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Empirical Modeling in Economics

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  • Granger,Clive W. J.

Abstract

In these three essays, Professor Granger explains the process of constructing and evaluating an empirical model. Drawing on a wide range of cases and vignettes from economics, finance, politics and environment economics, as well as from art, literature, and the entertainment industry, Professor Granger combines rigour with intuition to provide a unique and entertaining insight into one of the most important subjects in modern economics. Chapter 1 deals with Specification. The process of specifying a model is discussed using deforestation in the Amazon region of Brazil as an illustration. Chapter 2 considers Evaluation, and argues that insufficent evaluation is undertaken by economists, and that models should be evaluated in terms of the quality of their output. In Chapter 3, the question of how to evaluate forecasts is considered at several levels of increasing depth and using a more sophisticated, technical approach than in the earlier two chapters.

Suggested Citation

  • Granger,Clive W. J., 1999. "Empirical Modeling in Economics," Cambridge Books, Cambridge University Press, number 9780521662086.
  • Handle: RePEc:cup:cbooks:9780521662086
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    Cited by:

    1. Francesco Guala & Andrea Salanti, 2002. "On the Robustness of Economic Models," Working Papers (-2012) 0208, University of Bergamo, Department of Economics.
    2. Trino-Manuel Niguez & Javier Perote, 2004. "Forecasting the density of asset returns," STICERD - Econometrics Paper Series 479, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Fabio Bacchini & Cristina Brandimarte & Piero Crivelli & Roberta De Santis & Marco Fioramanti & Alessandro Girardi & Roberto Golinelli & Cecilia Jona-Lasinio & Massimo Mancini & Carmine Pappalardo & D, 2013. "Building the core of the Istat system of models for forecasting the Italian economy: MeMo-It," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(1), pages 17-45.
    4. Gunnar Bardsen & Eilev Jansen & Ragnar Nymoen, 2002. "Model Specification and Inflation Forecast Uncertainty," Annals of Economics and Statistics, GENES, issue 67-68, pages 495-517.
    5. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    6. Lee, Tae-Hwy & Tu, Yundong & Ullah, Aman, 2014. "Nonparametric and semiparametric regressions subject to monotonicity constraints: Estimation and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 196-210.
    7. Francesco Audrino & Dominik Colagelo, 2007. "Forecasting Implied Volatility Surfaces," University of St. Gallen Department of Economics working paper series 2007 2007-42, Department of Economics, University of St. Gallen.
    8. Mircea ASANDULUI, 2012. "On forecasting stock options volatility: evidence from London international financial futures and options exchange," Anale. Seria Stiinte Economice. Timisoara, Faculty of Economics, Tibiscus University in Timisoara, vol. 0, pages 505-511, May.
    9. Sainan Jin & Valentina Corradi & Norman Swanson, 2015. "Robust Forecast Comparison," Departmental Working Papers 201502, Rutgers University, Department of Economics.
    10. McCauley, Joseph L., 2004. "What Economists can learn from physics and finance," MPRA Paper 2240, University Library of Munich, Germany.
    11. McCauley, Joseph L., 2005. "Making dynamic modeling effective in economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 1-9.
    12. Bernt P. Stigum, 2015. "Introduction," Introductory Chapters,in: Econometrics and the Philosophy of Economics: Theory-Data Confrontations in Economics Princeton University Press.
    13. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.
    14. Bin Chen & Yongmiao Hong, 2013. "A Unified Approach to Validating Univariate and Multivariate Conditional Distribution Models in Time Series," WISE Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    15. Mircea ASANDULUI, 2012. "A Multi-Horizon Comparison Of Volatility Forecasts: An Application To Stock Options Traded At Euronext Exchange Amsterdam," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 10, pages 179-190, December.
    16. Gunnar Bårdsen & Kjersti-Gro Lindquist & Dimitrios P. Tsomocos, 2006. "Evaluation of macroeconomic models for financial stability analysis," Working Paper Series 6806, Department of Economics, Norwegian University of Science and Technology.
    17. Chen, Bin & Hong, Yongmiao, 2014. "A unified approach to validating univariate and multivariate conditional distribution models in time series," Journal of Econometrics, Elsevier, vol. 178(P1), pages 22-44.
    18. Castle, Jennifer L. & Hendry, David F., 2014. "Model selection in under-specified equations facing breaks," Journal of Econometrics, Elsevier, vol. 178(P2), pages 286-293.
    19. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
    20. Christoffel, Kai & Warne, Anders & Coenen, Günter, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    21. Nymoen, Ragnar, 2005. "Evaluating a Central Bank’s Recent Forecast Failure," Memorandum 22/2005, Oslo University, Department of Economics.
    22. Rodolphe Buda, 2015. "Data Checking and Econometric Software Development: A Technique of Traceability by Fictive Data Encoding," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 325-357, August.

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