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Hypothesis Testing with Model Search

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Empirical Asset Pricing Models
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Abstract

This chapter covers the discussions of model selection tests in empirical asset pricing models with the asymptotic properties developed in Chap. 4. In particular, model selection with forward selection for variables in empirical asset pricing models is introduced. The purpose of this chapter is to consider the sequential model search where model selection tests (or criteria) with additional asymptotic properties for common factors of asset returns are used. Differing from the other empirical studies, the emphasis is on the cross-sectional commonality of these presumed variables or factors when the asset returns are projected onto these variables. Given that the underlying intrinsic mechanism of asset returns is unknown, the sequential model search is to pursue the optimality in approximation that the basic requirement for these presumed variables or factors will satisfy the coherence condition where cross-sectional dependence is persistent.

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Notes

  1. 1.

    Likewise, Hansen et al. (2011) provide the bootstrap applications of MCS procedures since they don’t depend on the orders or sequences of hypothesis tests.

  2. 2.

    Notice that this is only one candidate estimator for the long-run variance. Many other estimates can also be applied and the same convergence in distribution still holds.

  3. 3.

    Notice that the forward-selection sequential model search is not identical to the forward search proposed by Atkinson and Riani (2002). Their approach is based on the increasing subsets of all observations to verify the model.

  4. 4.

    One reason for the orthogonalization is to obtain the estimate of the factor loadings of the newly included proxy—given that these proxies are possibly correlated. This approach is similar to Forsythe et al. (1973) and Billings and Wei (2005).

References

  • Atkinson, A.C., and M. Riani. 2002. Forward Search Added-Variable t-Tests and the Effect of Masked Outliers on Model Selection. Biometrika 89: 939–946.

    Article  Google Scholar 

  • Barmalzan, G., and A.T.P. Najafabadi. 2012. Model Confidence Set based on Kullback-Leibler Divergence Distance. Journal of Statistical Research, Iran 9: 179–193.

    Google Scholar 

  • Billings, S., and H.-L. Wei. 2005. A Multiple Sequential Orthogonal Least Squares Algorithm for Feature Ranking and Subset Selection. ACSE Research Report No. 908.

    Google Scholar 

  • Forsythe, A.B., L. Engelman, R. Jennrich, and P.R.A. May. 1973. A Stopping Rule for Variable Selection in Multiple Regression. Journal of the American Statistical Association 68: 75–77.

    Article  Google Scholar 

  • Hansen, B. 2005. Challenges for Econometric Model Selection. Econometric Theory 21: 60–68.

    Google Scholar 

  • Hansen, P.R., A. Lunde, and J. Nason. 2011. The Model Confidence Set. Econometrica 79: 453–497.

    Article  Google Scholar 

  • Jeng, J.-L. 2015. Analyzing Event Statistics in Corporate Finance: Methodologies, Evidences, and Critiques. Basingstoke: Palgrave Macmillan.

    Book  Google Scholar 

  • Johnson, B. McK., and T. Killeen. 1983. An Explicit Formula for the C.D. F. of the L 1 Norm of the Brownian Bridge. Annals of Probability 11: 807–808.

    Article  Google Scholar 

  • Khan, J.A., S.V. Aelst, and R.H. Zamar. 2007. Building and Robust Linear Model with Forward Selection and Stepwise Procedures. Computational Statistics and Data Analysis 52: 239–248.

    Article  Google Scholar 

  • Ouysse, R. 2006. Consistent Variable Selection in Large Panels when Factors are Observable. Journal of Multivariate Analysis 97: 946–984.

    Article  Google Scholar 

  • Vuong, Q.H. 1989. Likelihood Ratio Test for Model Selection and Non-Nested Hypotheses. Econometrica 57: 307–333.

    Article  Google Scholar 

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Jeng, JL. (2018). Hypothesis Testing with Model Search. In: Empirical Asset Pricing Models. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-74192-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-74192-5_5

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  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-319-74191-8

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