Causality in Macroeconomics examines causality while taking macroeconomics seriously. A pragmatic and realistic philosophy is joined to a macroeconomic foundation that refines Herbert Simon's well-known work on causal order to make a case for a structural approach to causality. The structural approach is used to understand modern rational expectations models, regime switching models, Granger causality, vector autoregressions, the Lucas critique, and concept exogeneity. Techniques of causal inference based on patterns of stability and instability in the face of identified regime changes (...) are developed and illustrated in two empirical case studies. (shrink)

Elliot Sober ([2001]) forcefully restates his well-known counterexample to Reichenbach's principle of the common cause: bread prices in Britain and sea levels in Venice both rise over time and are, therefore, correlated; yet they are ex hypothesi not causally connected, which violates the principle of the common cause. The counterexample employs nonstationary data—i.e., data with time-dependent population moments. Common measures of statistical association do not generally reflect probabilistic dependence among nonstationary data. I demonstrate the inadequacy of the counterexample and of (...) some previous responses to it, as well as illustrating more appropriate measures of probabilistic dependence in the nonstationary case. A challenge to the principle of the common causeSober's argument and the attempts to rescue the principleProbabilistic dependenceNonstationary time seriesProbabilistic dependence in nonstationary time seriesDo Venetian sea levels and British bread prices violate the principle of the common cause? (shrink)

Macroeconomists overwhelmingly believe that macroeconomics requires microfoundations, typically understood as a strong eliminativist reductionism. Microfoundations aims to recover intentionality. In the face of technical and data constraints macroeconomists typically employ a representative-agent model, in which a single agent solves the microeconomic optimization problem for the whole economy, and take it to be microfoundationally adequate. The characteristic argument for the representative-agent model holds that the possibility of the sequential elaboration of the model to cover any number of individual agents justifies treating (...) the policy conclusions of the single-agent model as practically relevant. This eschatological justification is examined and rejected. (shrink)

The Methodology of Empirical Macroeconomics stakes out a pragmatic middle-ground between traditional, prescriptive economic methodology and recent descriptive methodology. The former is sometimes seen as arrogantly telling economists how to do their work and the latter as irrelevant to their practice. The lectures are built around a case study of a concrete example of macroeconomic analysis. They demonstrate that economic methodology and the philosophy of science offer insights that help to resolve the genuine concerns of macroeconomists. Some examples of questions (...) addressed include: What is the relationship between theoretical models and empirical observations? What is the relevance of macroeconomics to policy? Should macroeconomics be viewed as a special case of microeconomics? What is the place of long-standing philosophical issues in macroeconomics, such as the scope and nature of economic laws, the role of idealizations, methodological individualism, and the problem of causality? (shrink)

Argues that ontological reduction of macroeconomics to microeconomics is untenable. Existence of macroeconomic aggregates; Microfoundations of macroeconomics; Examinations of the general price level; Limits of the scientific development of microeconomics.

Discontented people might talk of corruption in the Commons, closeness in the Commons and the necessity of reforming the Commons, said Mr. Spenlow solemnly, in conclusion; but when the price of wheat per bushel had been the highest, the Commons had been the busiest; and a man might lay his hand upon his heart, and say this to the whole world, – ‘Touch the Commons, and down comes the country!’.

Recent debates over the nature of causation, casual inference, and the uses of causal models in counterfactual analysis, involving inter alia Nancy Cartwright (Hunting Causes and Using Them), James Woodward (Making Things Happen), and Judea Pearl (Causation), hinge on how causality is represented in models. Economists’ indigenous approach to causal representation goes back to the work of Herbert Simon with the Cowles Commission in the early 1950s. The paper explicates a scheme for the representation of causal structure, inspired by Simon, (...) and shows how this representation sheds light on some important debates in the philosophy of causation. This structural account is compared to Woodward’s manipulability account. It is used to evaluate the recent debates – particularly, with respect to the nature of causal structure, the identity of causes, causal independence, and modularity. Special attention is given to modeling issues that arise in empirical economics. (shrink)

Economics prefers complete explanations: general over partial equilibrium, microfoundational over aggregate. Similarly, probabilistic accounts of causation frequently prefer greater detail to less as in typical resolutions of Simpson’s paradox. Strategies of causal refinement equally aim to distinguish direct from indirect causes. Yet, there are countervailing practices in economics. Representative-agent models aim to capture economic motivation but not to reduce the level of aggregation. Small structural vector-autoregression and dynamic stochastic general-equilibrium models are practically preferred to larger ones. The distinction between exogenous (...) and endogenous variables suggests partitioning the world into distinct subsystems. The tension in these practices is addressed within a structural account of causation inspired by the work of Herbert Simon’s, which defines cause with reference to complete systems adapted to deal with incomplete systems and piecemeal evidence. The focus is on understanding the constraints that a structural account of causation places on the freedom to model complex or lower-order systems as simpler or higher-order systems and on to what degree piecemeal evidence can be incorporated into a structural account. (shrink)

In the eyes of its practitioners, economics is both a deductive science and an empirical science. The starting point of its deductions might be thought of as first principles. But what is the status of such principles? The tension between foundationalism, the idea that there are necessary and secure first principles for economic inquiry, and fallibilism, the idea that no belief can be certified as true beyond the possibility of doubt, is explored. Empirical disciplines require some sort of falsifiability. Yet, (...) empirical inquiries also require a starting place—if not a necessarily true one, at least an indubitable one, that is, one that is not actually doubted. Indubitability appears to have necessary consequences, undercutting fallibilism, while fallibilism threatens confidence in the de facto first principles that begin inquiry. This tension is examined in three well-known attempts to define economics and its method: John Stuart Mill’s economics as the science of wealth, Lionel Robbins’s economics as constrained optimization; and George Stigler and Becker’s attempt to reformulate neoclassical economics to square empiricism with Robbins’ deductivism. (shrink)

The difficulty of conducting relevant experiments has long been regarded as the central challenge to learning about the economy from data. The standard solution, going back to Haavelmo's famous “The Probability Approach in Econometrics” (1944), involved two elements: first, it placed substantial weight on a priori theory as a source of structural information, reducing econometric estimates to measurements of causally articulated systems; second, it emphasized the need for an appropriate statistical model of the data. These elements are usually seen as (...) tightly linked. I argue that they are, to a large extent, separable. Careful attention to the role of an empirically justified statistical model in underwriting probability explains puzzles not only in economics, but more generally with respect to recent criticisms of Reichenbach's principle of the common cause, which lies behind graph-theoretic causal search algorithms. And it provides an antidote to the pessimistic understanding of the possibilities for passive observation of causal structure in econometrics and related areas of Nancy Cartwright and others. (shrink)

Modern empirical macroeconomic models, known as structural autoregressions (SVARs) are dynamic models that typically claim to represent a causal order among contemporaneously valued variables and to merely represent non-structural (reduced-form) co-occurence between lagged variables and contemporaneous variables. The strategy is held to meet the minimal requirements for identifying the residual errors in particular equations in the model with independent, though otherwise not directly observable, exogenous causes (“shocks”) that ultimately account for change in the model. In nonstationary models, such shocks accumulate (...) so that variables have discernible trends. Econometricians have conceived of variables that trend in sympathy with each other (so-called “cointegrated variables”) as sharing one or more of these unobserved trends as a common cause. It is possible for estimates of the values of both the otherwise unobservable individual shocks and the otherwise unobservable common trends to be backed-out of cointegrated systems of equations. The issue addressed in this paper is whether and in what circumstances these values can be regarded as observations of real entities rather than merely artifacts of the representation of variables in the model. The issue is related, on the one hand, to practical methodological problems in the use of SVARs for policy analysis—e.g., does it make sense to estimate of shocks or trends in one model and then use them as measures of variables in a conceptually distinct model? The issue is also related to debates in the philosophical analysis of causation—particularly, whether we are entitled, as assumed by the developers of Bayes-net approaches, to rely on the causal Markov condition (a generalization of Reichenbach’s common-cause condition) or whether cointegration generates a practical example of Nancy Cartwright’s “byproducts” objection to the causal Markov condition. (shrink)

Although the relevance and importance of his work has been recognized only belatedly, Charles Sanders Peirce was, throughout his life, a careful student and significant contributor to the development of logic, scientific theory, and philosophy generally. Occasionally, complete appreciation of Peirce's efforts has been hampered because his work is often unique and, at times, highly idiosyncratic. Yet, we hope to show in this paper that for one aspect of his work in logic Peirce did not abandon the ordinary without purpose. (...) Only relatively recently have philosophers of science become interested in the logic of discovery - that is, in the logic of the selection of hypotheses to be tested rather than simply in the ways of testing hypotheses which have already been selected. (shrink)

This paper addresses the role of specification tests in the selection of a statistically admissible model used to evaluate economic hypotheses. The issue is formulated in the context of recent philosophical accounts on the nature of models and related to some results in the literature on specification search. In contrast to enumerative induction and a priori theory, powerful search methodologies are often adequate substitutes for experimental methods. They underwrite and support, rather than distort, statistical hypothesis tests. Their success is grounded (...) in a systematic effort to mold appropriate models to, and test them against, constraints of the data. (shrink)

(2013). Beyond mechanical markets – asset price swings, risk and the role of the state. Journal of Economic Methodology: Vol. 20, Methodology, Systemic Risk, and the Economics Profession, pp. 69-75. doi: 10.1080/1350178X.2013.774856.

Through most of the history of economics, the most influential commentators on methodology were also eminent practitioners of economics. And even not so long ago, it was so. Milton Friedman, Paul Samuelson, Trygve Haavelmo, and Tjalling Koopmans were awarded Nobel prizes for their substantive contributions to economics, and were each important contributors to methodological thought. But the fashion has changed. Specialization has increased. Not only has methodology become its own field, but many practitioners have come to agree with Frank Hahn's (...) (1992) view that methodology is a distraction to the practitioner, best left to the professional methodologists and philosophers, and of little practical import even when delivered from their pens. John Sutton's lectures, Marshall's Tendencies: What Economists Can Know, is a welcome return to the older fashion, for Sutton is an eminent practitioner of game theory and industrial organization. One of the main themes of these rich and nuanced lectures is the relationship of economic theory to econometric evidence. Sutton's reflections on econometrics appear to arise from the darker recesses of his practitioner's soul. While he affects a sunny disposition and ends on a hopeful note, his analysis articulates the lurking fear that econometrics is a hopeless project and that economics has little to learn from the interaction of theory and econometrics. Sutton's book is like a play in which virtue triumphs, but the villain gets all the good lines. (shrink)

Critics argued that the 2007–09 financial crisis was failure of macroeconomics, locating its source in the dynamic, stochastic general-equilibrium model and calling for fundamental re-orientation of the field. Critics exaggerated the role of DSGE models in actual policymaking, and DSGE modelers addressed some criticisms within the DSGE framework. But DSGE modelers oversold their success and even claimed that their approach is the sine qua non of competent macroeconomics. The DSGE modelers and their critics renew an old debate over the relative (...) priority of a priori theory and empirical data, classically exemplified in the Measurement without Theory Debate of the 1940s between the Cowles Commission and the National Bureau of Economic Research. The earlier debate is reviewed for its implications for the recent controversy. In adopting the Cowles-Commission position, some DSGE modelers would essentially straight-jacket macroeconomics and undermine economic science and the pursuit of knowledge in an open-minded, yet critical framework. (shrink)

The dominant view among macroeconomists is that macroeconomics reduces to microeconomics, both in the sense that all macroeconomic phenomena arise out of microeconomic phenomena and in the sense that macroeconomic theory—to the extent that it is correct—can be derived from microeconomic theory. More than that, the dominant view believes that macroeconomics should in practice use the reduced microeconomic theory: this is the program of microfoundations for macroeconomics to which the vast majority of macroeconomists adhere. The "microfoundational" models that they actually (...) employ are, however, characterized by another feature: they are highly idealized, even when they are applied as direct characterizations of actual data, which itself consists of macroeconomic aggregates. This paper explores the interrelationship between reductionism and idealization in the microfoundational program and the role of idealization in empirical modeling. (shrink)