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Smoothing Spline as a Guide to Elaborate Explanatory Modeling

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Predictive Econometrics and Big Data (TES 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 753))

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Abstract

Although there are substantial theoretical and empirical differences between explanatory modeling and predictive modeling, they should be considered as two dimensions. And predictive modeling can work as a “fact check” to propose improvements to existing explanatory modeling. In this paper, I use smoothing spline, a nonparametric calibration technique which is originally designed to intensify the predictive power, as a guide to revise explanatory modeling. It works for the housing value model of Harrison and Rubinfeld (1978) because the modified model is more meaningful and fits better to actual data.

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Notes

  1. 1.

    This part is to a large extent based on Shmueli (2010).

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Correspondence to Chon Van Le .

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Van Le, C. (2018). Smoothing Spline as a Guide to Elaborate Explanatory Modeling. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-70942-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70941-3

  • Online ISBN: 978-3-319-70942-0

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