This paper researches the application of grey system theory in cost forecasting of the coal mine. The grey model (GM(1.1)) is widely used in forecasting in business and industrial systems with advantages of minimal data, a short time and little fluctuation. Also, the model fits exponentially with increasing data more precisely than other prediction techniques. However, the traditional GM(1.1) model suffers from the poor anti-interference ability. Aimed at the flaws of the conventional GM(1.1) model, this paper proposes a novel dynamic forecasting model with the theory of background value optimization and Fourier-series residual error correction based on the traditional GM(1.1) model. The new model applies the golden segmentation optimization method to optimize the background value and Fourier-series theory to extract periodic information in the grey forecasting model for correcting the residual error. In the proposed dynamic model, the newest data is gradually added while the oldest is removed from the original data sequence. To test the new model’s forecasting performance, it was applied to the prediction of unit costs in coal mining, and the results show that the prediction accuracy is improved compared with other grey forecasting models. The new model gives a MAPE & C value of 0.14% and 0.02, respectively, compared to 1.75% and 0.37 respectively for the traditional GM(1.1) model. Thus, the new GM(1.1) model proposed in this paper, with advantages of practical application and high accuracy, provides a new method for cost forecasting in coal mining, and then help decision makers to make more scientific decisions for the mining operation.
The aim of the study is to discuss the relationship of the crude oil price, speculative activity and fundamental factors. An empirical study was conducted with a VEC model. Two cointegrating vectors were identified. The first vector represents the speculative activity. We argue that the number of short non-commercial positions increases with the crude oil stock and price, decreases with the higher number of long non-commercial positions. A positive trend of crude oil prices may be a signal for traders outside the industry to invest in the oil market, especially as access to information could be limited for them. The second vector represents the crude oil price under the fundamental approach. The results support the hypothesis that the crude oil price is dependent on futures trading. The higher is a number of commercial long positions, the greater is the pressure on crude oil price to increase.
This paper addresses the issue of obtaining maximum likelihood estimates of parameters for structural VAR models with a mixture of distributions. Hence the problem does not have a closed form solution, numerical optimization procedures need to be used. A Monte Carlo experiment is designed to compare the performance of four maximization algorithms and two estimation strategies. It is shown that the EM algorithm outperforms the general maximization algorithms such as BFGS, NEWTON and BHHH. Moreover, simplification of the problem introduced in the two steps quasi ML method does not worsen small sample properties of the estimators and therefore may be recommended in the empirical analysis.
The paper provides analysis of the influence of temperature on the error of weigh-in-motion (WIM) systems utilizing piezoelectric polymer load sensors. Results of tests of these sensors in a climatic chamber, as well as results of long-term tests at the WIM site, are presented. Different methods for correction of the influence of changes in temperature were assessed for their effectiveness and compared.
Time-interleaved analog-to-digital converter (ADC) architecture is crucial to increase the maximum sample rate. However, offset mismatch, gain mismatch, and timing error between time-interleaved channels degrade the performance of time-interleaved ADCs. This paper focuses on the gain mismatch and timing error. Techniques based on Discrete Fourier Transform (DFT) for estimating and correcting gain mismatch and timing error in an M-channel ADC are depicted. Numerical simulations are used to verify the proposed estimation and correction algorithm.
The economy of Slovakia experienced a turning point in the 1st half of 2008 and entered a phase of decline. The negative impacts of the global economic crisis became evident in the 2nd half of 2008 and led into a recession in the 1st quarter of 2009. The composite leading indicator was originally intended for forecasting of business cycle turning points between the decline and growth phases. The aim of this paper is to transform the qualitative information from composite leading indicator into quantitative forecast and verify whether the beginning of recession in Slovakia could have been identied in advance. The ARIMAX and error correction models are used for the composite reference series and GDP forecasts respectively. The nal result shows that the composite leading indicator is useful not only for identifying turning points, but also for the prediction of recession phase.
We develop a fully Bayesian framework for analysis and comparison of two competing approaches to modelling daily prices on different markets. The first approach, prevailing in financial econometrics, amounts to assuming that logarithms of prices behave like a multivariate random walk; this approach describes logarithmic returns most often by the VAR(1) model with MGARCH (or sometimes MSV) disturbances. In the second approach, considered here, it is assumed that daily price levels are linked together and, thus, the error correction term is added to the usual VAR(1)–MGARCH or VAR(1)–MSV model for logarithmic returns, leading to a reduced rank VAR(2) specification for logarithms of prices. The model proposed in the paper uses a hybrid MSV-MGARCH structure for VAR(2) disturbances. In order to keep cointegration modelling as simple as possible, we restrict to the case of two prices representing two different markets. The aim of the paper is to show how to check if a long-run relationship between daily prices exists and whether taking it into account influences our inference on volatility and short-run relations between returns on different markets. In the empirical example the daily values of the S&P500 index and the WTI oil price in the period 19.12.2005 – 30.09.2011 are jointly modelled. It is shown that, although the logarithms of the values of S&P500 and WTI oil price seem to be cointegrated, neglecting the error correction term leads to practically the same conclusions on volatility and conditional correlation as keeping it in the model.
Sample-time errors can greatly degrade the dynamic range of a time-interleaved sampling system. In this paper, a novel correction technique employing a cubic spline interpolation is proposed for inter-channel sample-time error compensation. The cubic spline interpolation compensation filter is developed in the form of a finite-impulse response (FIR) filter structure. The correction method of the interpolation compensation filter coefficients is deduced. A 4GS/s two-channel, time-interleaved ADC prototype system has been implemented to evaluate the performance of the technique. The experimental results showed that the correction technique is effective to attenuate the spurious spurs and improve the dynamic performance of the system.
The purpose of the article is to verify a hypothesis about the asymmetric pass-through of crude oil prices to the selling prices of refinery products (unleaded 95 petrol and diesel oil). The distribution chain is considered at three levels: the European wholesale market, the domestic wholesale market and the domestic retail market. The error correction model with threshold cointegration proved to be an appropriate tool for making an empirical analysis based on the Polish data. As found, price transmission asymmetry in the fuel market is significant and its scale varies depending on the level of distribution. The only exception is the wholesale price transmission to the domestic refinery price. All conclusions are supported by the cumulative response functions. The analysis sheds new light on the price-setting processes in an imperfectly competitive fuel market of a medium-sized, non-oil producing European country in transition.
The national total expenditure of a country is precipitated on several factors of which revenue generated could be one and very significant. This paper therefore examines the contribution of some selected sources of Nigerian government revenue to total national expenditure. Statistical and econometric techniques used for the data analysis are unit root test, cointegration test, combined estimators’ analysis, the error correction model (ECM) and the feasible generalized linear (FGLS) estimators. Results showed that the variables are non stationary but are stationary at first difference. The long-run relationship of total expenditure on oil revenue, non-oil revenue, federation account and federal retained revenue revealed that the variables are co-integrated and required the use of combined estimators. The effect of non-oil revenue and federal retained revenue is very significant. Investigations on the short-run modeling necessitated the use of FGLS estimators. The effect of ECM and federal retained revenue is very significant. Consequently, other sources of revenue apart from federal retained revenue need to be enhanced and tailored towards improving economic growth and development through national expenditure.