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Forecasters have frequently been concerned with designing seasonal adjustment procedures that satisfy particular theoretical criteria (e.g. orthogonality, idempotency, symmetry, Lovell). In evaluating the merits of a particular technique, Monte Carlo studies are often undertaken and the results are then compared to those derived from the Census Bureau's X—11 routine (Wallis, Stephenson, Grether). However, many practical questions have not been addressed, such as to what extent can seasonal routines affect parameter estimates, forecast values, and policy scenarios? The purpose of this article is to focus upon these questions. Data from a short‐term petroleum demand model is seasonally adjusted six different ways. The seasonally adjusted data is then used to estimate the demand relationships of the model using the same structural equation in each case. The results of these estimations provide illuminating information about how seasonality affects parameter values. For policy purposes, this information can be crucial as various policies can be predicated upon an estimated response to a particular variable (e.g. the price of gasoline). The question answered here is how sensitive are the expected policy results to the type of seasonal routine employed in making the estimations.

The reflectance spectra of natural and man‐made surfaces are highly constrained. Statistical analyses have been conducted that confirm that the surface reflectance spectra form a set of band‐limited functions with a frequency limit of approximately 0.02 cycles/nm. The reflectance spectra can be represented by a linear‐model framework and are adequately described by 6‐12 basis functions. However, the spectral properties of surfaces are not so constrained as to allow the human visual system to recover the surface properties from cone excitations. Furthermore, trichromatic colour devices such as scanners and cameras can only capture illumination‐specific colour information.

This paper aims to overcome the defect that the traditional clustering method is excessively dependent on initial clustering radius and also provide new technical measures for detecting the component content of lubricating oil based on the fuzzy neural system model.

According to the layers model of the fuzzy neural system model for the given sample data pair, the new clustering method can be implemented, and through the fuzzy system model, the detection method for the selected oil samples is given. By applying this method, the composition contents of 30 kinds of oil samples in lubricating oil are checked, and the actual composition contents of oil samples are compared.

Through the detection of 21 mineral elements in 30 oil samples, it can be known that the four mineral elements such as Zn, P, Ca and Mg have largest contribution rate to the lubricating oil, and they can be regarded as the main factors for classification of lubricating oil. The results show that the fuzzy system to be established based on sample data clustering has better performance in detection lubricant component content.

In spite of lots of methods for detecting the component of lubricating oil at the present, there is still no detection of the component of lubricating oil through clustering method based on sample data pair. The new nearest clustering method is proposed in this paper, and it can be more effectively used to detect the content of lubricating oil.

This paper aims to investigate the cyclical associations among energy prices and key macroeconomic variables for the USA.

To this end, the recently developed Hamilton filter (HF) and the oft-used Hodrick–Prescott filter (HPF) are used. The two methods produce starkly different results regarding the relationships between energy prices on the one hand and output and employment on the other.

While the HF suggests that energy prices are acyclical, the HPF suggests they are procyclical. However, the associations between energy prices and inflation are robust across the two methods, indicating that energy prices are strongly correlated with – and lead – the consumer price index (CPI). Furthermore, unlike the results produced by the HPF, those produced by the HF are robust across seasonally adjusted and unadjusted data.

Given the inherent seasonality in energy prices and the differences in the underlying processes that generate macroeconomic and energy prices, the results obtained from the HPF filter should be interpreted with caution.

To the best of the authors’ knowledge, this is the first study that uses the recently developed HF to examine the associations between the cyclical behaviors of three key macroeconomic variables in the USA – the industrial production index, the CPI, and total nonfarm employment – and the prices of natural gas, crude oil, gasoline, diesel, and heating oil. Second, this study presents a comparison of the results produced by the two filtering techniques. Third, recognizing that energy prices are characterized by seasonality, this study tests the robustness of the results produced by the two filters across seasonally adjusted and unadjusted data.

Polybisphenol maleate was prepared by the reaction of bisphenol diacetate and maleic acid and its molecular weight, relative viscosity, solubility and melting points were determined. Its structure was confirmed by spectral and chemical analysis. It was incorporated physically and chemically into linseed oil alkyds and resulting products were termed as physical blends and copolyesters respectively. Film properties of blends and copolyesters were studied and compared with those of plain linseed oil alkyds. It was found that copolyesters were superior in film properties to those of plain linseed oil alkyds, and blends were up to some extent.

This paper aims to investigate the transmission of international oil prices to the stock market indices of the Gulf Cooperation Council (GCC) countries over the weekly period from April 07, 2004, to August 15, 2018.

The authors use the augmented Dickey–Fuller (ADF) unit root test to check the order of integration of data series. Afterward, the authors use the ordinary least square method to determine the spillover of international oil prices to the stock markets of GCC countries while accounting for the time-varying volatility of oil and stock market returns through the generalized autoregressive conditional heteroskedasticity. Then, the Johansen (1991) cointegration test is used to determine the long-run equilibrium relationship. Finally, the Granger (1969) causality test is used to determine the short-run causal effects between oil and the stock markets returns of GCC countries.

The findings indicate that the stock markets of GCC countries are efficient and respond significantly to international oil prices and evidence of high volatility associated with oil returns.

Investors and portfolio managers should consider the association between international oil prices and GCC stock returns when allocating their funds for diversification strategy. Moreover, policymakers should better understand the behavior of local stock markets.

The purpose of this paper is to check the actual life of lubricating oil.

Present work aims to find the remaining useful life of the lubricant based on study of periodic deterioration of oil. Chronological samples of oil were selected from the dumper of a local open cast mine. The deterioration in oil was studied using Fourier transform infrared (FTIR) spectroscopy.

The data obtained from FTIR spectroscopy was used in vector projection approach and analytical hierarchy process to evaluate the remaining useful life of the lubricating oil.

FTIR spectra were used to study the periodic deterioration of oil. IR radiation with all frequencies in the range was passed through the sample. Radiations at certain frequency, depending upon the molecular structure of compounds in the sample were absorbed and rest was transmitted by the sample. A spectrum representing molecular absorption or transmission was obtained. Transmission spectra have been used in the study. Comparing the percent value of transmission peak of different chronological sample with that of fresh oil was used to represent the periodic degradation in oil.

Purpose – This research explores how social movement activists work to influence the framing of oil spill impacts, and related scientific and political processes. It focuses on the Louisiana Bucket Brigade (LABB), an environmental justice organization that has worked in the Gulf Coast, and looks particularly at the experience of the 2010 Deepwater Horizon Oil Spill.

Design/methodology – Research is based on qualitative interviews, ethnographic observations, and video data with local social movement organizations, grassroots groups, spill workers, fishermen, local residents, scientists, and government representatives during three time periods, in 2010 within five months of the spill, Fall of 2011, and Summer of 2012.

Findings – Legal institutional constrictions inherent in official oil spill assessments and cleanup processes fostered a transformation in activist tactics and the communities they seek to represent.

Originality/value of the paper – Social movement activism has not often been studied in response to an oil spill. This chapter demonstrates how such an event shapes activism, and how activism has an effect on local responses to the event.

This paper aims to focus on a better model to capture the trait of varying volatility in various financial time series, as well as to obtain reliable estimate of value at risk (VaR).

The typical methods in spectral analysis are used to obtain the sample of conditional mean, conditional variance and residual term. The generalized regression neural network is used to establish a time-varying non-linear model, and the non-parametric kernel density estimation method is applied for the estimation of VaR.

The proposed model is able to follow the heteroscedastic characteristic which is common in financial time series, and the estimated VaR is satisfactory.

The analysis method in this study allows the hedgers, bankers, financial analysts as well as economists to draw a better inference from financial time series. Also, relatively more precise estimate of the VaR value for a certain kind of financial asset is available. The model with its derived estimates would definitely help in developing other models.

Up-to-date, the study in literature which models financial time serial from the viewpoint of spectral analysis is rare to see. Thus, the proposed model, along with a comprehensive empirical study which reveals desirable result for the estimation of VaR would enrich the related researches at present.