite sample results for various econometric models are discussed in Chapters 4-7.

This book provides a comprehensive description of the state-of-the-art in macroeconometric modelling and describes the ‘long-run structural modelling approach’ applied to the modelling of national economies in a global context. The first part of the book discusses the ways in which economic theory and econometric analysis can be brought together to construct a macroeconometric model, in which the long-run relationships are consistent with economic theory and where the short-run dynamics have an interpretation. The discussion considers theoretical as well as practical considerations involved in the model building process, and gives an overview of the econometric methods covering cointegrating VAR analysis and probability forecasting. The second part of the book is devoted to the practical detail of estimating a long-run structural macroeconometric model and is illustrated through various global and national examples, including a step-by-step description of the development of a model of the UK economy. The third part discusses the interpretation and use of long-run structural macroeconometric models, describing the use of the UK model along with illustrations of the modelling approach in investigating regional interdependencies in a global macroeconometric model and other specified issues in a global or national macroeconometric context. Throughout, the book emphasizes the use of macroeconometric modelling in the real world and provides sufficient detail, including discussion of data collection and computer programmes employed, for the techniques that are introduced to be replicated or applied in new contexts.