The paper considers two types of models for forecasting seasonally adjusted Russian GDP under the structural breaks. Models that allow breaks in a deterministic trend, which dates are set exogenously, and more flexible class – with stochastic trend considered. It is shown modeling break or adding significantly improves quality 3–4 steps ahead forecasts, sometimes even on shorter horizons, compa...

Abstract In this paper, income per capita convergence hypothesis is tested in selected OIC countries. For this purpose, we use the time series model and univariate KPSS stationary test with multiple structural breaks (Carrion-i-Silvestre et al. (2005)) over the period 1950-2008. The results show that most OIC countries could not catch up toward USA. Although because of some positive term of tra...

Structural breaks and existence of outliers in time series variables results in misleading forecasts. We forecast wheat and rice prices by capturing the exogenous breaks and outliers using Automatic modeling. The procedure identifies the outliers as the observations with large residuals. The suggested model is compared on the basis of Root Mean Square Error (RMSE) and Mean Absolute Percentage E...

Abstract I n this paper, the behavior of the real oil price and OPEC and non-OPEC oil production during 1973-2013 are modelled. Interactions among OPEC, non-OPEC oil production, global oil consumption, and the real price of crude oil are estimated using a Structural VAR model (SVAR). After providing evidence for the structural breaks in oil price in 1996, the results indicate that, ac...

In this paper, we tested the catching-up hypothesis toward the USA using Becker et al. (2006) flexible Fourier KPSS stationary test over the period 1960-2009. The mentioned test could control for unknown number and form of structural breaks using a selected frequency component of a Fourier function. Our results show almost poor countries stay poor and almost rich countries stay rich. South Kore...

We propose a new approach to forecasting stock returns in the presence of structural breaks that simultaneously affect the parameters of multiple portfolios. Exploiting information in the cross-section increases our ability to identify breaks in return prediction models and enables us to detect breaks more rapidly in real time, thereby allowing the parameters of the predictive return regression...