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Fiscal Foresight: Analytics and Econometrics

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  • Eric M. Leeper
  • Todd B. Walker
  • Shu-Chun Susan Yang

Abstract

Fiscal foresight---the phenomenon that legislative and implementation lags ensure that private agents receive clear signals about the tax rates they face in the future---is intrinsic to the tax policy process. This paper develops an analytical framework to study the econometric implications of fiscal foresight. Simple theoretical examples show that foresight produces equilibrium time series with a non-invertible moving average component, which misaligns the agents' and the econometrician's information sets in estimated VARs. Economically meaningful shocks to taxes, therefore, cannot be extracted from statistical innovations in conventional ways. Econometric analyses that fail to align agents' and the econometrician's information sets can produce distorted inferences about the effects of tax policies. Because non-invertibility arises as a natural outgrowth of the fact that agents' optimal decisions discount future tax obligations, it is likely to be endemic to the study of fiscal policy. In light of the implications of the analytical framework, we evaluate two existing empirical approaches to quantifying the impacts of fiscal foresight. The paper also offers a formal interpretation of the narrative approach to identifying fiscal policy.

Suggested Citation

  • Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2008. "Fiscal Foresight: Analytics and Econometrics," NBER Working Papers 14028, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:14028
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    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Thomas J. Sargent & Mark W. Watson, 2007. "ABCs (and Ds) of Understanding VARs," American Economic Review, American Economic Association, vol. 97(3), pages 1021-1026, June.
    2. Domenico Giannone & Lucrezia Reichlin, 2006. "Does information help recovering structural shocks from past observations?," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 455-465, 04-05.
    3. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    4. Martin Feldstein & Daniel Feenberg, 1996. "The Effect of Increased Tax Rates on Taxable Income and Economic Efficiency: A Preliminary Analysis of the 1993 Tax Rate Increases," NBER Chapters, in: Tax Policy and the Economy, Volume 10, pages 89-118, National Bureau of Economic Research, Inc.
    5. Douglas G. Steigerwald & Charles Stuart, 1997. "Econometric Estimation Of Foresight: Tax Policy And Investment In The United States," The Review of Economics and Statistics, MIT Press, vol. 79(1), pages 32-40, February.
    6. Yang, Shu-Chun Susan, 2007. "Tentative evidence of tax foresight," Economics Letters, Elsevier, vol. 96(1), pages 30-37, July.
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    More about this item

    JEL classification:

    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
    • H3 - Public Economics - - Fiscal Policies and Behavior of Economic Agents

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