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Book part
Publication date: 25 July 1997

Ehsan S. Soofi

Abstract

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Applying Maximum Entropy to Econometric Problems
Type: Book
ISBN: 978-0-76230-187-4

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Article
Publication date: 26 September 2011

Samih Azar

This paper seeks to reconsider the Euler equation of the Consumption Capital Asset Pricing Model (CCAPM), to derive a regression‐based model to test it, and to present…

Abstract

Purpose

This paper seeks to reconsider the Euler equation of the Consumption Capital Asset Pricing Model (CCAPM), to derive a regression‐based model to test it, and to present evidence that the model is consistent with reasonable values for the coefficient of relative risk aversion (CRRA). This runs contrary to the findings of the literature on the equity premium puzzle, but is in agreement with the literature that estimates the CRRA for the purpose of computing the social discount rate, and is in line with the research on labor supply. Tests based on General Method of Moments (GMM) for the same sample produce results that are extremely disparate and unstable. The paper aims to check and find support for the robustness of the regression‐based tests. Habit formation models are also to be evaluated with regression‐based and GMM tests. However, the validity of the regression‐based models depends critically on their functional forms.

Design/methodology/approach

The paper presents empirical evidence that the conventional use of GMM fails because of four pathological features of GMM that are referred to under the general caption of “weak identification”. In addition to GMM, the paper employs linear regression analysis to test the CCAPM, and it is found that the regression residuals follow well‐behaved distributional properties, making valid all statistical inferences, while GMM estimates are highly unstable.

Findings

Four unexpected findings are reported. The first is that the regression‐based models are consistent with reasonable values for the CRRA, i.e. estimates that are below 4. The second is that the regression‐based tests are robust, while the GMM‐based tests are not. The third is that regression‐based tests with habit formation depend crucially on the specification of the model. The fourth is that there is evidence that market stock returns are sensitive to both consumption and dividends. The author calls the latter “extra sensitivity of market stock returns”, and it is described as a new puzzle.

Originality/value

The regression‐based models of the CCAPM Euler equation are novel. The comparison between GMM and regression‐based models for the same sample is original. The regression‐based models with habit formation are new. The equity premium puzzle disappears because the estimates of the CRRA are reasonable. But another puzzle is documented, which is the “extra sensitivity of market stock returns” to consumption and dividends together.

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Article
Publication date: 1 July 2005

Duncan Orr, David Emanuel and Norman Wong

This study examines the relationship between board composition and firm value, and the extent to which this relationship may be affected by a company’s investment…

Abstract

This study examines the relationship between board composition and firm value, and the extent to which this relationship may be affected by a company’s investment opportunity set. There is little research that examines this issue, particularly for the New Zealand market. Of the research that exists, and generally for the research that examines how board composition affects firm performance, the findings have been mixed. Using a randomly chosen sample, which improves the external validity of results from prior studies, we find that board composition of high growth option firms is positively related to firm value, and this relationship is maintained when more refined measures that proxy the characteristics of outside directors (such as tenure of outside directors, the level of outside director equity ownership, the number of other board positions held by outside directors, and the total proportion of non‐executive directors, including grey directors) are recognised.

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Pacific Accounting Review, vol. 17 no. 2
Type: Research Article
ISSN: 0114-0582

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Article
Publication date: 17 October 2019

Mahmoud ELsayed and Amr Soliman

The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM…

Abstract

Purpose

The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method.

Design/methodology/approach

In this paper, the authors adopted the incurred claims of Egyptian non-life insurance market as a dependent variable during a 10-year period. MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate the parameters of interest. However, the authors used the R package to estimate the parameters of the linear regression using the above techniques.

Findings

These procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.

Originality/value

In this paper, the authors will estimate the parameters of a linear regression model using MCMC method via R package. Furthermore, MCMC uses Gibbs sampling to generate a sample from a posterior distribution of a linear regression to estimate parameters to predict future claims. In the same line, these procedures will guide the decision-maker for estimating the reserve and set proper investment strategy.

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Journal of Humanities and Applied Social Sciences, vol. 2 no. 1
Type: Research Article
ISSN: 2632-279X

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Article
Publication date: 6 September 2016

Silvana Chambers

Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper…

Abstract

Purpose

Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper aims to educate human resource development (HRD) researchers and practitioners on the implementation of RD design as an ethical alternative for making causal claims about training interventions.

Design/methodology/approach

To demonstrate the key features of RD designs, a simulated data set was generated from actual pre-test and post-test diversity training scores of 276 participants from three organizations in the USA. Parametric and non-parametric analyses were conducted, and graphical presentations were produced.

Findings

This study found that RD design can be used for evaluating training interventions. The results of the simulated data set yielded statistically significant results for the treatment effects, showing a positive causal effect of the training intervention. The analyses found support for the use of RD models with retrospective training intervention data, eliminating ethical concerns from random group assignment. The results of the non-parametric model provided evidence of the plausibility of finding the right balance between precision of estimates and generalizable results, making it an alternative to experimental designs.

Practical implications

This study contributes to the HRD field by explicating the implementation of a sophisticated, statistical tool to strengthen causal claims, contributing to an evidence-based HRD approach to practice and providing the R syntax for replicating the analyses contained herein.

Originality/value

Despite the growing number of scholarly articles being published in HRD journals, very few have used experimental or quasi-experimental design approaches. Therefore, a very limited amount of research has been devoted to uncovering causal relationships.

Details

European Journal of Training and Development, vol. 40 no. 8/9
Type: Research Article
ISSN: 2046-9012

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Article
Publication date: 3 April 2017

Ahmad Hakimi, Amirhossein Amiri and Reza Kamranrad

The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the…

Abstract

Purpose

The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T2 control chart. In addition, the performance of the non-robust and the proposed robust control charts is evaluated in Phase II.

Design/methodology/approach

In this paper some, robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) are used to decrease the effects of outliers on estimating the logistic regression parameters as well as the performance of the T2 control chart.

Findings

The results of the simulation studies in both Phases I and II show the better performance of the proposed robust control charts rather than the non-robust control chart for estimating the logistic regression profile parameters and monitoring the logistic regression profiles.

Practical implications

In many practical applications, there are outliers in processes which may affect the estimation of parameters in Phase I and as a result of deteriorate the statistical performance of control charts in Phase II. The methods developed in this paper are effective for decreasing the effect of outliers in both Phases I and II.

Originality/value

This paper considers monitoring the logistic regression profile in Phase I under the presence of outliers. Also, three robust approaches are developed to decrease the effects of outliers on the parameter estimation and monitoring the logistic regression profiles in both Phases I and II.

Details

International Journal of Quality & Reliability Management, vol. 34 no. 4
Type: Research Article
ISSN: 0265-671X

Keywords

Abstract

Economists and sociologists have proposed arguments for why there can exist wage penalties for work involving helping and caring for others, penalties borne disproportionately by women. Evidence on wage penalties is neither abundant nor compelling. We examine wage differentials associated with caring jobs using multiple years of Current Population Survey (CPS) earnings files matched to O*NET job descriptors that provide continuous measures of “assisting & caring” and “concern” for others across all occupations. This approach differs from prior studies that assume occupations either do or do not require a high level of caring. Cross-section and longitudinal analyses are used to examine wage differences associated with the level of caring, conditioned on worker, location, and job attributes. Wage level estimates suggest substantive caring penalties, particularly among men. Longitudinal estimates based on wage changes among job switchers indicate smaller wage penalties, our preferred estimate being a 2% wage penalty resulting from a one standard deviation increase in our caring index. We find little difference in caring wage gaps across the earnings distribution. Measuring mean levels of caring across the U.S. labor market over nearly thirty years, we find a steady upward trend, but overall changes are small and there is no evidence of convergence between women and men.

Details

Gender Convergence in the Labor Market
Type: Book
ISBN: 978-1-78441-456-6

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Abstract

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International Comparisons of Prices, Output and Productivity
Type: Book
ISBN: 978-1-84950-865-0

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Article
Publication date: 15 May 2017

Felix Canitz, Panagiotis Ballis-Papanastasiou, Christian Fieberg, Kerstin Lopatta, Armin Varmaz and Thomas Walker

The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither…

Abstract

Purpose

The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither stationary nor cointegrated.

Design/methodology/approach

The authors conducted Monte Carlo simulations according to Baltagi et al. (2011), Petersen (2009) and Gow et al. (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.

Findings

The results of this study suggest that biases in regression estimates can be reduced and valid inferences can be obtained by using robust standard errors clustered by firm, clustered by firm and time or Fama–MacBeth t-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.

Originality/value

The findings of this study are suited to guide future researchers regarding which estimation methods are the most reliable given the possible nonstationarity of the variables of interest.

Details

The Journal of Risk Finance, vol. 18 no. 3
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 17 July 2019

Harish Kumar Singla and Priyanka Bendigiri

The purpose of this paper is to find out the factors affecting rentals of residential apartments in Pune, India.

Abstract

Purpose

The purpose of this paper is to find out the factors affecting rentals of residential apartments in Pune, India.

Design/methodology/approach

Four regression models are developed, i.e. basic ordinary least square (OLS) regression model, OLS regression model with robust estimates, OLS regression model with clustered robust estimates and generalized least square (GLS) regression model with maximum likelihood (ML) robust estimates. Based on the Akaike information criterion and Bayesian information criterion criteria, OLS regression model with clustered robust estimates and GLS regression model with robust estimates are best fit. The data are tested for multicollinearity and the models are tested for heteroscedasticity. The study uses the expected rent value data collected from Web portals and the data on factors affecting the rental value of residential property are collected through the study of land use maps, Google earth software and field visits.

Findings

Total floor area and number of rooms are structure related factors that positively affect the rental value, i.e. more the area and number of rooms, higher the rental value. The distances from the nearest police station and fire station are security and safety factors. The results suggest that higher distance from these factors leads to lower rental values, as safety and security is the top priority of residents seeking residential property on rental basis. The distance from employment zones, distance from nearest school/college and the distance from the nearest public transport terminal are convenience related factors that negatively affect the rental value, as greater the distance, lesser the rental value and vice versa. The distance from Central Business District and hospitals has a positive effect on the rental values of a residential property implying that higher distances from these places command higher rental value.

Research limitations/implications

The study relies on rental data that owner is expecting for a particular property, it is not certain that the property would be actually rented for the same value. Second, researchers had to drop certain important drivers of rental value because of the issue of multicollinearity.

Practical implications

This is one of the rare studies conducted in Indian context, and the findings of the study are useful from the owner, tenants, urban bodies and developers’ point of view. Knowing that India is one of the fastest growing markets and need for housing is increasing day by day (including housing facility on rental basis), the stakeholders need to take care of the factors that affect the rental values of a residential property.

Social implications

The authors suggest the governments and the municipal bodies in India to come up with a public rental housing policy that separately caters to the needs of the lower income group, middle and upper income group in at least metros, tier I and tier II cities that are witnessing unprecedented growth in job seeking immigrants, who are seeking properties on rental basis. While developing a public rental policy, they must keep in mind the factors that are driving the rental values, such as proximity to employment zones, proximity to proper school and college, efficient public transport system as well as all safety and security measures. Creation of such a public rental policy is a win–win situation for immigrants, property owners and government/urban development bodies.

Originality/value

This paper is the first empirical study about the factors affecting rental values in Pune, India. The study will help property owners, immigrant and local tenants, government and urban development bodies to develop an understanding about the important factors affecting rental value and come up with their respective plans. Advanced econometric regression models are used based on the data that is collected through actual field visits, study of maps and secondary information rather than use of survey method or creation of dummy variables.

Details

International Journal of Housing Markets and Analysis, vol. 12 no. 6
Type: Research Article
ISSN: 1753-8270

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