IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/81568.html
   My bibliography  Save this paper

Model Averaging and its Use in Economics

Author

Listed:
  • Steel, Mark F. J.

Abstract

The method of model averaging has become an important tool to deal with model uncertainty, in particular in empirical settings with large numbers of potential models and relatively limited numbers of observations, as are common in economics. Model averaging is a natural response to model uncertainty in a Bayesian framework, so most of the paper deals with Bayesian model averaging. In addition, frequentist model averaging methods are also discussed. Numerical methods to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on the problem of variable selection in linear regression models, but the paper also discusses other, more challenging, settings. Some of the applied literature is reviewed with particular emphasis on applications in economics. The role of the prior assumptions in Bayesian procedures is highlighted, and some recommendations for applied users are provided

Suggested Citation

  • Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:81568
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/81568/1/MPRA_paper_81568.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lars Peter Hansen & Thomas J Sargent, 2014. "Uncertainty within Economic Models," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9028, August.
    2. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
    3. Rodney W. Strachan & Herman K. Van Dijk, 2013. "Evidence On Features Of A Dsge Business Cycle Model From Bayesian Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 54(1), pages 385-402, February.
    4. William A. Brock & Steven N. Durlauf, 2015. "On Sturdy Policy Evaluation," The Journal of Legal Studies, University of Chicago Press, vol. 44(S2), pages 447-473.
    5. Martin Feldkircher & Stefan Zeugner, 2012. "The impact of data revisions on the robustness of growth determinants—a note on ‘determinants of economic growth: Will data tell?’," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(4), pages 686-694, June.
    6. Havranek, Tomas & Rusnak, Marek & Sokolova, Anna, 2017. "Habit formation in consumption: A meta-analysis," European Economic Review, Elsevier, vol. 95(C), pages 142-167.
    7. Li, Mingliang & Tobias, Justin, 2004. "Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures," Staff General Research Papers Archive 12011, Iowa State University, Department of Economics.
    8. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2011. "Market Freedom and the Global Recession," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 59(1), pages 111-135, April.
    9. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    10. Tai-kuang Ho, 2015. "Looking for a Needle in a Haystack: Revisiting the Cross-country Causes of the 2008–9 Crisis by Bayesian Model Averaging," Economica, London School of Economics and Political Science, vol. 82(328), pages 813-840, October.
    11. Garret Christensen & Edward Miguel, 2018. "Transparency, Reproducibility, and the Credibility of Economics Research," Journal of Economic Literature, American Economic Association, vol. 56(3), pages 920-980, September.
    12. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney, 2012. "Bayesian model averaging in the instrumental variable regression model," Journal of Econometrics, Elsevier, vol. 171(2), pages 237-250.
    13. Christoph Hanck, 2016. "I just ran two trillion regressions," Economics Bulletin, AccessEcon, vol. 36(4), pages 2037-2042.
    14. Qingfeng Liu & Ryo Okui & Arihiro Yoshimura, 2016. "Generalized Least Squares Model Averaging," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1692-1752, December.
    15. Winford H. Masanjala & Chris Papageorgiou, 2008. "Rough and lonely road to prosperity: a reexamination of the sources of growth in Africa using Bayesian model averaging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 671-682.
    16. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    17. Rose, Andrew K. & Spiegel, Mark M., 2011. "Cross-country causes and consequences of the crisis: An update," European Economic Review, Elsevier, vol. 55(3), pages 309-324, April.
    18. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    19. Camarero, Mariam & Forte, Anabel & Garcia-Donato, Gonzalo & Mendoza, Yurena & Ordoñez, Javier, 2015. "Variable selection in the analysis of energy consumption–growth nexus," Energy Economics, Elsevier, vol. 52(PA), pages 207-216.
    20. David Y. Albouy, 2012. "The Colonial Origins of Comparative Development: An Empirical Investigation: Comment," American Economic Review, American Economic Association, vol. 102(6), pages 3059-3076, October.
    21. Mauro Bernardi & Leopoldo Catania, 2014. "The Model Confidence Set package for R," Papers 1410.8504, arXiv.org.
    22. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
    23. Frankel, Jeffrey & Saravelos, George, 2012. "Can leading indicators assess country vulnerability? Evidence from the 2008–09 global financial crisis," Journal of International Economics, Elsevier, vol. 87(2), pages 216-231.
    24. Minerva Mukhopadhyay & Tapas Samanta, 2017. "A mixture of g-priors for variable selection when the number of regressors grows with the sample size," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 377-404, June.
    25. Kourtellos, Andros & Marr, Christa & Tan, Chih Ming, 2016. "Robust determinants of intergenerational mobility in the land of opportunity," European Economic Review, Elsevier, vol. 81(C), pages 132-147.
    26. Magnus, J.R. & Powell, O.R. & Prüfer, P., 2008. "A Comparison of Two Averaging Techniques with an Application to Growth Empirics," Other publications TiSEM 0392dffa-51e0-4bc9-9644-f, Tilburg University, School of Economics and Management.
    27. Jetter, Michael & Parmeter, Christopher F., 2018. "Sorting through global corruption determinants: Institutions and education matter – Not culture," World Development, Elsevier, vol. 109(C), pages 279-294.
    28. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    29. Geweke, John & Koop, Gary & van Dijk, Herman (ed.), 2011. "The Oxford Handbook of Bayesian Econometrics," OUP Catalogue, Oxford University Press, number 9780199559084.
    30. Onorante, Luca & Raftery, Adrian E., 2016. "Dynamic model averaging in large model spaces using dynamic Occam׳s window," European Economic Review, Elsevier, vol. 81(C), pages 2-14.
    31. Andros Kourtellos & Charalambos G. Tsangarides, 2022. "Robust Correlates of Growth Spells: Do Inequality and Redistribution Matter?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1302-1328, December.
    32. Devereux, John & Dwyer, Gerald P., 2016. "What determines output losses after banking crises?," Journal of International Money and Finance, Elsevier, vol. 69(C), pages 69-94.
    33. Jeffrey Frankel, 2012. "The Natural Resource Curse: A Survey of Diagnoses and Some Prescriptions," Growth Lab Working Papers 36, Harvard's Growth Lab.
    34. Feldkircher, Martin & Horvath, Roman & Rusnak, Marek, 2014. "Exchange market pressures during the financial crisis: A Bayesian model averaging evidence," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 21-41.
    35. Dasgupta, Amil & Leon-Gonzalez, Roberto & Shortland, Anja, 2011. "Regionality revisited: An examination of the direction of spread of currency crises," Journal of International Money and Finance, Elsevier, vol. 30(5), pages 831-848, September.
    36. Havranek, Tomas & Horvath, Roman & Irsova, Zuzana & Rusnak, Marek, 2015. "Cross-country heterogeneity in intertemporal substitution," Journal of International Economics, Elsevier, vol. 96(1), pages 100-118.
    37. David H. Romer & Jeffrey A. Frankel, 1999. "Does Trade Cause Growth?," American Economic Review, American Economic Association, vol. 89(3), pages 379-399, June.
    38. León-González, Roberto & Montolio, Daniel, 2015. "Endogeneity and panel data in growth regressions: A Bayesian model averaging approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 23-39.
    39. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, vol. 1(2), pages 1-23, September.
    40. José Manuel Cordero & Manuel Muñiz & Cristina Polo, 2016. "The determinants of cognitive and non-cognitive educational outcomes: empirical evidence in Spain using a Bayesian approach," Applied Economics, Taylor & Francis Journals, vol. 48(35), pages 3355-3372, July.
    41. Christian Daude & Arne Nagengast & Jose Ramon Perea, 2016. "Productive capabilities: An empirical analysis of their drivers," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 25(4), pages 504-535, June.
    42. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    43. Antonio Ciccone & Marek Jarociński, 2010. "Determinants of Economic Growth: Will Data Tell?," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(4), pages 222-246, October.
    44. Robert J. Barro, 1991. "Economic Growth in a Cross Section of Countries," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(2), pages 407-443.
    45. Jan R. Magnus & Wendun Wang, 2014. "Concept-Based Bayesian Model Averaging and Growth Empirics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(6), pages 874-897, December.
    46. Marcin Błażejowski & Paweł Kufel & Jacek Kwiatkowski, 2020. "Model simplification and variable selection: A replication of the UK inflation model by Hendry (2001)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 645-652, August.
    47. William A. Brock & Steven N. Durlauf & Kenneth D. West, 2003. "Policy Evaluation in Uncertain Economic Environments," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 34(1), pages 235-322.
    48. David F. Hendry & Hans-Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    2. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
    3. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
    4. Bruns, Stephan B. & Ioannidis, John P.A., 2020. "Determinants of economic growth: Different time different answer?," Journal of Macroeconomics, Elsevier, vol. 63(C).
    5. D'Andrea, Sara, 2022. "Are there any robust determinants of growth in Europe? A Bayesian Model Averaging approach," International Economics, Elsevier, vol. 171(C), pages 143-173.
    6. Anastasia Dimiski, 2020. "Factors that affect Students’ performance in Science: An application using Gini-BMA methodology in PISA 2015 dataset," Working Papers 2004, University of Guelph, Department of Economics and Finance.
    7. Jetter, Michael & Parmeter, Christopher F., 2018. "Sorting through global corruption determinants: Institutions and education matter – Not culture," World Development, Elsevier, vol. 109(C), pages 279-294.
    8. León-González, Roberto & Montolio, Daniel, 2015. "Endogeneity and panel data in growth regressions: A Bayesian model averaging approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 23-39.
    9. Jesus regstdpo-Cuaresma & Neil Foster & Robert Stehrer, 2011. "Determinants of Regional Economic Growth by Quantile," Regional Studies, Taylor & Francis Journals, vol. 45(6), pages 809-826.
    10. Kuo-Jung Lee & Yi-Chi Chen, 2018. "Of needles and haystacks: revisiting growth determinants by robust Bayesian variable selection," Empirical Economics, Springer, vol. 54(4), pages 1517-1547, June.
    11. Michael Jetter & Christopher F. Parmeter, 2016. "Uncovering the determinants of corruption," Working Papers 2016-02, University of Miami, Department of Economics.
    12. Aart Kraay & Norikazu Tawara, 2013. "Can specific policy indicators identify reform priorities?," Journal of Economic Growth, Springer, vol. 18(3), pages 253-283, September.
    13. Yin-Wong Cheung & Wenhao Wang, 2020. "A Jackknife Model Averaging Analysis of RMB Misalignment Estimates," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 1-45, June.
    14. Marcin Błażejowski & Jacek Kwiatkowski & Jakub Gazda, 2019. "Sources of Economic Growth: A Global Perspective," Sustainability, MDPI, vol. 11(1), pages 1-14, January.
    15. Bettina Grün & Paul Hofmarcher, 2021. "Identifying groups of determinants in Bayesian model averaging using Dirichlet process clustering," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 1018-1045, September.
    16. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
    17. Ebersberger, Bernd & Galia, Fabrice & Laursen, Keld & Salter, Ammon, 2021. "Inbound Open Innovation and Innovation Performance: A Robustness Study," Research Policy, Elsevier, vol. 50(7).
    18. Hofmarcher, Paul & Crespo Cuaresma, Jesus & Grün, Bettina & Humer, Stefan & Moser, Mathias, 2018. "Bivariate jointness measures in Bayesian Model Averaging: Solving the conundrum," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 150-165.
    19. Eriṣ, Mehmet N. & Ulaṣan, Bülent, 2013. "Trade openness and economic growth: Bayesian model averaging estimate of cross-country growth regressions," Economic Modelling, Elsevier, vol. 33(C), pages 867-883.
    20. Shahram Amini & Christopher F. Parmeter, 2020. "A Review of the ‘BMS’ Package for R with Focus on Jointness," Econometrics, MDPI, vol. 8(1), pages 1-21, February.

    More about this item

    Keywords

    Bayesian methods; Model uncertainty; Normal linear model; Prior specification; Robustness;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:81568. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.