Written from the Haavelmo-Cowles Commission econometric perspective, this book provides an account of the advances in the field of econometrics since the 1970s.

This book illustrates how economists first learnt to harness statistical methods to measure and test the 'laws' of economics.

How are economists and historians to explain what happened in history? What statistical inferences can be drawn from historical data? The authors believe that explanation in history can be identified with the problems of prediction in a probabilistic universe. Using this approach, the historian can act upon his a priori information and his judgment of what is unique and particular in each past event, even with data hitherto considered to be intractable for statistical treatment. In essence, the book is an argument for and a demonstration of the point of view that the restricted approach of "measurement without theory" is not necessary in history, or at least not necessary in economic history. After two chapters of theoretical introduction, the authors explore the meanings and implications of evidence, explanation and proof in history by applying econometric methods to the analysis of three major problems in 19th century economic history--the profitability of slavery in the antebellum South, income growth and development in the United States during the 1800's, and The Great Depression in the British economy; also included is a postscript on growth reassessing some current arguments in the light of the findings of these papers. The book presents an original and provocative approach to historical problems that have long plagued economists and historians and provides the reader with a new approach to these and similar questions.

The Handbook of Historical Economics guides students and researchers through a quantitative economic history that uses fully up-to-date econometric methods. The book's coverage of statistics applied to the social sciences makes it invaluable to a broad readership. As new sources and applications of data in every economic field are enabling economists to ask and answer new fundamental questions, this book presents an up-to-date reference on the topics at hand. Provides an historical outline of the two cliometric revolutions, highlighting the similarities and the differences between the two Surveys the issues and principal results of the "second cliometric revolution" Explores innovations in formulating hypotheses and statistical testing, relating them to wider trends in data-driven, empirical economics

This comparative historical study of econometrics focuses on the development of econometric methods and their application to macroeconomics.The analysis covers the origins of modern econometrics in the USA and Europe during the 1920's and 30's, the rise of `structural estimation' in the 1940's and 50's as the dominant research paradigm, and the crisis of the large macroeconomic models in the 1970's and 80's.The completely original feature of this work is the use of previously unknown manuscript material from the archives of the Cowles Commission and other collections. The history so constructed shows that recent debates over methodology are incomplete without understanding the many deep criticisms that were first raised by the earliest researchers in the field.

Without economic history, economics runs the risk of being too abstract or parochial, of failing to notice precedents, trends and cycles, of overlooking the long-run and thus misunderstanding ‘how we got here’. Recent financial and economic crises illustrate spectacularly how the economics profession has not learnt from its past. This important and unique book addresses this problem by demonstrating the power of historical thinking in economic research. Concise chapters guide economics lecturers and their students through the field of economic history, demonstrating the use of historical thinking in economic research, and advising them on how they can actively engage with economic history in their teaching and learning. Blum and Colvin bring together important voices in the field to show readers how they can use their existing economics training to explore different facets of economic history. Each chapter introduces a question or topic, historical context or research method and explores how they can be used in economics scholarship and pedagogy. In a century characterised to date by economic uncertainty, bubbles and crashes, An Economist’s Guide to Economic History is essential reading. For further information visit http://www.blumandcolvin.org

Exploring the characteristics of inflations and comparing historical cases from Roman times up to the modern day, this book provides an in depth discussion of the subject. It analyses the high and moderate inflations caused by the inflationary bias of

‘Big data’ is now readily available to economic historians, thanks to the digitisation of primary sources, collaborative research linking different data sets, and the publication of databases on the internet. Key economic indicators, such as the consumer price index, can be tracked over long periods, and qualitative information, such as land use, can be converted to a quantitative form. In order to fully exploit these innovations it is necessary to use sophisticated statistical techniques to reveal the patterns hidden in datasets, and this book shows how this can be done. A distinguished group of economic historians have teamed up with younger researchers to pilot the application of new techniques to ‘big data’. Topics addressed in this volume include prices and the standard of living, money supply, credit markets, land values and land use, transport, technological innovation, and business networks. The research spans the medieval, early modern and modern periods. Research methods include simultaneous equation systems, stochastic trends and discrete choice modelling. This book is essential reading for doctoral and post-doctoral researchers in business, economic and social history. The case studies will also appeal to historical geographers and applied econometricians.

The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.