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Article

Yong Liu, Jun-liang Du, Ren-Shi Zhang and Jeffrey Yi-Lin Forrest

This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.

Abstract

Purpose

This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.

Design/methodology/approach

Because of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.

Findings

The results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.

Practical implications

The novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.

Originality/value

The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

Details

Kybernetes, vol. 48 no. 9
Type: Research Article
ISSN: 0368-492X

Keywords

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Book part

Badi H. Baltagi, Georges Bresson and Jean-Michel Etienne

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as…

Abstract

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

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Article

P. de Jager

Empirical accounting research frequently makes use of data sets with a time‐series and a cross‐sectional dimension ‐ a panel of data. The literature review indicates that…

Abstract

Empirical accounting research frequently makes use of data sets with a time‐series and a cross‐sectional dimension ‐ a panel of data. The literature review indicates that South African researchers infrequently allow for heterogeneity between firms when using panel data and the empirical example shows that regression results that allow for firm heterogeneity are materially different from regression results that assume homogeneity among firms. The econometric analysis of panel data has advanced significantly in recent years and accounting researchers should benefit from those improvements.

Details

Meditari Accountancy Research, vol. 16 no. 2
Type: Research Article
ISSN: 1022-2529

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Article

Esmaeil Hadavandi, Arash Ghanbari, S. Mohsen Mirjani and Salman Abbasian

The purpose of this paper is to estimate long‐run elasticities for housing prices in Tehran's (capital of Iran) 20 different zones relative to several explanatory…

Abstract

Purpose

The purpose of this paper is to estimate long‐run elasticities for housing prices in Tehran's (capital of Iran) 20 different zones relative to several explanatory variables available for use such as land price, total substructure area, material price, etc. Moreover, another goal of this paper is to propose a new approach to deal with problems which arise due to a lack of proper data.

Design/methodology/approach

The data set is gathered from “The Municipality of Tehran” and “The Central Bank of Islamic Republic of Iran (CBI)”. One‐way fixed effects and one‐way random effects approaches (which are panel data approaches) are applied to model housing price forecasting function in Tehran's 20 different zones. Results are compared with ordinary least squares approach which is a common approach in this field. Finally, outcomes of the preferred approach are discussed and analyzed with regard to the economic point of view.

Findings

Results show that one‐way fixed effects approach provides more accurate forecasts and can be considered as a suitable tool to deal with housing price forecasting problems in environments which are: uncertain, complex, and faced with a lack of proper data. Moreover, it is found that land price is the most effective factor that has impact on total housing cost in Tehran, i.e. the main portion of house prices in Tehran is affected by land price, so appropriate policies have to be made by the government to control fluctuations of this factor.

Practical implications

The proposed approach will supply policy makers with improved estimations with decreased errors in uncertain and complex environments which are faced with a lack of proper data, and it extracts valuable information which enables policy makers for handling non‐linearity, complexity, as well as uncertainty that may exist in actual data sets with respect to housing price forecasting. Moreover, the proposed approach can be applied to similar housing price case studies to obtain more accurate and more reliable outcomes.

Originality/value

Applying panel data approach for estimation of housing prices is relatively new in the field of housing economics. Moreover, this is the first study which employs panel data approach for analyzing the housing market in Tehran.

Details

International Journal of Housing Markets and Analysis, vol. 4 no. 1
Type: Research Article
ISSN: 1753-8270

Keywords

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Article

Shuyun Ren and Tsan-Ming Choi

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and…

Abstract

Purpose

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

Design/methodology/approach

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

Findings

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

Research limitations/implications

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

Practical implications

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

Originality/value

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

Details

Industrial Management & Data Systems, vol. 116 no. 6
Type: Research Article
ISSN: 0263-5577

Keywords

Abstract

Details

Panel Data Econometrics Theoretical Contributions and Empirical Applications
Type: Book
ISBN: 978-1-84950-836-0

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Article

Liv Osland

Hedonic models are commonly used in housing markets studies to obtain quantitative measures of various implicit prices. The use of panel data in other fields of research…

Abstract

Purpose

Hedonic models are commonly used in housing markets studies to obtain quantitative measures of various implicit prices. The use of panel data in other fields of research has proved to be valuable when accounting for unobserved heterogeneity. Given that houses are extremely heterogeneous, and given that it is impossible to include all relevant attributes in hedonic models, removing unobserved heterogeneity by basic panel data models sounds appealing. This paper seeks to compare results between models that use pooled cross section data and panel data. The main research question is whether the pooled model gives unbiased estimates on some basic implicit prices.

Design/methodology/approach

The paper applies the hedonic methodology. It uses regression analysis and estimate basic and parsimonious models that use either pooled time series and cross section data or panel data. The empirical results when using the two different approaches are compared.

Findings

The paper illustrates that the results from the pooled timeseries and cross section model could be biased for some basic implicit prices. With some nuances, it is illustrated that in specific situations the use of a basic panel data estimator could be a simple solution to the problem of misspecification due to omitted, time‐invariant explanatory variables.

Research limitations/implications

Most of the included variables do not change over time, however. In these cases potential bias using a basic fixed effects approach could not be checked for. It is also problematic that the variation in some of the time‐varying variables is not reliable and small. Finally, there could be a problem with sample selection bias. This may limit the usefulness of using panel data in disaggregated hedonic house price studies.

Originality/value

Hedonic house price models are frequently used in housing market research. It is therefore important to study in various ways whether the traditional approaches provide unbiased results. In this paper models that use panel data are compared to models that use more traditional time series and cross section data. To the author's knowledge, this approach has not been followed before.

Details

International Journal of Housing Markets and Analysis, vol. 6 no. 1
Type: Research Article
ISSN: 1753-8270

Keywords

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Article

Allen M. Featherstone, Timothy A. Park and Jeremy G. Weber

The purpose of this paper is to discuss opportunities to obtain more information from the Agricultural Resource Management Survey (ARMS). Specifically, the paper will…

Abstract

Purpose

The purpose of this paper is to discuss opportunities to obtain more information from the Agricultural Resource Management Survey (ARMS). Specifically, the paper will explore the issue of survey nonresponse, the development of pseudo panels, and more frequent updating of cost of production data on an enterprise basis.\

Design/methodology/approach

Researchers from the Land Grant University System and the Economic Research Service have relied on ARMS to evaluate the effect of agricultural, macroeconomic, and other factors on the US farm sector, farm businesses, and the households that manage them. This paper will identify gaps in understanding and proposes approaches to extract additional information from ARMS.

Findings

The relevance of ARMS in the future will depend on the ability to continue to understand potential pitfalls and areas of additional research that can develop new procedures to extract additional information. Three issues which are in need of further study include continuing to examine the issue of non‐response, refining methods to develop pseudo panel data, and examining methods to develop commodity specific financial information between the commodity specific surveys.

Originality/value

The National Research Council completed a review of ARMS to address challenges in keeping the survey relevant into the future. However, research that examines the construction of financial statements and other information had not been conducted since the early 1990s. This study fills part of that gap.

Details

Agricultural Finance Review, vol. 72 no. 2
Type: Research Article
ISSN: 0002-1466

Keywords

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Article

Xin Shen and Mark J. Holmes

– This paper investigates whether mean reversion holds for a panel of 16 OECD stock price indices for the period 1970 to 2011.

Abstract

Purpose

This paper investigates whether mean reversion holds for a panel of 16 OECD stock price indices for the period 1970 to 2011.

Design/methodology/approach

We employ seemingly unrelated regression (SUR)-based linear and non-linear unit root tests which are not only able to exploit the power of panel data analysis but also account for cross sectional dependencies as well as identify which panel members are stationary.

Findings

In contrast to a literature that offers mixed findings on stationarity, it was found that most of our sample is characterized as mean- or trend-reverting with approximated half-lives in the region of three to five years.

Originality/value

In contrast to other panel unit root tests of stock prices, the authors identify which individual panel members are stationary and non-stationary using a SURADF test. A further novelty of our approach is that we also develop a SUR-based panel KSS test that allows us to explore the possibility that stock prices exhibit non-linear stationarity.

Details

Studies in Economics and Finance, vol. 31 no. 4
Type: Research Article
ISSN: 1086-7376

Keywords

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Book part

Yu Yvette Zhang, Qi Li and Dong Li

This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the…

Abstract

This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the issues of censoring, sample selection, attrition, missing data, and measurement error in panel data models. Although most of these issues, except attrition, occur in cross-sectional or time series data as well, panel data models introduce some particular challenges due to the presence of persistent individual effects. The past two decades have seen many stimulating developments in the econometric and statistical methods dealing with these problems. This review focuses on two strands of research of the rapidly growing literature on semiparametric and nonparametric methods for panel data models: (i) estimation of panel models with discrete or limited dependent variables and (ii) estimation of panel models based on nonparametric deconvolution methods.

Details

Missing Data Methods: Cross-sectional Methods and Applications
Type: Book
ISBN: 978-1-78052-525-9

Keywords

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