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Model Selection Criteria: An Investigation of Relative Accuracy, Posterior Probabilities, and Combinations of Criteria

Author

Listed:
  • Roland T. Rust

    (Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203)

  • Duncan Simester

    (Graduate School of Business, University of Chicago, 1101 E. 58th St., Chicago, Illinois 60637)

  • Roderick J. Brodie

    (University of Auckland, Auckland, New Zealand)

  • V. Nilikant

    (University of Canterbury, Canterbury, United Kingdom)

Abstract

We investigate the performance of empirical criteria for comparing and selecting quantitative models from among a candidate set. A simulation based on empirically observed parameter values is used to determine which criterion is the most accurate at identifying the correct model specification. The simulation is composed of both nested and nonnested linear regression models. We then derive posterior probability estimates of the superiority of the alternative models from each of the criteria and evaluate the relative accuracy, bias, and information content of these probabilities. To investigate whether additional accuracy can be derived from combining criteria, a method for obtaining a joint prediction from combinations of the criteria is proposed and the incremental improvement in selection accuracy considered. Based on the simulation, we conclude that most leading criteria perform well in selecting the best model, and several criteria also produce accurate probabilities of model superiority. Computationally intensive criteria failed to perform better than criteria which were computationally simpler. Also, the use of several criteria in combination failed to appreciably outperform the use of one model. The Schwarz criterion performed best overall in terms of selection accuracy, accuracy of posterior probabilities, and ease of use. Thus, we suggest that general model comparison, model selection, and model probability estimation be performed using the Schwarz criterion, which can be implemented (given the model log likelihoods) using only a hand calculator.

Suggested Citation

  • Roland T. Rust & Duncan Simester & Roderick J. Brodie & V. Nilikant, 1995. "Model Selection Criteria: An Investigation of Relative Accuracy, Posterior Probabilities, and Combinations of Criteria," Management Science, INFORMS, vol. 41(2), pages 322-333, February.
  • Handle: RePEc:inm:ormnsc:v:41:y:1995:i:2:p:322-333
    DOI: 10.1287/mnsc.41.2.322
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    Cited by:

    1. Franziska Völckner & Henrik Sattler, 2005. "Markentransfererfolgsanalysen bei kurzlebigen Konsumgütern unter Berücksichtigung von Konsumentenheterogenität," Schmalenbach Journal of Business Research, Springer, vol. 57(8), pages 669-688, December.
    2. Trost, Robert & Silk, Julian, 2003. "Quantitative Models in Marketing Research,: Philip Hans Franses and Richard Paap (Eds.), Cambridge University Press, Cambridge, UK. (2001), 206 pp. - ISBN 0-521-80166-4, [UK pound]30.00," International Journal of Forecasting, Elsevier, vol. 19(3), pages 535-538.
    3. Guitart, Ivan A. & Hervet, Guillaume, 2017. "The impact of contextual television ads on online conversions: An application in the insurance industry," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 480-498.
    4. Sarstedt, Marko & Salcher, André, 2007. "Modellselektion in Finite Mixture PLS-Modellen," Discussion Papers in Business Administration 1394, University of Munich, Munich School of Management.
    5. Heerde, Harald J. van & Leeflang, Peter S.H. & Wittink, Dick R., 1999. "The estimation of pre- and postpromotion dips with store-level scanner data," Research Report 99B36, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    6. DeSarbo, Wayne S. & Choi, Jungwhan, 1998. "A latent structure double hurdle regression model for exploring heterogeneity in consumer search patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 423-455, November.
    7. Eelco Kappe & Ashley Stadler Blank & Wayne S. DeSarbo, 2014. "A General Multiple Distributed Lag Framework for Estimating the Dynamic Effects of Promotions," Management Science, INFORMS, vol. 60(6), pages 1489-1510, June.
    8. Rodrigo Gil & Carlos Ricardo Bojacá & Eddie Schrevens, 2017. "Uncertainty of the Agricultural Grey Water Footprint Based on High Resolution Primary Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3389-3400, September.
    9. Rosbergen, Edward & Wedel, Michel & Pieters, Rik, 1997. "Analyzing visual attention tot repeated print advertising using scanpath theory," Research Report 97B32, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    10. Kim, Namwoon & Srivastava, Rajendra K., 2007. "Modeling cross-price effects on inter-category dynamics: The case of three computing platforms," Omega, Elsevier, vol. 35(3), pages 290-301, June.
    11. Zhiqiang Zheng & Balaji Padmanabhan, 2007. "Constructing Ensembles from Data Envelopment Analysis," INFORMS Journal on Computing, INFORMS, vol. 19(4), pages 486-496, November.
    12. Jason A. Duan & Leigh McAlister & Shameek Sinha, 2011. "Commentary--Reexamining Bayesian Model-Comparison Evidence of Cross-Brand Pass-Through," Marketing Science, INFORMS, vol. 30(3), pages 550-561, 05-06.
    13. repec:dgr:rugsom:99b36 is not listed on IDEAS
    14. Wayne DeSarbo & Joonwook Park & Vithala Rao, 2011. "Deriving joint space positioning maps from consumer preference ratings," Marketing Letters, Springer, vol. 22(1), pages 1-14, March.
    15. Lehmann, Donald R., 2020. "The evolving world of research in marketing and the blending of theory and data," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 27-42.
    16. Jorge M. Silva-Risso & Randolph E. Bucklin & Donald G. Morrison, 1999. "A Decision Support System for Planning Manufacturers' Sales Promotion Calendars," Marketing Science, INFORMS, vol. 18(3), pages 274-300.
    17. repec:dgr:rugsom:97b32 is not listed on IDEAS

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