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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. [ABSTRACT FROM AUTHOR]

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1468-0084.2007.00496.x Byline: Selva Demiralp ([dagger]), Kevin D. Hoover ([double dagger]), Stephen J. Perez (s.) Keywords: C30; C32; C51 Abstract: Abstract Graph-theoretic methods of causal search based on the ideas of Pearl (2000), Spirtes et al. (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data-based contemporaneous causal order for the structural vector autoregression, rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, as the causal structure of the true data-generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e. without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph-theoretic search algorithms. Author Affiliation: ([dagger])Department of Economics, Koc University, Sariyer, Istanbul 34450, Turkey (e-mail:sdemiralp@ku.edu.tr) ([double dagger])Department of Economics, Duke University, 213 Social Science Building, Box 90097, Durham, NC 27708-0097, USA (e-mail:kd.hoover@duke.edu) (s.)Department of Economics, California State University, Sacramento, 6000 J Street, Sacramento, CA 95819-6082, USA (e-mail:sjperez@csus.edu) Article History: Final Manuscript Received: October 2007