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Are Small-Scale SVARs Useful for Business Cycle Analysis? Revisiting Nonfundamentalness

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  • Fabio Canova
  • Mehdi Hamidi Sahneh

Abstract

Nonfundamentalness arises when current and past values of the observables do not contain enough information to recover structural vector autoregressive (SVAR) disturbances. Using Granger causality tests, the literature suggested that several small-scale SVAR models are nonfundamental and thus not necessarily useful for business cycle analysis. We show that causality tests are problematic when SVAR variables cross-sectionally aggregate the variables of the underlying economy or proxy for nonobservables. We provide an alternative testing procedure, illustrate its properties with Monte Carlo simulations, and re-examine a prototypical small-scale SVAR model.

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  • Fabio Canova & Mehdi Hamidi Sahneh, 2018. "Are Small-Scale SVARs Useful for Business Cycle Analysis? Revisiting Nonfundamentalness," Journal of the European Economic Association, European Economic Association, vol. 16(4), pages 1069-1093.
  • Handle: RePEc:oup:jeurec:v:16:y:2018:i:4:p:1069-1093.
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    1. Lippi, Marco & Reichlin, Lucrezia, 1993. "The Dynamic Effects of Aggregate Demand and Supply Disturbances: Comment," American Economic Review, American Economic Association, vol. 83(3), pages 644-652, June.
    2. 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.
    3. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.
    4. Lippi, Marco & Reichlin, Lucrezia, 1994. "VAR analysis, nonfundamental representations, blaschke matrices," Journal of Econometrics, Elsevier, vol. 63(1), pages 307-325, July.
    5. 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.
    6. Hansen, Lars Peter & Hodrick, Robert J, 1980. "Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis," Journal of Political Economy, University of Chicago Press, vol. 88(5), pages 829-853, October.
    7. Paul Beaudry & Franck Portier, 2006. "Stock Prices, News, and Economic Fluctuations," American Economic Review, American Economic Association, vol. 96(4), pages 1293-1307, September.
    8. Paul Beaudry & Patrick Fève & Alain Guay & Franck Portier, 2015. "When is Nonfundamentalness in VARs a Real Problem? An Application to News Shocks," NBER Working Papers 21466, National Bureau of Economic Research, Inc.
    9. Forni, Mario & Gambetti, Luca, 2014. "Sufficient information in structural VARs," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 124-136.
    10. Mario Forni & Luca Gambetti & Marco Lippi & Luca Sala, 2017. "Noisy News in Business Cycles," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(4), pages 122-152, October.
    11. Yongsung Chang & Jay H. Hong, 2006. "Do Technological Improvements in the Manufacturing Sector Raise or Lower Employment?," American Economic Review, American Economic Association, vol. 96(1), pages 352-368, March.
    12. Hansen, Lars Peter & Sargent, Thomas J., 1980. "Formulating and estimating dynamic linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 7-46, May.
    13. Eric M. Leeper & Todd B. Walker & Shu‐Chun Susan Yang, 2013. "Fiscal Foresight and Information Flows," Econometrica, Econometric Society, vol. 81(3), pages 1115-1145, May.
    14. Fabio Canova & David Lopez-Salido & Claudio Michelacci, 2010. "The effects of technology shocks on hours and output: a robustness analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 755-773.
    15. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    Cited by:

    1. Efrem Castelnuovo & Guay Lim, 2019. "What Do We Know About the Macroeconomic Effects of Fiscal Policy? A Brief Survey of the Literature on Fiscal Multipliers," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 52(1), pages 78-93, March.
    2. Paul Beaudry & Patrick Feve & Alain Guay & Franck Portier, 2019. "When is Nonfundamentalness in SVARs a Real Problem?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 34, pages 221-243, October.
    3. Fabio Canova & Filippo Ferroni, 2018. "Mind the gap! Stylized dynamic facts and structural models," Working Papers No 13/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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