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Semiparametric model average prediction in panel data analysis. (English) Zbl 1388.62111
Authors’ abstract: Forecasting in economic data analysis is dominated by linear prediction methods where the predicted values are calculated from a fitted linear regression model. With multiple predictor variables, multivariate nonparametric models were proposed in the literature. However, empirical studies indicate the prediction performance of multi-dimensional nonparametric models may be unsatisfactory. We propose a new semiparametric model average prediction (SMAP) approach to analyse panel data and investigate its prediction performance with numerical examples. Estimation of individual covariate effect only requires univariate smoothing and thus may be more stable than previous multivariate smoothing approaches. The estimation of optimal weight parameters incorporates the longitudinal correlation and the asymptotic properties of the estimated results are carefully studied in this paper.
MSC:
62G08 Nonparametric regression and quantile regression
62F12 Asymptotic properties of parametric estimators
62M20 Inference from stochastic processes and prediction
62G20 Asymptotic properties of nonparametric inference
Software:
hgam
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