Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Criticism and the Growth of Knowledge. Edited by I. Lakatos and A. Musgrave. Pp. viii, 282. £3·50, paperback £1. 1970. (Cambridge University Press.)

Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

Citation classic - optimal foraging - a selective review of theory and tests

Knowledge Management

Econometric policy evaluation: A critique

In this article we critically examine the use of Big Data in the legal system. Big Data is driving a trend towards behavioral optimization and “personalized law,” in which legal decisions and rules are optimized for best outcomes and where law is tailored to individual consumers based on analysis of past data. Big Data, however, has serious limitations and dangers when applied in the legal context. Advocates of Big Data make theoretically problematic assumptions about the objectivity of data and scientific observation. Law is always theory-laden. Although Big Data strives to be objective, law and data have multiple possible meanings and uses and thus require theory and interpretation in order to be applied. Further, the meanings and uses of law and data are indefinite and continually evolving in ways that cannot be captured or predicted by Big Data.

Due to these limitations, the use of Big Data will likely generate unintended consequences in the legal system. Large-scale use of Big Data will create distortions that adversely influence legal decision-making, causing irrational herding behaviors in the law. The centralized nature of the collection and application of Big Data also poses serious threats to legal evolution and democratic accountability. Furthermore, its focus on behavioral optimization necessarily restricts and even eliminates the local variation and heterogeneity that makes the legal system adaptive. In all, though Big Data has legitimate uses, this article cautions against using Big Data to replace independent legal judgment.

The law and Big Data

Ludwig M Lachmann was born in Berlin in 1906 and died in Johannesburg in 1990 For more than forty years, until his retirement in 1972, Lachmann established himself as a prominent South African economist and for a time served as head of the economics department at the University of Witwatersrand From 1974 to 1987, he worked with Professor Israel Kirzner in New York City to give new shape and life to the older Austrian school of economics Lachmann influenced a small army of modern Austrians to discard the elaborate formalisms of orthodox economics for a “radical subjectivism” that had its roots in the teachings of the founder of the Austrian school, Carl Menger Here a small platoon of scholars offer their thoughts about Lachmann, his contributions to economic reasoning, and his eccentric but engaging character First hand reports explain what their mentor taught and what his students took away Lavoie makes the case that Lachmann’s “radical subjectivism” took a rhetorical turn toward the end of Lachmann’s career in New York City In addition, Kirzner reports on his long and most productive relationship with Lachmann and provides additional insights about the seminal role of the Austrian Economics Seminar at New York University from 1985 to 1987 in giving shape to the modern Austrian revival This article is the written version of a “Remembrance and Appreciation Session” held on June 28, 1999 at the History of Economics Society meeting at the University of North Carolina in Greensboro It is one of an ongoing series that appears in the July issues of this journal

Professor Ludwig M. Lachmann (1906-1990): Scholar, Teacher, and Austrian School Critic of Late Classical Formalism in Economics

Complexity and Austrian Economics

As defined by the editors of this book, ‘non-market entrepreneurship’ consists of all forms of entrepreneurship not being undertaken solely for purposes of profit maximization or commercialization, and encompasses entrepreneurial activities such as social enterprise and entrepreneurship, public sector entrepreneurship, policy entrepreneurship, non-profit entrepreneurship, and philanthropic enterprise, among many others. The eminent cast of contributors gives coherence to the academic and public discussions on the topic, builds a theoretical edifice within the field of entrepreneurship and helps to establish and delineate the contours of the research field of non-market entrepreneurship.

Entrepreneurship and human action

The Great Recession seems to be creating a change in the trend of macroeconomic thinking. Prior to the financial crisis of 2008, dynamic stochastic general equilibrium (DSGE) models dominated the macroeconomics literature without any apparent challengers on the horizon. Since then, however, we have seen an increasing interest in macroeconomic models that address the state of confidence (“animal spirits”), complexity, cognition, and radical uncertainty. Most of the renewed interest in animal spirits, complexity, cognition, and radical uncertainty has come from a more or less “Keynesian” perspective. We discuss the potential to emphasize these elements from a more “Hayekian” perspective and argue that Austrian approaches to macroeconomics along these lines are more likely to resonate with mainstream economists than in years past.

Roger Koppl

Papers

The law and Big Data

Professor Ludwig M. Lachmann (1906-1990): Scholar, Teacher, and Austrian School Critic of Late Classical Formalism in Economics

Complexity and Austrian Economics

Entrepreneurship and human action

Hayek, Keynes, and modern macroeconomics