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Bootstrapping Macroeconometric Models

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  • Fair Ray C

    ()
    (Yale University)

Abstract

This paper outlines a bootstrapping approach to the estimation and analysis of macroeconometric models. It integrates for dynamic, nonlinear, simultaneous equation models the bootstrapping approach to evaluating estimators initiated by Efron (1979) and the stochastic simulation approach to evaluating models' properties initiated by Adelman and Adelman (1959). It also estimates for a particular model the gain in coverage accuracy from using bootstrap confidence intervals over asymptotic confidence intervals.

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File URL: http://www.degruyter.com/view/j/snde.2003.7.4/snde.2003.7.4.1110/snde.2003.7.4.1110.xml?format=INT
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Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 7 (2003)
Issue (Month): 4 (December)
Pages: 1-26

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Handle: RePEc:bpj:sndecm:v:7:y:2003:i:4:n:1

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References

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  1. Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 1015, Cowles Foundation for Research in Economics, Yale University.
  2. Fair, Ray C, 1993. "Testing the Rational Expectations Hypothesis in Macroeconometric Models," Oxford Economic Papers, Oxford University Press, vol. 45(2), pages 169-90, April.
  3. David E. Runkle, 1987. "Vector autoregressions and reality," Staff Report, Federal Reserve Bank of Minneapolis 107, Federal Reserve Bank of Minneapolis.
  4. repec:sae:niesru:v:164:y::i:1:p:90-99 is not listed on IDEAS
  5. Runkle, David E, 1987. "Vector Autoregressions and Reality," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 5(4), pages 437-42, October.
  6. Ray C. Fair & John B. Taylor, 1980. "Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 564, Cowles Foundation for Research in Economics, Yale University.
  7. Härdle, Wolfgang & Horowitz, Joel L. & Kreiss, Jens-Peter, 2001. "Bootstrap methods for time series," SFB 373 Discussion Papers 2001,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  8. Calzolari, Giorgio & Corsi, Paolo, 1977. "Stochastic simulation as a validation tool for econometric models," MPRA Paper 21226, University Library of Munich, Germany.
  9. Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics, EconWPA 9602009, EconWPA, revised 05 Mar 1996.
  10. Brown, Bryan W & Mariano, Roberto S, 1984. "Residual-Based Procedures for Prediction and Estimation in a Nonlinear Simultaneous System," Econometrica, Econometric Society, Econometric Society, vol. 52(2), pages 321-43, March.
  11. Fair, Ray C, 1980. "Estimating the Expected Predictive Accuracy of Econometric Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(2), pages 355-78, June.
  12. Jeremy Berkowitz & Lutz Kilian, 1996. "Recent developments in bootstrapping time series," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 96-45, Board of Governors of the Federal Reserve System (U.S.).
  13. Michael K. Evans & Lawrence R. Klein & Mitsuo Saito & Michael D. McCarthy, 1972. "Short-Run Prediction And Long-Run Simulation Of The Wharton Model," NBER Chapters, in: Econometric Models of Cyclical Behavior, Vols. 1 and 2, pages 139-200 National Bureau of Economic Research, Inc.
  14. Yoel Haitovsky & Neil Wallace, 1972. "A Study Of Discretionary And Nondiscretionary Monetary And Fiscal Policies In The Context Of Stochastic Macroeconometric Models," NBER Chapters, in: Economic Research: Retrospect and Prospect Vol 1: The Business Cycle Today, pages 261-310 National Bureau of Economic Research, Inc.
  15. repec:wop:humbsf:2001-59 is not listed on IDEAS
  16. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, Econometric Society, vol. 64(4), pages 891-916, July.
  17. Runkle, David E, 1987. "Vector Autoregressions and Reality: Reply," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 5(4), pages 454, October.
  18. Bianchi, Carlo & Calzolari, Giorgio & Corsi, Paolo, 1976. "Divergences in the results of stochastic and deterministic simulation of an Italian non linear econometric model," MPRA Paper 21287, University Library of Munich, Germany.
  19. T. Muench & A. Rolnick & N. Wallace, 1974. "Tests for Structural Change and Prediction Intervals for the reduced Forms of Two Structural Models of the U.S.: THe FRB-MIT and Michigan Quarterly Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 3, pages 45-74 National Bureau of Economic Research, Inc.
  20. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
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Cited by:
  1. Dag Kolsrud, 2008. "Stochastic Ceteris Paribus Simulations," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 31(1), pages 21-43, February.
  2. Bhattacharjee, Arnab & Jensen-Butler, Chris, 2013. "Estimation of the spatial weights matrix under structural constraints," Regional Science and Urban Economics, Elsevier, Elsevier, vol. 43(4), pages 617-634.
  3. Debby Lanser & Henk Kranendonk, 2008. "Investigating uncertainty in macroeconomic forecasts by stochastic simulation," CPB Discussion Paper 112, CPB Netherlands Bureau for Economic Policy Analysis.

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