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1 – 10 of over 23000Jeffrey J. Burks, David W. Randolph and Jim A. Seida
This study examines the use of linear regressions that include interaction terms, finding frequent interpretation errors in published accounting research. We provide insights on…
Abstract
This study examines the use of linear regressions that include interaction terms, finding frequent interpretation errors in published accounting research. We provide insights on how to estimate, interpret, and present interactive regression models, and explain seldom-used but easily-implemented methods to report conditional marginal effects. We also examine the use of interaction terms in tax and financial reporting trade-off studies, evaluating the conceptual fit between a regression model with interactions and alternative definitions of trade-off. Although we advocate the use of interactive models, noise levels common in accounting research greatly reduce the ability to detect interaction effects.
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Bingjun Li, Weiming Yang and Xiaolu Li
The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.
Abstract
Purpose
The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.
Design/methodology/approach
Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values.
Findings
The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction.
Practical implications
The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed.
Originality/value
This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.
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William McCluskey, Peadar Davis, Martin Haran, Michael McCord and David McIlhatton
The aim of this paper is to investigate the comparative performance of an artificial neural network (ANN) and several multiple regression techniques in terms of their predictive…
Abstract
Purpose
The aim of this paper is to investigate the comparative performance of an artificial neural network (ANN) and several multiple regression techniques in terms of their predictive accuracy and capability of being used within the mass appraisal industry.
Design/methodology/approach
The methodology first tested that the data set had neglected non‐linearity which suggested that a non‐linear modelling technique should be applied. Given the capability of ANNs to model non‐linear data, this technique was used along with an OLS regression model (baseline model) and two non‐linear multiple regression techniques. In addition, the models were evaluated in terms of predictive accuracy and their capability of use within the mass appraisal environment.
Findings
Previous studies which have compared the predictive performance of an ANN model against multiple regression techniques are inconclusive. Having superior predictive capability is important but equally important is whether the technique can be successfully employed for the mass appraisal of residential property. This research found that a non‐linear regression model had higher predictive accuracy than the ANN. Also the output of the ANN was not sufficiently transparent to provide an unambiguous appraisal model upon which predicted values could be defended against objections.
Research limitations/implications
The research provides an informative view as to the efficacy of ANN methodology within the real estate field. A number of issues have been raised on the applicability of ANN models within the mass appraisal environment.
Practical implications
This work demonstrates that ANNs whilst useful as a predictive tool have a limited practical role for the assessment of residential property values for property tax purposes.
Originality/value
The work has taken forward the debate on the usefulness of ANN techniques within the mass appraisal environment.
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Ann‐Marie Lamb, Tugrul U. Daim and Timothy R. Anderson
Airplane technology is undergoing several exciting developments, particularly in avionics, material composites, and design tool capabilities, and, though there are many studies…
Abstract
Purpose
Airplane technology is undergoing several exciting developments, particularly in avionics, material composites, and design tool capabilities, and, though there are many studies conducted on subsets of airplane technology, market, and economic parameters, few exist in forecasting new commercial aircraft model introduction. In fact, existing research indicates the difficulty in quantitatively forecasting commercial airplanes due in part to the complexity and quantity of exogenous factors which feed into commercial airplane introduction decisions. This paper seeks to address this gap.
Design/methodology/approach
The analysis is based on a literature review, supplemented by a collection of secondary data. The study then focuses on applying three technology forecasting techniques: multiple regression; linear regression; and the Pearl growth curve.
Findings
The results provide a valid model for multiple regression and linear regression on range and composite material percentage for use in commercial airplane forecasting. However, growth curve analysis, comparatively, appears to provide the most intriguing and flexible forecast outlook in alignment with industry dynamics.
Research limitations/implications
Research implications include a caution for forecasters in support of the difficulty of commercial aircraft forecasting due in part to the quantity of exogenous factors, particularly compared with a related industry, military aircraft. Future work could include: utilizing other forecasting techniques that allow for greater numbers of forecast factors, additional future models, additional range aircraft and/or analyzing the impact that competing transportation modes in mid‐range aircraft could have on long‐range aircraft introduction.
Originality/value
The study provides value in extending a previous descriptive paper on airplane parameters. Additionally, it appears to be one of the first quantitative examples supporting previous research indicating the complexity of forecasting airplane new product introduction, but it overcomes some of this complexity by providing a valid model for forecasting with range and composite material percentage as inputs.
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Anirban Dutta and Biswapati Chatterjee
The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for…
Abstract
Purpose
The purpose of this paper is to establish the regression equation based upon a set of samples prepared through structured design of experiment and form a prediction model for prediction of the areal density gram per square meter (GSM) of the embroidered fabrics and study the influence of basic input parameters.
Design/methodology/approach
Embroidery samples are prepared taking input parameters as GSM of the base fabric, linear density of the embroidery thread and stitch density of the embroidery design. Three levels of values are identified for each of the input parameters. Taguchi and Box-Behnken experiment design principles are used to prepare two sets of samples. Linear multiple regression is used to determine the prediction equations based upon each of the two sets and the combined set as well. Prediction equations are statistically verified for the prediction accuracy. Also, surface curves are prepared to study the influence of embroidery parameters on the GSM.
Findings
It is found that all the three prediction models developed in this study can predict with a very satisfactory level of accuracy. However, the regression equation based upon the data set prepared according to Taguchi experiment design is emerged as the prediction model with highest level of prediction accuracy. Corresponding equation coefficients and several three-dimensional surface curves are used to study the influence of embroidery parameters and it is found that the stitch density is the most influential input parameter followed by stitch length and the GSM of base fabric.
Research limitations/implications
This can be used to assess the GSM of embroidered fabrics before starting the actual embroidery process. So, this model can help the embroidery designers significantly to pre-estimate the GSM of the embroidered fabrics and select the design parameters accordingly. Also, this model can be a useful tool for estimation of thread consumption and thread cost in embroidery.
Practical implications
The input parameters used here are very basic parameters related to design and materials, which can be easily available. And also, a simple linear multiple regression is used to make the prediction equation simple and easy to use. So, this model can help the embroidery designers or garment designers to select/adjust the embroidery parameters and thread parameters accordingly in the planning and designing stage itself to ensure that the GSM of embroidered fabrics remains within desirable range. Also, this prediction model developed hereby may be a very useful tool for estimation of the consumption and cost of embroidery threads.
Originality/value
This paper presents a very fundamental study to reveal the effect of embroidery parameters on the GSM, through development of regression equations. It can help future researchers in optimizations of input parameters and forming a technical guideline for the embroidery designers for selection of the design parameters for a desired GSM of embroidered fabric.
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Jae-huei Jan and Arun Kumar Gopalaswamy
The purpose of this paper is to estimate long-term currency exchange rate and also identify the key factors for decision makers in the currency exchange market. The study is…
Abstract
Purpose
The purpose of this paper is to estimate long-term currency exchange rate and also identify the key factors for decision makers in the currency exchange market. The study is expected to aid decision makers to take positions in the dynamic Forex market.
Design/methodology/approach
This study is based on quantitative and fundamental analysis of statistically oriented regression models. The trend of quarterly exchange rates is investigated using 110 variables including economic elements, interest rate and other currencies. This research is based on the same information that banks’ dealers use for the analysis. Ordinary least squares linear regression also known as “least squared errors regression” was used to estimate the value of the dependent variable.
Findings
The study concludes that “only Australian economic data” or “only the US economic data” cannot fully reflect the trend of AUD/USD. EUR influences AUD relatively larger than the other main market currencies. Six-month Australian interest rate itself affects AUD/USD trend much more than the six-month interest difference between AUD and USD.
Research limitations/implications
The results indicate that the economic autoregressive moving average model can be used to predict future exchange rate using primary factors identified and not from the generic market or economic view. This helps adjust to the general, common (and possibly wrong) views when making a buy or sell decision.
Originality/value
This is one of the first studies in the context using the information of bank dealers for AUD/USD. This study is highly relevant in the current context, given the significant growth in Forex trade.
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Fredrick Otieno Okuta, Titus Kivaa, Raphael Kieti and James Ouma Okaka
The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose…
Abstract
Purpose
The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.
Design/methodology/approach
The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.
Findings
The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.
Practical implications
A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.
Originality/value
While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.
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R. Dale Wilson and Harriette Bettis-Outland
Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in…
Abstract
Purpose
Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models.
Design/methodology/approach
A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples.
Findings
ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples.
Research limitations/implications
Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications.
Practical implications
ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike.
Originality/value
The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.
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Streaming video on demand (SVOD) services are comprised of digital media content creation and content distribution that provide a vast array of genre content playable on an…
Abstract
Purpose
Streaming video on demand (SVOD) services are comprised of digital media content creation and content distribution that provide a vast array of genre content playable on an assortment of different technology platforms. Additionally, these digital services are equipped to collect data and information on consumers. However, these services do not capture extensive consumer demographics, lifestyles or personalities information.
Design/methodology/approach
To resolve this discrepancy, collecting external information such as complete demographics, personalities and lifestyles of consumers can be useful in advancing SVOD consumer behavior knowledge. This study examined how consumer demographics, lifestyles and personalities may predict SVOD genre consumption and SVOD platform consumption. A survey was executed and disseminated to collect consumer information across these dimensions. Multiple linear regressions and a structural equation model were formed to explicate variance.
Findings
Consumer demographics, lifestyles and personalities’ information do predict SVOD genre consumption and SVOD platform consumption.
Originality/value
Media selection and trait theory have not been applied to understanding unexplained variance behind consumer media consumption, and are often used to predict media consumption variance among consumers. These findings illustrate that, while digital consumer touchpoints are necessary to collect and analyze, marketers should not lose sight of easily-obtainable consumer data, much of which dictates consumption choices.
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Modeling helps to determine how structural parameters of fabric affect the surface of a fabric and also identify the way they influence fabric properties. Moreover, it helps to…
Abstract
Purpose
Modeling helps to determine how structural parameters of fabric affect the surface of a fabric and also identify the way they influence fabric properties. Moreover, it helps to estimate and evaluate without the complexity and time-consuming experimental procedures. The purpose of this study is to develop and select the best regression model equations for the prediction and evaluation of surface roughness of plain-woven fabrics.
Design/methodology/approach
In this study, a linear and quadratic regression model was developed for the prediction and evaluation of surface roughness of plain-woven fabrics, and the capability in accuracy and reliability of the two-model equation was determined by the root mean square error (RMSE). The Design-Expert AE11 software was used for developing the two model equations and analysis of variance “ANOVA.” The count and density were used for developing linear model equation one “SMD1” as well as for quadratic model equation two “SMD2.”
Findings
From results and findings, the effects of count and density and their interactions on the roughness of plain-woven fabric were found statistically significant for both linear and quadratic models at a confidence interval of 95%. The count has a positive correlation with surface roughness, while density has a negative correlation. The correlations revealed that models were strongly correlated at a confidence interval of 95% with adjusted R² of 0.8483 and R² of 0.9079, respectively. The RMSE values of the quadratic model equation and linear model equation were 0.1596 and 0.0747, respectively.
Originality/value
Thus, the quadratic model equation has better capability accuracy and reliability in predictions and evaluations of surface roughness than a linear model. These models can be used to select a suitable fabric for various end applications, and it was also used for tests and predicts surface roughness of plain-woven fabrics. The regression model helps to reduce the gap between the subjective and objective surface roughness measurement methods.
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