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Complete and Incomplete Econometric Models

Author

Listed:
  • John Geweke

Abstract

Econometric models are widely used in the creation and evaluation of economic policy in the public and private sectors. But these models are useful only if they adequately account for the phenomena in question, and they can be quite misleading if they do not. In response, econometricians have developed tests and other checks for model adequacy. All of these methods, however, take as given the specification of the model to be tested. In this book, John Geweke addresses the critical earlier stage of model development, the point at which potential models are inherently incomplete. Summarizing and extending recent advances in Bayesian econometrics, Geweke shows how simple modern simulation methods can complement the creative process of model formulation. These methods, which are accessible to economics PhD students as well as to practicing applied econometricians, streamline the processes of model development and specification checking. Complete with illustrations from a wide variety of applications, this is an important contribution to econometrics that will interest economists and PhD students alike.

Suggested Citation

  • John Geweke, 2010. "Complete and Incomplete Econometric Models," Economics Books, Princeton University Press, edition 1, number 9218.
  • Handle: RePEc:pup:pbooks:9218
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    Citations

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    Cited by:

    1. Takashi Kano & James M. Nason, 2014. "Business Cycle Implications of Internal Consumption Habit for New Keynesian Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(2-3), pages 519-544, March.
    2. Buncic, Daniel & Müller, Oliver, 2017. "Measuring the output gap in Switzerland with linear opinion pools," Economic Modelling, Elsevier, vol. 64(C), pages 153-171.
    3. Szabolcs Deák & Paul Levine & Afrasiab Mirza & Joseph Pearlman, 2019. "Designing Robust Monetary Policy Using Prediction Pools," School of Economics Discussion Papers 1219, School of Economics, University of Surrey.
    4. Inoue, Atsushi & Kuo, Chun-Hung & Rossi, Barbara, 2020. "Identifying the sources of model misspecification," Journal of Monetary Economics, Elsevier, vol. 110(C), pages 1-18.
    5. Pablo A. Guerrón-Quintana & James M. Nason, 2013. "Bayesian estimation of DSGE models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.),Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 21, pages 486-512, Edward Elgar Publishing.
    6. Välilä, Timo, 2020. "Infrastructure and growth: A survey of macro-econometric research," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 39-49.
    7. Han Lin Shang, 2014. "Bayesian bandwidth estimation for a functional nonparametric regression model with mixed types of regressors and unknown error density," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(3), pages 599-615, September.
    8. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.),Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    9. Rodriguez-Lopez, Jesus & Solis-Garcia, Mario, 2018. "Defense spending and fiscal multipliers: it's all in the variance," MPRA Paper 86911, University Library of Munich, Germany.
    10. John Geweke & Gianni Amisano, 2014. "Analysis of Variance for Bayesian Inference," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 270-288, June.
    11. Leeper, E.M. & Leith, C., 2016. "Understanding Inflation as a Joint Monetary–Fiscal Phenomenon," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.),Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 2305-2415, Elsevier.
    12. Eric M. Leeper & Nora Traum & Todd B. Walker, 2017. "Clearing Up the Fiscal Multiplier Morass," American Economic Review, American Economic Association, vol. 107(8), pages 2409-2454, August.
    13. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
    14. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2019. "Does a financial accelerator improve forecasts during financial crises? Evidence from Japan with prediction-pooling methods," Journal of Asian Economics, Elsevier, vol. 60(C), pages 45-68.
    15. Kano, Takashi & Nason, James M., 2012. "Appendix: Business Cycle Implications of Internal Consumption Habit for New Keynesian Models," Discussion Papers 2012-08, Graduate School of Economics, Hitotsubashi University.
    16. Campbell Leith & Eric Leeper, 2016. "Understanding Inflation as a Joint Monetary-Fiscal Phenomenon," Working Papers 2016_01, Business School - Economics, University of Glasgow.
    17. Matteo Cacciatore & Nora Traum, 2020. "Trade Flows and Fiscal Multipliers," NBER Working Papers 27652, National Bureau of Economic Research, Inc.
    18. Gelain, Paolo & Manganelli, Simone, 2020. "Monetary policy with judgment," Working Paper Series 2404, European Central Bank.
    19. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
    20. In-Koo Cho & Kenneth Kasa, 2017. "Model Averaging and Persistent Disagreement," Review, Federal Reserve Bank of St. Louis, vol. 99(3), pages 279-294.
    21. Michal Andrle & Miroslav Plašil, 2016. "System Priors for Econometric Time Series," IMF Working Papers 16/231, International Monetary Fund.
    22. Fei Tan, 2017. "Interpreting rational expectations econometrics via analytic function approach," Economics Bulletin, AccessEcon, vol. 37(2), pages 1182-1190.
    23. McAdam, Peter & Warne, Anders, 2020. "Density forecast combinations: the real-time dimension," Working Paper Series 2378, European Central Bank.

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