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Published online by Cambridge University Press:  08 February 2005

Kevin D. Hoover
University of California, Davis


When Stephen Perez and I first began our Monte Carlo studies of the efficacy of general-to-specific search methodologies in 1995, we were keenly aware of our limited ability to capture the tacit knowledge of the skilled time-series econometrician operating in the London School of Economics (LSE) tradition (Hoover and Perez, 1999a, 1999b). Econometrics, we believed, was an art, and our algorithm was not intended to replace the artist. David Hendry and Hans-Martin Krolzig's subsequent development of PcGets did not, in fact, eliminate the art of econometrics. Power tools did not eliminate the art of the cabinetmaker but changed where his value added lay and—importantly—made new things possible. PcGets is likewise a new, powerful tool, useful in the hands of a skilled craftsman.I thank Peter Phillips, Selva Demiralp, Stephen Perez, and an anonymous referee for helpful comments on an earlier draft.

Research Article
© 2005 Cambridge University Press

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Bessler, D.A. & S. Lee (2002) Money and prices: U.S. data 1869–1914 (a study with directed graphs). Empirical Economics, 27, 427446.Google Scholar
Breitung, J. & N.R. Swanson (2002) Temporal aggregation and spurious instantaneous causality in multiple time series models. Journal of Time Series Analysis 23, 651665.Google Scholar
Cooley, T.F. & S.F. LeRoy (1985) Atheoretical macroeconomics: A critique, Journal of Monetary Economics 16, 283308.Google Scholar
Cooper, G.F. (1999) An overview of the representation and discovery of causal relationships using Bayesian networks. In C. Glymour & G.F. Cooper (eds.), Computation, Causation, and Discovery, pp. 364. American Association for Artificial Intelligence and MIT Press.
Demiralp, S. (2000) The Structure of Monetary Policy and Transmission Mechanism. Ph.D diss., Department of Economics, University of California, Davis.
Demiralp, S. & K.D. Hoover (2004) Searching for the causal structure of a vector autoregression. Oxford Bulletin of Economics and Statistics 65, supplement, 745767.Google Scholar
Glymour, C. & P. Spirtes (1988) Latent variables, causal models and overidentifying constraints. Journal of Econometrics 39, 175198.Google Scholar
Granger, C.W.J. (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424438.Google Scholar
Granger, C.W.J. (1988) Some recent developments in a concept of causality. Journal of Econometrics 39, 199211.Google Scholar
Hoover, K.D. (1988) The New Classical Macroeconomics. Blackwell.
Hoover, K.D. (1990) The logic of causal inference: Econometrics and the conditional analysis of causality. Economics and Philosophy 6, 207234.Google Scholar
Hoover, K.D. (1991) The causal direction between money and prices: An alternative approach. Journal of Monetary Economics 27, 381423.Google Scholar
Hoover, K.D. (2001) Causality in Macroeconomics. Cambridge University Press.
Hoover, K.D. (2003) Review of Judea Pearl's Causality: Models, Reasoning, and Inference. Economic Journal 113, F411F413.Google Scholar
Hoover, K.D. & S.J. Perez (1999a) Data mining reconsidered: Encompassing and the general-to-specific approach to specification search. Econometrics Journal, 2, 167191.Google Scholar
Hoover, K.D. & S.J. Perez (1999b) Reply to our discussants. Econometrics Journal 2, 244247.Google Scholar
Hoover, K.D. & S.M. Sheffrin (1992) Causation, spending and taxes: Sand in the sandbox or tax collector for the welfare state? American Economic Review 82, 225248.Google Scholar
Hoover, K.D. & M. Siegler (2000) Taxing and spending in the long view: The causal structure of U.S. fiscal policy after 1791. Oxford Economic Papers 52, 745773.Google Scholar
Leamer, E.E. (1985) Vector autoregressions for causal inference. In K. Brunner & A.H. Meltzer (eds.), Understanding Monetary Regimes, Carnegie-Rochester Conference Series on Public Policy, vol. 22, pp. 225304. North-Holland.
LeRoy, S.F. (2002) Causality: Models, Reasoning, and Inference: A review of Judea Pearl, Causality. Journal of Economic Methodology. 9, 100103.Google Scholar
Moneta, A. (2003) Graphical Models for Structural Vector Autoregressions. Typescript, S. Anna School of Advanced Studies, Pisa, Italy.
Neuberg, L.G. (2003) Review of Causality: Models, Reasoning, and Inference by Judea Pearl. Econometric Theory 19, 675685.Google Scholar
Pearl, J. (2000) Causality: Models, Reasoning, and Inference. Cambridge University Press.
Pearl, J. (2003) Comments on Neuberg's review of Causality. Econometric Theory 19, 686689.Google Scholar
Richardson, T. (1996) A discovery algorithm for directed cyclical graphs. In F. Jensen & E. Horwitz (eds.), Uncertainty in Artificial Intelligence: Proceedings of the Twelfth Congress, pp. 462469. Morgan Kaufman.
Sims, C.A. (1980) Macroeconomics and reality, Econometrica 48, 148.Google Scholar
Sims, C.A. (1986) Are forecasting models usable for policy analysis? Federal Reserve Bank of Minneapolis Quarterly Review 10, 215.Google Scholar
Spirtes, P., C. Glymour, & R. Scheines (2000) Causation, Prediction, and Search, 2nd ed. MIT Press.
Swanson, N.R. (2002) Review of Judea Pearl's Causality: Models, reasoning and inference. Journal of Economic Literature 40(3), 925926.Google Scholar
Swanson, N.R. & C.W.J. Granger (1997) Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions. Journal of the American Statistical Association 92, 357367.Google Scholar
Wold, H.O.A. (1949) Statistical estimation of economic relationships. Econometrica 17, supplement, 121.Google Scholar