Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation

Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation

Modeling and Forecasting Effects of Crude Oil Price Changes on the US and UK GDP

        This paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and GDP of the US, as the largest oil consumer, and the UK, as the oil producer. GMDH neural network and MLFF neural network approaches, which are both non-linear models, are employed to forecast GDP responses to the oil price changes. The resul...

Forecasting Gold Price Changes: Application of an Equipped Artificial Neural Network

The forecast of fluctuations and prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critically favorable to the investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help to make the firm de...

Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK in ation

We discuss computational aspects of likelihood-based speci cation, estimation, inference, and forecasting of possibly nonstationary series with long memory. We use the arfima(p; d; q) model with deterministic regressors and we compare sampling characteristics of approximate and exact rst-order asymptotic methods. We extend the analysis using a higher-order asymptotic method, suggested by Cox an...

Modeling and Forecasting Iranian Inflation with Time Varying BVAR Models

This paper investigates the forecasting performance of different time-varying BVAR models for Iranian inflation. Forecast accuracy of a BVAR model with Litterman’s prior compared with a time-varying BVAR model (a version introduced by Doan et al., 1984); and a modified time-varying BVAR model, where the autoregressive coefficients are held constant and only the deterministic components are allo...