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Article
Publication date: 2 October 2017

Ajay Kumar Dhamija, Surendra S. Yadav and P.K. Jain

The purpose of this paper is to find out the best method for forecasting European Union Allowance (EUA) returns and determine its price determinants. The previous studies…

Abstract

Purpose

The purpose of this paper is to find out the best method for forecasting European Union Allowance (EUA) returns and determine its price determinants. The previous studies in this area have focused on a particular subset of EUA data and do not take care of the multicollinearities. The authors take EUA data from all three phases and the continuous series, adopt the principal component analysis (PCA) to eliminate multicollinearities and fit seven different homoscedastic models for a comprehensive analysis.

Design/methodology/approach

PCA is adopted to extract independent factors. Seven different linear regression and auto regressive integrated moving average (ARIMA) models are employed for forecasting EUA returns and isolating their price determinants. The seven models are then compared and the one with minimum (root mean square error is adjudged as the best model.

Findings

The best model for forecasting the EUA returns of all three phases is dynamic linear regression with lagged predictors and that for forecasting EUA continuous series is ARIMA errors. The latent factors such as switch to gas (STG) and clean spread (capturing the effects of the clean dark spread, clean spark spread, switching price and natural gas price), National Allocation Plan announcements events, energy variables, German Stock Exchange index and extreme temperature events have been isolated as the price determinants of EUA returns.

Practical implications

The current study contributes to effective carbon management by providing a quantitative framework for analyzing cap-and-trade schemes.

Originality/value

This study differs from earlier studies mainly in three aspects. First, instead of focusing on a particular subset of EUA data, it comprehensively analyses the data of all the three phases of EUA along with the EUA continuous series. Second, it expressly adopts PCA to eliminate multicollinearities, thereby reducing the error variance. Finally, it evaluates both linear and non-linear homoscedastic models incorporating lags of predictor variables to isolate the price determinants of EUA.

Details

Journal of Advances in Management Research, vol. 14 no. 4
Type: Research Article
ISSN: 0972-7981

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Abstract

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Applying Partial Least Squares in Tourism and Hospitality Research
Type: Book
ISBN: 978-1-78756-700-9

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Book part
Publication date: 21 November 2014

Purevdorj Tuvaandorj and Victoria Zinde-Walsh

We consider conditional distribution and conditional density functionals in the space of generalized functions. The approach follows Phillips (1985, 1991, 1995) who…

Abstract

We consider conditional distribution and conditional density functionals in the space of generalized functions. The approach follows Phillips (1985, 1991, 1995) who employed generalized functions to overcome non-differentiability in order to develop expansions. We obtain the limit of the kernel estimators for weakly dependent data, even under non-differentiability of the distribution function; the limit Gaussian process is characterized as a stochastic random functional (random generalized function) on the suitable function space. An alternative simple to compute estimator based on the empirical distribution function is proposed for the generalized random functional. For test statistics based on this estimator, limit properties are established. A Monte Carlo experiment demonstrates good finite sample performance of the statistics for testing logit and probit specification in binary choice models.

Details

Essays in Honor of Peter C. B. Phillips
Type: Book
ISBN: 978-1-78441-183-1

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Article
Publication date: 10 April 2019

Patricio Esteban Ramírez-Correa, Elizabeth E. Grandón and Jorge Arenas-Gaitán

The purpose of this paper is to determine differences in customers’ personal disposition to online shopping.

Abstract

Purpose

The purpose of this paper is to determine differences in customers’ personal disposition to online shopping.

Design/methodology/approach

The research model was proposed based on two types of purchases (hedonic vs utilitarian) and on personal traits of individuals against technology throughout the Technology Readiness Index (TRI) 2.0. Generation and gender were considered to evaluate their impact on the type of purchases. Consumers’ data were collected in Chile through 788 face-to-face surveys. The partial least squares approach was used to test the research model.

Findings

The findings show that optimism and discomfort influence online shopping. Moreover, generation and gender moderate the relationship between the dimensions of the TRI and online purchases.

Originality/value

The contributions of this study are threefold. The analysis of personal traits and the type of purchases contribute to the existing literature on consumer behavior and e-commerce, and provide some insights for marketers to identify segmentation strategies by analyzing the gender and generation of individuals. Second, this study contributes to examining the stability and invariances of the TRI 2.0 instrument, which has not been fully revised in less developed countries. Third, this study adds to the existing body of research that argues that demographic variables are not sufficient to understand technology adoption by individuals by including psychological variables.

Details

Industrial Management & Data Systems, vol. 119 no. 4
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 15 May 2019

Haoqiang Shi, Shaolin Hu and Jiaxu Zhang

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value…

Abstract

Purpose

Abnormal changes in temperature directly affect the stability and reliability of a gyroscope. Predicting the temperature and detecting the abnormal change is great value for timely understanding of the working state of the gyroscope. Considering that the actual collected gyroscope shell temperature data have strong non-linearity and are accompanied by random noise pollution, the prediction accuracy and convergence speed of the traditional method need to be improved. The purpose of this paper is to use a predictive model with strong nonlinear mapping ability to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

Design/methodology/approach

In this paper, an double hidden layer long-short term memory (LSTM) is presented to predict temperature data for the gyroscope (including single point and period prediction), and the evaluation index of the prediction effect is also proposed, and the prediction effects of shell temperature data are compared by BP network, support vector machine (SVM) and LSTM network. Using the estimated value detects the abnormal change of the gyroscope.

Findings

By combined simulation calculation with the gyroscope measured data, the effect of different network hyperparameters on shell temperature prediction of the gyroscope is analyzed, and the LSTM network can be used to predict the temperature (time series data). By comparing the performance indicators of different prediction methods, the accuracy of the shell temperature estimation by LSTM is better, which can meet the requirements of abnormal change detection. Quick and accurate diagnosis of different types of gyroscope faults (steps and drifts) can be achieved by setting reasonable data window lengths and thresholds.

Practical implications

The LSTM model is a deep neural network model with multiple non-linear mapping levels, and can abstract the input signal layer by layer and extract features to discover deeper underlying laws. The improved method has been used to solve the problem of strong non-linearity and random noise pollution in time series, and the estimated value can detect the abnormal change of the gyroscope.

Originality/value

In this paper, based on the LSTM network, an double hidden layer LSTM is presented to predict temperature data for the gyroscope (including single point and period prediction), and validate the effectiveness and feasibility of the algorithm by using shell temperature measurement data. The prediction effects of shell temperature data are compared by BP network, SVM and LSTM network. The LSTM network has the best prediction effect, and is used to predict the temperature of the gyroscope to improve the prediction accuracy and detect the abnormal change.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 2
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 8 February 2021

Emrah Dokur, Cihan Karakuzu, Uğur Yüzgeç and Mehmet Kurban

This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural…

Abstract

Purpose

This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process.

Design/methodology/approach

The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (Nh) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated.

Findings

It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and Nh = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time.

Originality/value

The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 40 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 25 June 2021

Ai-Fen Lim, Voon-Hsien Lee, Pik-Yin Foo, Keng-Boon Ooi and Garry Wei–Han Tan

In today’s globalized and heavily industrialized economy, sustainability issues that negatively affect the human population and external environment are on the rise. This…

Abstract

Purpose

In today’s globalized and heavily industrialized economy, sustainability issues that negatively affect the human population and external environment are on the rise. This study aims to investigate a synergistic combination of supply chain management and quality management practices in strengthening the sustainability performance of Malaysian manufacturing firms.

Design/methodology/approach

A total sample of 177 usable surveys was collected. Given the contributions and acceptability of the artificial neural network (ANN) approach in evaluating the findings of this study, this study uses ANN to measure the relationship between each predictor (i.e. supply chain integration [SCI], quality leadership [QL], supplier focus [SF], customer focus (CF) and information sharing [IS]) and the dependent variable (i.e. sustainability performance). Via sensitivity analysis, the relative significance of each predictor variable is ranked based on the normalized importance value.

Findings

The sensitivity analysis indicates that CF has the greatest effect on sustainability performance (SP) with 100% normalized relative importance, followed by QL (75%), IS (61.5%), SF (57.3%) and SCI (46.7%).

Originality/value

The findings of this study have the potential to provide valuable guidance and insights that can help all manufacturing firms enhance their SP from the optimum combination of the selected SCQM practices with a focus on sustainability.

Details

Supply Chain Management: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1359-8546

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Article
Publication date: 12 September 2008

Marco Gianinetto

Cartosat‐1 is the first Indian Remote Sensing satellite, developed for topographic mapping, able to collect in‐track high‐resolution stereo images with a 2.5 m pixel size…

Abstract

Purpose

Cartosat‐1 is the first Indian Remote Sensing satellite, developed for topographic mapping, able to collect in‐track high‐resolution stereo images with a 2.5 m pixel size. In the framework of the Cartosat‐1 Scientific Assessment Programme (C‐SAP), the Politecnico di Milano University (Italy) evaluated the performances of the Cartosat‐1 satellite in the generation of digital terrain models (DTMs) from stereo‐couples. The purpose of this paper is to describe in detail the outcomes for the Salon de Provence (France) test site, with respect to existing standards and products actually used in France and also to provide a comparison with the global Shuttle Radar Topography Mission's DTM freely available from by NASA.

Design/methodology/approach

The Cartosat‐1 data processing was done using the commercial off‐the‐shelf software ENVI®, selected for investigating the capabilities and limits of the system using standard image processing tools, so from the point of view of a typical remote sensing user. The data processing involved the following aspects: data pre‐processing; optimization of the DTM's extraction procedure; analysis of the influence of ground control points' (GCPs) in the generated DTMs; analysis of the influence of the DTM's resolution in the elevation accuracy; and post‐processing refinement.

Findings

When generating relative DTMs an error was observed in elevation of some hundreds of meters. After georeferencing, the root mean square error (RMSE) was between 9.0 and 14.2 m and the LE90 between 16.1 and 19.0 m. When generating absolute DTMs, the optimum number of GCPs was found to be 9, with a regular geometric distribution (4.6 m RMSE and 6.5 m LE90 for 10 m grid cell size). Post‐processing may be applied to enhance results (1.6 m RMSE and 2.0 m LE90 for 10 m grid cell size). In this case, the absolute DTMs fulfilled and also overcame the standards required for the IGNs and Spot Image's Reference 3D®.

Originality/value

This paper describes the outcomes of the C‐SAP led by the International Society for Photogrammetry and Remote Sensing and the Indian Space Research Organisation for evaluating the capabilities of the last Cartosat‐1 satellite. The aim is to provide remote sensing users a comprehensive study about the potentialities and limits of the Cartosat‐1 images for multi‐resolution DTM generation (from 5 to 90 m grid cell size).

Details

Sensor Review, vol. 28 no. 4
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 2 March 2010

Alper Ozun, Atilla Cifter and Sait Yılmazer

The purpose of this paper is to use filtered extreme‐value theory (EVT) model to forecast one of the main emerging market stock returns and compare the predictive…

Abstract

Purpose

The purpose of this paper is to use filtered extreme‐value theory (EVT) model to forecast one of the main emerging market stock returns and compare the predictive performance of this model with other conditional volatility models.

Design/methodology/approach

This paper employs eight filtered EVT models created with conditional quantile to estimate value‐at‐risk (VaR) for the Istanbul Stock Exchange. The performances of the filtered EVT models are compared to those of generalized autoregressive conditional heteroskedasticity (GARCH), GARCH with student‐t distribution, GARCH with skewed student‐t distribution, and FIGARCH by using alternative back‐testing algorithms, namely, Kupiec test, Christoffersen test, Lopez test, Diebold and Mariano test, root mean squared error (RMSE), and h‐step ahead forecasting RMSE.

Findings

The results indicate that filtered EVT performs better in terms of capturing fat‐tails in stock returns than parametric VaR models. An increase in the conditional quantile decreases h‐step ahead number of exceptions and this shows that filtered EVT with higher conditional quantile such as 40 days should be used for forward looking forecasting.

Originality/value

The research results show that emerging market stock return should be forecasted with filtered EVT and conditional quantile days lag length should also be estimated based on forecasting performance.

Details

The Journal of Risk Finance, vol. 11 no. 2
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 12 February 2021

Ahmet Esat Suzer and Aziz Kaba

The purpose of this study is to describe precisely the wind speed regime and characteristics of a runway of an International Airport, the north-western part of Turkey.

Abstract

Purpose

The purpose of this study is to describe precisely the wind speed regime and characteristics of a runway of an International Airport, the north-western part of Turkey.

Design methodology approach

Three different probability distributions, namely, Inverse Gaussian (IG), widely used two-parameter Weibull and Rayleigh distributions in the literature, are used to represent wind regime and characteristics of the runway. The parameters of each distribution are estimated by the pattern search (PS)-based heuristic algorithm. The results are compared with the other three methods-based numerical computation, including maximum-likelihood method, moment method (MoM) and power density method, respectively. To evaluate the fitting performance of the proposed method, several statistical goodness tests including the mostly used root mean square error (RMSE) and chi-squared (X2) are conducted.

Findings

In the light of the statistical goodness tests, the results of the IG-based PS attain better performance than the classical Weibull and Rayleigh functions. Both the RMSE and X2 values achieved by the IG-based PS method lower than that of Weibull and Rayleigh distributions. It exhibits a better fitting performance with 0.0074 for RMSE and 0.58 × 10−4 for X2 for probability density function (PDF) in 2012 and with RMSE of 0.0084 and X2 of 0.74 × 10−4 for PDF in 2013. As regard the cumulative density function of the measured wind data, the best results are found to be Weibull-based PS with RMSE of 0.0175 and X2 of 3.25 × 10−4 in 2012. However, Weibull-based MoM shows more excellent ability in 2013, with RMSE of 0.0166 and X2 of 2.94 × 10−4. Consequently, it is considered that the results of this study confirm that IG-based PS with the lowest error value can a good choice to model more accurately and characterize the wind speed profile of the airport.

Practical implications

This paper presents a realistic point of view regarding the wind regime and characteristics of an airport. This study may cast the light on researchers, policymakers, policy analysts and airport designers intending to investigate the wind profile of a runway at the airport in the world and also provide a significant pathway on how to determine the wind distribution of the runway.

Originality value

Instead of the well-known Weibull distribution for the representing of wind distribution in the literature, in this paper, IG distribution is used. Furthermore, the suitability of IG to represent the wind distribution is validated when compared with two-parameter Weibull and Rayleigh distributions. Besides, the performance and efficiency of PS have been evaluated by comparing it with other methods.

Details

Aircraft Engineering and Aerospace Technology, vol. 93 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

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