This paper addresses the role of tests of statistical hypotheses (specification tests) in selection of a statistically admissible model in which 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.
The price puzzle, an increase in the price level associated with a contractionary monetary shock, is investigated in a rich, 12-variable SVAR in which various factors that have been mooted as solutions are considered jointly. SVARs for the pre-1980 and post-1990 periods are identified empirically using a graph-theoretic causal search algorithm combined with formal tests of the implied overidentifying restrictions. In this SVAR, the pre-1980 price puzzle depends on the characterization of monetary policy, and the post-1990 price puzzle is statistically insignificant. Commonly suggested theoretical resolutions to the price puzzle are shown to have causal implications inconsistent with the data.
Recent debates over the nature 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.
Trygve Haavelmo’s The Probability Approach in Econometrics (1944) has been widely regarded as the foundation document of modern econometrics. Nevertheless, its significance has been interpreted in widely different ways. Some modern economists regard it as a blueprint for a provocative, but ultimately unsuccessful, program dominated by the need for a priori theoretical identification of econometric models. They call for new techniques that better acknowledge the interrelationship of theory and data. Others credit Haavelmo with an approach that focuses on statistical adequacy rather than theoretical identification. They see many of Haavelmo’s deepest insights as having been unduly neglected. The current paper uses bibliometric techniques and a close reading of econometrics articles and textbooks to trace the way in which the economics profession received, interpreted, and transmitted Haavelmo’s ideas. A key irony is that the first group calls for a reform of econometric thinking that goes several steps beyond Haavelmo’s initial vision; while the second group argues that essentially what the first group advocates was already in Haavelmo’s Probability Approach from the beginning.
A careful, analytical account of Trygve Haavelmo's use of the analogy between controlled experiments common in the natural sciences and econometric techniques. The experimental analogy forms the linchpin of the methodology for passive observation that he develops in his famous monograph, The Probability Approach in Econometrics (1944). Contrary to some recent interpretations of Haavelmo’s method, the experimental analogy does not commit Haavelmo to a strong, apriorism in which econometrics can only test and reject theoretical hypotheses. Rather it supports the acquisition of knowledge through a two-way exchange between theory and empirical evidence. Once the details of the analogy are systematically understood, the experimental analogy can be used to shed light on theory-consistent cointegrated vector autoregression (CVAR) scenario analyses. A CVAR scenario analysis can be seen as a clear example of Haavelmo's 'experimental' approach; and, in turn, it can be shown to extend and develop Haavelmo's methodology and to address issues that Haavelmo regarded as unresolved.