Fiscal Foresight and Information Flows

Authors

  • Eric M. Leeper,

    1. Dept. of Economics, Indiana University, Bloomington, IN 47405, U.S.A., Monash University, and NBER; eleeper@indiana.edu
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  • Todd B. Walker,

    1. Dept. of Economics, Indiana University, Bloomington, IN 47405, U.S.A.; walkertb@indiana.edu
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  • Shu-Chun Susan Yang

    1. International Monetary Fund, Washington, DC 20431, U.S.A.; SYang@imf.org
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    • Walker acknowledges support from NSF Grant SES-0962221. Yang thanks Academia Sinica for support in the early stages of this research. We also acknowledge comments by Troy Davig, Mike Dotsey, Jesús Fernández-Villaverde, Dale Henderson, Beth Klee, Karel Mertens, Jim Nason, Ricardo Nunes, Valerie Ramey, Morten Ravn, Chris Sims, and participants at many conferences and presentations. Joonyoung Hur provided excellent research assistance. We are particularly grateful to Harald Uhlig and four anonymous referees for helpful comments. The views expressed herein are those of the authors and should not be attributed to the IMF, its Executive Board, or its management.


Abstract

News—or foresight—about future economic fundamentals can create rational expectations equilibria with non-fundamental representations that pose substantial challenges to econometric efforts to recover the structural shocks to which economic agents react. Using tax policies as a leading example of foresight, simple theory makes transparent the economic behavior and information structures that generate non-fundamental equilibria. Econometric analyses that fail to model foresight will obtain biased estimates of output multipliers for taxes; biases are quantitatively important when two canonical theoretical models are taken as data generating processes. Both the nature of equilibria and the inferences about the effects of anticipated tax changes hinge critically on hypothesized information flows. Different methods for extracting or hypothesizing the information flows are discussed and shown to be alternative techniques for resolving a non-uniqueness problem endemic to moving average representations.

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