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Applied Nonparametric Regression Analysis: the Choice of Generalized Additive Models

Listed author(s):
  • Morteza Haghiri


    (Memorial University ┬ĘC Corner Brook, Canada)

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    Literature has documented tremendous changes in classical regression analysis techniques since 1980s. The drawbacks of simple and multiple parametric regression analyses on model specifications and the non-robust assumption of error terms followed by the introduction of a series of diagnostic tests to fix these inevitable pitfalls have made econometricians to develop new methodologies in nonparametric and semi-parametric regressions that either do not have or mitigate the major shortcomings of what their traditional counterparts inherently demonstrate. The development of the generalized linear models followed by the introduction of generalized additive models and generalized additive mixed models has attracted practitioners to use these methodologies in applied studies. The main objective of this paper is to conduct a comprehensive survey on studies that used generalized additive models as econometric models and show how the parameters of these models are estimated. In particular, it briefly reviews the theory of generalized additive models, and then introduces various techniques to estimate the parameters of the models. Finally, it presents a comprehensive review of studies in which generalized additive models are specified as the econometric model.

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    Article provided by Better Advances Press, Canada in its journal Review of Economics & Finance.

    Volume (Year): 3 (2013)
    Issue (Month): (February)
    Pages: 25-34

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    Handle: RePEc:bap:journl:130103
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    1. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355, March.
    2. Morteza Haghiri & Stephen M. Law & James F. Nolan & Alireza Simchi, 2013. "Comparing non-parametric to parametric interaction terms in generalised additive models: production technology in the Canadian cable television industry," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 5(3), pages 229-251.
    3. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, October.
    4. Morteza Haghiri & Alireza Simchi, 2005. "An application of the residual deviance analysis in testing input separability restrictions," Applied Economics Letters, Taylor & Francis Journals, vol. 12(12), pages 755-758.
    5. Morteza Haghiri & James Nolan & Kien Tran, 2004. "Assessing the impact of economic liberalization across countries: a comparison of dairy industry efficiency in Canada and the USA," Applied Economics, Taylor & Francis Journals, vol. 36(11), pages 1233-1243.
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