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

Edited collection examining the ways in which models are used in modern science.

Fascinating and authoritative, this work, challenges the traditional view of the history of econometrics and offers a comprehensive overview of what went on to be one of the defining subsets within the economics profession.

Collection of classic papers by pioneer econometricians

Haavelmo’s 1944 monograph, The Probability Approach in Econometrics, is widely acclaimed as the manifesto of econometrics. This book challenges Haavelmo’s probability approach, shows how its use is delivering defective and inefficient results, and argues for a paradigm shift in econometrics towards a full embrace of machine learning, with its attendant benefits. Machine learning has only come into existence over recent decades, whereas the universally accepted and current form of econometrics has developed over the past century. A comparison between the two is, however, striking. The practical achievements of machine learning significantly outshine those of econometrics, confirming the presence of widespread inefficiencies in current econometric research. The relative efficiency of machine learning is based on its theoretical foundation, and particularly on the notion of Probably Approximately Correct (PAC) learning. Careful examination reveals that PAC learning theory delivers the goals of applied economic modelling research far better than Haavelmo’s probability approach. Econometrics should therefore renounce its outdated foundation, and rebuild itself upon PAC learning theory so as to unleash its pent-up research potential. The book is catered for applied economists, econometricians, economists specialising in the history and methodology of economics, advanced students, philosophers of social sciences.

Our narratives of postwar Japan have long been cast in terms almost synonymous with the story of rapid economic growth. Scott O’Bryan reinterprets this seemingly familiar history through an innovative exploration, not of the anatomy of growth itself, but of the history of growth as a set of discourses by which Japanese "growth performance" as "economic miracle" came to be articulated. The premise of his work is simple: To our understandings of the material changes that took place in Japan during the second half of the twentieth century we must also add perspectives that account for growth as a new idea around the world, one that emerged alongside rapid economic expansion in postwar Japan and underwrote the modes by which it was imagined, forecast, pursued, and regulated. In an accessible, lively style, O’Bryan traces the history of growth as an object of social scientific knowledge and as a new analytical paradigm that came to govern the terms by which Japanese understood their national purposes and imagined a newly materialist vision of social and individual prosperity. Several intersecting obsessions worked together after the war to create an agenda of social reform through rapid macroeconomic increase. Epistemological developments within social science provided the conceptual instruments by which technocrats gave birth to a shared lexicon of growth. Meanwhile, reformers combined prewar Marxist critiques with new modes of macroeconomic understanding to mobilize long-standing fears of overpopulation and "backwardness" and argue for a growthist vision of national reformation. O’Bryan also presents surprising accounts of the key role played by the ideal of full employment in national conceptions of recovery and of a new valorization of consumption in the postwar world that was taking shape. Both of these, he argues, formed critical components in a constellation of ideas that even in the context of relative poverty and uncertainty coalesced into a powerful vision of a materially prosperous future. Even as Japan became the premier icon of the growthist ideal, neither the faith in rapid growth as a prescription for national reform nor the ascendancy of social scientific epistemologies that provided its technical support was unique to Japanese experience. The Growth Idea thus helps to historicize a concept of never-ending growth that continues to undergird our most basic beliefs about the success of nations and the operations of the global economy. It is a particularly timely contribution given current imperatives to reconceive ideas of purpose and prosperity in an age of resource depletion and global warming.

The economic crisis of 2008 led to an unprecedented focus on the world of high finance—and revealed it to be far more arcane and influential than most people could ever have imagined. Any hope of avoiding future crises, it’s clear, rest on understanding finance itself. To understand finance, however, we have to learn its history, and this book fills that need. Kevin R. Brine, an industry veteran, and Mary Poovey, an acclaimed historian, show that finance as we know it today emerged gradually in the late nineteenth century and only coalesced after World War II, becoming ever more complicated—and ever more central to the American economy. The authors explain the models, regulations, and institutions at the heart of modern finance and uncover the complex and sometimes surprising origins of its critical features, such as corporate accounting standards, the Federal Reserve System, risk management practices, and American Keynesian and New Classic monetary economics. This book sees finance through its highs and lows, from pre-Depression to post-Recession, exploring the myriad ways in which the practices of finance and the realities of the economy influenced one another through the years. A masterwork of collaboration, Finance in America lays bare the theories and practices that constitute finance, opening up the discussion of its role and risks to a broad range of scholars and citizens.

This book contains a set of notes prepared by Ragnar Frisch for a lecture series that he delivered at Yale University in 1930., complete with an introdutory essay from Olav Bjerkholt and Duo Qin placing the notes in their historical context.

Econometric Business Cycle Research deals with econometric business cycle research (EBCR), a term introduced by the Nobel-laureate Jan Tinbergen for his econometric method of testing (economic) business cycle theories. EBCR combines economic theory and measurement in the study of business cycles, i.e., ups and downs in overall economic activity. We assess four methods of EBCR: business cycle indicators, simultaneous equations models, vector autoregressive systems and real business indicators. After a sketch of the history of the methods, we investigate whether the methods meet the goals of EBCR: the three traditional ones, description, forecasting and policy evaluation, and the one Tinbergen introduced, the implementation|testing of business cycles. The first three EBCR methods are illustrated for the Netherlands, a typical example of a small, open economy. The main conclusion of the book is that simultaneous equation models are the best vehicle for EBCR, if all its goals are to be attained simultaneously. This conclusion is based on a fairly detailed assessment of the methods and is not over-turned in the empirical illustrations. The main conclusion does not imply the end of other EBCR methods. Not all goals have to be met with a single vehicle, other methods might serve the purpose equally well - or even better. For example, if one is interested in business cycle forecasts, one might prefer a business cycle indicator or vector autoregressive system. A second conclusion is that many ideas/concepts that play an important role in current discussions about econometric methodology in general and EBCR in particular, were put forward in the 1930s and 1940s. A third conclusion is that it is difficult, if not impossible, to compare the outcomes of RBC models to outcomes of the other three methods, because RBC modellers are not interested in modelling business cycles on an observation-per-observation basis. A more general conclusion in this respect is that methods should adopt the same concept of business cycles to make them comparable.