Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting
Tae Hwy Lee,
Yundong Tu and
No 201404, Working Papers from University of California at Riverside, Department of Economics
This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang (2001). Asymptotic properties of our proposed estimators and forecasts are established. Monte Carlo simulation is conducted to show their finite sample performance. An application to predicting equity premium is taken for illustration. We introduce a new forecasting evaluation criterion based on the second order stochastic dominance in the size of forecast errors and compare models over different sizes of forecast errors. Imposing monotonicity constraint can mitigate the chance of making large size forecast errors.
Keywords: Local monotonicity; Bagging; Asymptotic mean squared errors; Second order stochastic dominance; Equity premium prediction. (search for similar items in EconPapers)
JEL-codes: C14 C50 C53 G17 (search for similar items in EconPapers)
Pages: 38 Pages
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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Published in Journal of Econometrics 182(1): 196-210. September 2014.
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https://economics.ucr.edu/repec/ucr/wpaper/201404.pdf First version, 2014 (application/pdf)
Journal Article: Nonparametric and semiparametric regressions subject to monotonicity constraints: Estimation and forecasting (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201404
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