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Bayesian Model Averaging in the Instrumental Variable Regression Model

Author

Listed:
  • Gary Koop

    (Rimini Center for Economic Analysis)

  • Robert Leon Gonzalez

    (National Graduate Institute for Policy Studies)

  • Rodney Strachan

    (Rimini Center for Economic Analysis)

Abstract

This paper considers the instrumental variable regression model when there is uncertainly about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainly can result in a huge number of models. To avoid statistical problems associated with standard model selection procedures, we develop a reversible jump Markov chain Monte Carlo algorithm that allows us to do Bayesian model averaging. The algorithm is very flexible and can be easily adapted to analyze any of the different priors that have been proposed in the Bayesian instrumental variables literature. We show how to calculate the probability of any relevant restriction (e.g. the posterior probability that over-identifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returns-to-schooling application.

Suggested Citation

  • Gary Koop & Robert Leon Gonzalez & Rodney Strachan, 2011. "Bayesian Model Averaging in the Instrumental Variable Regression Model," GRIPS Discussion Papers 10-32, National Graduate Institute for Policy Studies.
  • Handle: RePEc:ngi:dpaper:10-32
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    References listed on IDEAS

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    1. Justin L. Tobias & Mingliang Li, 2004. "Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 153-180, April.
    2. Dreze, Jacques H. & Richard, Jean-Francois, 1983. "Bayesian analysis of simultaneous equation systems," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 9, pages 517-598 Elsevier.
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    4. Dreze, Jacques H, 1976. "Bayesian Limited Information Analysis of the Simultaneous Equations Model," Econometrica, Econometric Society, vol. 44(5), pages 1045-1075, September.
    5. Zellner, Arnold & Bauwens, Luc & Van Dijk, Herman K., 1988. "Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 39-72.
    6. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    7. Strachan, Rodney W. & Inder, Brett, 2004. "Bayesian analysis of the error correction model," Journal of Econometrics, Elsevier, vol. 123(2), pages 307-325, December.
    8. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
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    15. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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    Citations

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    Cited by:

    1. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    2. Horváth, Roman, 2013. "Does trust promote growth?," Journal of Comparative Economics, Elsevier, vol. 41(3), pages 777-788.
    3. Ampaabeng, Samuel K. & Tan, Chih Ming, 2013. "The long-term cognitive consequences of early childhood malnutrition: The case of famine in Ghana," Journal of Health Economics, Elsevier, vol. 32(6), pages 1013-1027.
    4. Samuel K. Ampaabeng & Chih Ming Tang, 2012. "The Long-Term Cognitive Consequences of Early Childhood Malnutrition: The Case of Famine in Ghana," Working Paper series 64_12, Rimini Centre for Economic Analysis.
    5. Tanaka, Kiyoyasu, 2015. "The impact of foreign firms on industrial productivity : evidence from Japan," IDE Discussion Papers 533, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    6. Michael Jetter & Christopher F. Parmeter, 2016. "Uncovering the determinants of corruption," Working Papers 2016-02, University of Miami, Department of Economics.
    7. San Ahmed, Arsalan & Holloway, Garth John, 2017. "Calories, conflict and correlates: Redistributive food security in post-conflict Iraq," Food Policy, Elsevier, vol. 68(C), pages 89-99.
    8. Pham, Thi Hong Hanh, 2017. "Impacts of globalization on the informal sector: Empirical evidence from developing countries," Economic Modelling, Elsevier, vol. 62(C), pages 207-218.
    9. Hedibert F. Lopes & Nicholas G. Polson, 2014. "Bayesian Instrumental Variables: Priors and Likelihoods," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 100-121, June.
    10. 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.
    11. Leon-Gonzalez, Roberto & Vinayagathasan, Thanabalasingam, 2015. "Robust determinants of growth in Asian developing economies: A Bayesian panel data model averaging approach," Journal of Asian Economics, Elsevier, vol. 36(C), pages 34-46.
    12. TANAKA Kiyoyasu, 2015. "The Impact of Foreign Firms on Industrial Productivity: A Bayesian-model averaging approach," Discussion papers 15009, Research Institute of Economy, Trade and Industry (RIETI).
    13. Jaroslava Hlouskova & Martin Wagner, 2013. "The Determinants of Long-Run Economic Growth: A Conceptually and Computationally Simple Approach," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 149(IV), pages 445-492, December.
    14. repec:eee:wdevel:v:109:y:2018:i:c:p:279-294 is not listed on IDEAS
    15. Martins, Luis F. & Gabriel, Vasco J., 2014. "Linear instrumental variables model averaging estimation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 709-724.
    16. Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.
    17. Andros Kourtellos & Alex Lenkoski & Kyriakos Petrou, 2017. "Measuring the Strength of the Theories of Government Size," University of Cyprus Working Papers in Economics 11-2017, University of Cyprus Department of Economics.

    More about this item

    Keywords

    Bayesian; endogeneity; simultaneous equations; reversible jump Markov chain Monte Carlo;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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