Abstract
Classical statistical approaches used widely in econometrics centering around parameter estimation, hypothesis testing, and p-values should be abandoned. In their place, predictive modeling should be used. A predictive model answer the question all users of statistics have: if I change x, or leave it out of my model, what does this do to the uncertainty in y? Classical methods never answer that question directly. The reason why this is so, and why the predictive approach does, is shown.
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Briggs, W.M. (2018). Testing, Prediction, and Cause in Econometric Models. In: Anh, L., Dong, L., Kreinovich, V., Thach, N. (eds) Econometrics for Financial Applications. ECONVN 2018. Studies in Computational Intelligence, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-73150-6_1
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DOI: https://doi.org/10.1007/978-3-319-73150-6_1
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