Title of article :
Nonparametric and semiparametric regressions subject to monotonicity constraints: Estimation and forecasting
Author/Authors :
Lee، نويسنده , , Tae-Hwy and Tu، نويسنده , , Yundong and Ullah، نويسنده , , Aman، نويسنده ,
Pages :
15
From page :
196
To page :
210
Abstract :
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 , Equity premium prediction , Asymptotic mean squared errors , Second order stochastic dominance
Journal title :
Astroparticle Physics
Record number :
2042132
Link To Document :
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