This paper aims at improving the prediction accuracy through using combining forecasts approaches. In forecast combination, the crucial issue is the selection of the weights to be assigned to each model. In addition to traditional methods, we propose, also, two sophisticated approaches. These suggested methods are modified Bayesian Moving Average (BMA) and Extended Time-varying coefficient (ETVC). The first technique is based on merging the traditional BMA with other frequentist combination schemes to avoid the subjective prior inside the traditional Bayesian technique. The suggested ETVC approach provides consistent time-varying parameters even if there are some measurement errors, omitted variables bias and if the true functional form is unknown. Concerning the included models, we consider both linear and nonlinear models in order to calculate the forecasts of quarterly Egyptian CPI inflation. We find that our proposed scheme ETVC is superior to the best model and all other static combination schemes including the time-varying scheme based on the random walk coefficients updated (TVR) approach. Additionally, the suggested modified Bayesian approach improves the traditional BMA and overcomes the problem of depending on the arbitrary choice for the initial priors