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Article
Publication date: 10 June 2021

Abhijat Arun Abhyankar and Harish Kumar Singla

The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general…

Abstract

Purpose

The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”

Design/methodology/approach

Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).

Findings

While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).

Research limitations/implications

The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.

Practical implications

The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.

Originality/value

To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

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Book part
Publication date: 17 November 2010

John F. Kros and Christopher M. Keller

This chapter presents an Excel-based regression analysis to forecast seasonal demand for U.S. Imported Beer sales data. The following seasonal regression models are…

Abstract

This chapter presents an Excel-based regression analysis to forecast seasonal demand for U.S. Imported Beer sales data. The following seasonal regression models are presented and interpreted including a simple yearly model, a quarterly model, a semi-annual model, and a monthly model. The results of the models are compared and a discussion of each model's efficacy is provided. The yearly model does the best at forecasting U.S. Import Beer sales. However, the yearly does not provide a window into shorter-term (i.e., monthly) forecasting periods and subsequent peaks and valleys in demand. Although the monthly seasonal regression model does not explain as much variance in the data as the yearly model it fits the actual data very well. The monthly model is considered a good forecasting model based on the significance of the regression statistics and low mean absolute percentage error. Therefore, it can be concluded that the monthly seasonal model presented is doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-201-3

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Article
Publication date: 5 November 2019

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…

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.

Details

Journal of Business & Industrial Marketing, vol. 35 no. 3
Type: Research Article
ISSN: 0885-8624

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Article
Publication date: 3 May 2013

Richard Hauser

The purpose of this paper is to investigate whether corporate dividend policy changed during the financial crisis.

Abstract

Purpose

The purpose of this paper is to investigate whether corporate dividend policy changed during the financial crisis.

Design/methodology/approach

For this study, a life‐cycle model is used to predict the probability that a firm pays a dividend. The data sample for this research follows that of Fama and French and of DeAngelo et al., for the time period of 2006‐2009. The panel logistic regression analysis considers the firm cluster effects and the autoregressive correlation of the firm clusters.

Findings

This study shows evidence that the probability that a firm paid a dividend declined in 2008 and 2009, even after taking the firm's financial condition into account. Furthermore, the analysis also shows that dividend policy did shift during the financial crisis.

Originality/value

The results of this study show that dividend policy did shift during the financial crisis. The research provides evidence that firms placed additional emphasis on financial viability after the financial crisis.

Details

Managerial Finance, vol. 39 no. 6
Type: Research Article
ISSN: 0307-4358

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Article
Publication date: 22 March 2019

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…

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.

Details

Journal of Advances in Management Research, vol. 16 no. 4
Type: Research Article
ISSN: 0972-7981

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Article
Publication date: 2 February 2021

Hao Wang, Guangming Dong and Jin Chen

The purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear.

Abstract

Purpose

The purpose of this paper is building the regression model related to tool wear, and the regression model is used to identify the state of tool wear.

Design/methodology/approach

In this paper, genetic programming (GP), which is originally used to solve the symbolic regression problem, is used to build the regression model related to tool wear with the strong regression ability. GP is improved in genetic operation and weighted matrix. The performance of GP is verified in the tool vibration, force and acoustic emission data provided by 2010 prognostics health management.

Findings

In result, the regression model discovered by GP can identify the state of tool wear. Compared to other regression algorithms, e.g. support vector regression and polynomial regression, the identification of GP is more precise.

Research limitations/implications

The regression models built in this paper can only make an assessment of the current wear state with current signals of tool. It cannot predict or estimate the tool wear after the current state. In addition, the generalization of model has some limitations. The performance of models is just proved in the signals from the same type of tools and under the same work condition, and different tools and different work conditions may have influences on the performance of models.

Originality/value

In this study, the discovered regression model can identify the state of tool wear precisely, and the identification performances of model applied in other tools are also excellent. It can provide a significant information about the health of tool, so the tools can be replaced or repaired in time, and the loss caused by tool damage can be avoided.

Details

Engineering Computations, vol. 38 no. 6
Type: Research Article
ISSN: 0264-4401

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Book part
Publication date: 16 December 2009

Zongwu Cai, Jingping Gu and Qi Li

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent…

Abstract

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.

Details

Nonparametric Econometric Methods
Type: Book
ISBN: 978-1-84950-624-3

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Book part
Publication date: 21 November 2014

Alex Maynard and Dongmeng Ren

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing…

Abstract

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

Details

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

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Article
Publication date: 1 July 2005

Heng Li, Q.P. Shen and Peter E.D. Love

This paper presents a set of step‐wise regression models which can incorporate multiple factors in modelling the costs of office buildings. The models appeared to be more…

Abstract

Purpose

This paper presents a set of step‐wise regression models which can incorporate multiple factors in modelling the costs of office buildings. The models appeared to be more accurate than the traditional method.

Design/methodology/approach

The data were collected from historical office building projects, which were then, adjusted using the construction price index. The step‐wise regression was conducted to produce the linear cost models.

Findings

Seven RC office buildings and 11 steel office buildings in Hong Kong completed in different years were selected randomly to verify the accuracy of the regression models developed. The data of these buildings were not used in the development of the cost models. The result shows that the variability of percentage difference is ranging from −4.11 per cent (4.11 per cent underestimate) to +2.74 per cent (2.74 per cent overestimate) for RC office buildings. For steel office buildings, it ranges from −6.65 per cent (6.65 per cent underestimate) to +2.78 per cent (2.78 per cent overestimate).

Research limitations/implications

This study presents a methodology that can be used in cost estimation of office buildings in Hong Kong at early stage of construction project. The regression cost models developed above are based on, in total, historical data of 30 completed office buildings in Hong Kong. The reliability of the cost models can be further improved by including more office buildings to develop the cost models. Furthermore, the application of cost modelling by regression analysis is not limited to office buildings. The same approach can be applied to residential and other non‐residential buildings as well. Regression cost modelling, with sufficient updating for new cost data available, can provide economic, quick and accurate cost estimation at early stage of construction projects. It will become rational guide supplementing judgmental forecast of cost advisors in near future.

Originality/value

Step‐wise regression procedure was applied to develop the cost models. Jackknife re‐sampling was carried out and both of the models show stability. Cross‐validation shows that the developed regression models performed satisfactorily. The paper considers that it can provide economic, quick and accurate cost estimation at the early stage of construction project. In addition, the approach of this study can be adopted to develop cost models of other types of buildings in other locations.

Details

Facilities, vol. 23 no. 9/10
Type: Research Article
ISSN: 0263-2772

Keywords

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Article
Publication date: 8 June 2012

Xinping Xiao and Yayun Lu

The purpose of this paper is to simplify the computation of parameter estimation in the grey linear regression model and solve the problem that the development coefficient…

Abstract

Purpose

The purpose of this paper is to simplify the computation of parameter estimation in the grey linear regression model and solve the problem that the development coefficient could not be computed in some sequence data, such as short‐term traffic flow.

Design/methodology/approach

Starting from the limitation that can be identified in the equation and analyzing the range using the method to estimate parameters, this paper researches the modelling mechanism and the other forms which are equivalent with the original form. At the same time, this paper gives an estimation method and gets the relationship in various forms and the relationship between the model and GM(1,1) model.

Findings

For the grey linear regression model, there exists a new method of parameter identification and three other forms as follows: the original form, the Whitenization equation and the connotation form.

Practical implications

The method of parameter identification exposed in the paper expanded the scope of the application of the grey linear regression model, and it can be used to model and forecast the urban road short‐time traffic flow.

Originality/value

This paper has solved some complicated problems such as the parameter estimation computation in the grey linear regression model. In addition, three kinds of representation forms of the model and its relationship between the model and GM(1,1) have also been presented. Finally, its application of the model in a short‐term traffic flow prediction has shown its superiority.

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