While forecasting is a common practice in academia, government and business alike, practitioners are often left wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2018, for example, should we use the last 30 years of data or the last 10 years of data? There is strong evidence of structural changes in economic time series, and the forecasting performance is often quite sensitive to the choice of such window size. In this paper, we develop a novel method for selecting the estimation window size for forecasting. Specifically, we propose to choose the optimal window size that minimizes the forecaster's quadratic loss function, and we prove the asymptotic validity of our approach. Our Monte Carlo experiments show that our method performs quite well under various types of structural changes. When applied to forecasting US real output growth and inflation, the proposed method tends to improve upon conventional methods, especially for output growth.

The forced vibration of a sandwich beam integrating a shear thickening fluid (STF) core and with conductive skins subjected to a periodic excitation was investigated theoretically in this study. The rheological properties of the STF material including viscosity, plasticity, and elasticity may be changed under the periodic vibration, and hence they were considered. The governing equation of motion was derived based on the complex stiffness method and some key parameters were derived based on the Timoshenko beam theory. Effects of the excitation frequency, the excitation amplitude, the excitation location, and the skin/core thickness ratio on the nature frequency of the sandwich beam were investigated. It was found that the STF core has a significant effect on the dynamic property of the sandwich beam. Based on the findings, integrating the STF core in a sandwich beam can reduce the vibration of the beam.

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