This paper assesses the performance of a number of long-term interest rate forecast approaches, namely time series models, structural economic models, expert forecasts and combinations thereof. The predictive performance of these approaches is compared using out of sample forecast errors, where a random walk forecast acts as benchmark. It is found that for five major OECD countries, namely Unit...

In applied forecasting, there is a trade-off between in-sample fit and out-ofsample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, there is often predictable information in forecast errors that is difficult to exploit. However, we show how this predictable information can be exploited in forecast combinations. In this case, o...

This paper proposes a generalized on-line risk-reward model, by introducing the notion of the probabilistic forecast. Using this model, we investigate the on-line rental problem. We design the risk rental algorithms under the basic probability forecast and the geometric distribution probability forecast, respectively. In contrast to the existing competitive analyses of the on-line rental proble...

Temporally distributed deterministic and stochastic excitation of the tangent linear forecast system governing forecast error growth and the tangent linear observer system governing assimilation error growth is examined. The method used is to determine the optimal set of distributed deterministic and stochastic forcings of the forecast and observer systems over a chosen time interval. Distribut...

during the recent decades, rapid urbanization growth has led to even faster growth of motorvehicles and especially in large cities. hence, evaluation of the actual level of traffic emissions has gained more interest. this paper, for the first time, presents a bottom-up approach for evaluation of vehicular emissions in tehran- the capital of iran- using the international vehicle emission (ive) m...

Four different error-forecast updating models are investigated in terms of their capability of providing real-time river flow forecast accuracy superior to that of rainfall-runoff models applied in the simulation (nonupdating) mode. The first and most widely used is the single autoregressive (AR) model, the second being an elaboration of that model, namely the autoregressive-threshold (AR-TS) u...

Fine-grained device-level predictions of both shiftable and nonshiftable energy demand and supply is vital in order to take advantage of Demand Response (DR) for efficient utilization of Renewable Energy Sources. The selection of an effective device-level load forecast model is a challenging task, mainly due to the diversity of the models and the lack of proper tools and datasets that can be us...

This paper proposes an exchange rate forecasting method by using the grey relative combination approach of chaos wavelet SVM-Markov model. The problem of short-term forecast of exchange rate by using the comprehensive method of the phase space reconstitution and SVM method has been researched We have suggested a wavelet-SVR-Markov forecasting model to predict the finance time series and demonst...

The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter Ci and ei assigned to each training data, the flexible Ci Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. F...

Efficient market hypothesis predicts that capital markets are beset with cer-tain biases which result from wrong estimation, and negatively influence shareholders’ expectations for higher returns, which in turn affects invest-ment efficiency, financial constraints and corporate performance efficacy in competitive markets, and eventually mitigates firm value. The present study aims at examining ...