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Financial Panel Data Models, Strict Versus Contemporaneous Exogeneity, and Durbin-Wu-Hausman Specification Tests

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Encyclopedia of Finance

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Abstract

Panel data, pooling cross-sectional and time series data, is increasingly used in estimating financial models. This chapter presents estimators for pooled, fixed effects (FE), and random effects (RE) linear panel data models, assumptions on which they are based, particularly contemporaneous and strict exogeneity assumptions on the explanatory variables, and model specification testing (DWH and Wooldridge tests). Implications of estimator and specification choice on parameter consistency and standard error efficiency are developed. The relationship between the approaches, choice of approach, and their advantages and disadvantages are discussed. Examples illustrate application of the estimators and tests, applying the concepts and testing specifications to illustrate their use and interpretation.

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Notes

  1. 1.

    Any panel data model with a lagged dependent variable as an explanatory variable and cross-sectional unobserved effects will by specification violate strict exogeneity.

  2. 2.

    The three journals Grieser and Hadlock (2019) searched were the Journal of Finance, Journal of Financial Economics, and the Review of Financial Studies from 2006 to 2013, with an updated search of the Journal of Finance for 2017.

  3. 3.

    Linear is used here to imply that parameters to be estimated enter the model linearly, rather than in a nonlinear fashion. The techniques covered here are applicable to nonlinear in variables models, but not nonlinear in parameters models.

  4. 4.

    See Engle, Hendry, and Richard (1983) for additional detail. Often exogenous is used to imply strict exogeneity (e.g., Davidson and MacKinnon 2004).

  5. 5.

    Strict exogeneity may also be an issue in time series models.

  6. 6.

    If the error term for cross-sect. i, conditional on the explanatory variables, is independent of being selected into the sample, random can be relaxed (random is actually a stronger assumption than is needed).

  7. 7.

    More precisely, as N and/or T →  ∞ .

  8. 8.

    These other results in the literature are beyond the scope of this chapter so are not covered further here. Pesaran (2015), Chamberlain (1984), Cameron and Trivedi (2005), Wooldridge (2010), Hsiao (2014), Greene (2018), and Baltagi (2021) provide detail.

  9. 9.

    Grieser and Hadlock (2019) use simulations to show that these tests have “substantial” power.

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Authors and Affiliations

  1. Professor Emeritus, Finance and Economics, Rutgers University, Newark, NJ, USA

    Robert H. Patrick

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  1. Robert H. Patrick
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Correspondence to Robert H. Patrick .

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Editors and Affiliations

  1. Rutgers School of Business, Rutgers, The State University of New Jer, Piscataway, NJ, USA

    Cheng-Few Lee

  2. Phoenix, AZ, USA

    Alice C. Lee

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Patrick, R.H. (2021). Financial Panel Data Models, Strict Versus Contemporaneous Exogeneity, and Durbin-Wu-Hausman Specification Tests. In: Lee, CF., Lee, A.C. (eds) Encyclopedia of Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-73443-5_78-1

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  • DOI: https://doi.org/10.1007/978-3-030-73443-5_78-1

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