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Semiparametric model average prediction in panel data analysis

Tao Huang and Jialiang Li

Journal of Nonparametric Statistics, 2018, vol. 30, issue 1, 125-144

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.

Date: 2018
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Handle: RePEc:taf:gnstxx:v:30:y:2018:i:1:p:125-144