Combination schemes for turning point prediction

We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by autoregressive (AR) and Markov-Switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach to both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and Euro area business cycles

1.3-μm Quantum-well InGaAsP, AlGaInAs, and InGaAsN laser material gain: A theoretical study

Due to the keen interest in improving the high-speed and high-temperature performance of 1.3-μm wavelength lasers, we compare, for the first time, the material gain of three different competing active layer materials, namely InGaAsP-InGaAsP, AlGaInAs-AlGaInAs, and InGaAsN-GaAs. We present a theoretical study of the gain of each quantum-well material system and present the factors that influence the material gain performance of each system. We find that AIGaInAs and InGaAsN active layer materials have substantially better material gain performance than the commonly used InGaAsP, both at room temperature and at high temperature