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This article is motivated by the lack of flexibility in Bayesian quantile regression for ordinal models where the error follows an asymmetric Laplace (AL) distribution. The…
Abstract
This article is motivated by the lack of flexibility in Bayesian quantile regression for ordinal models where the error follows an asymmetric Laplace (AL) distribution. The inflexibility arises because the skewness of the distribution is completely specified when a quantile is chosen. To overcome this shortcoming, we derive the cumulative distribution function (and the moment-generating function) of the generalized asymmetric Laplace (GAL) distribution – a generalization of AL distribution that separates the skewness from the quantile parameter – and construct a working likelihood for the ordinal quantile model. The resulting framework is termed flexible Bayesian quantile regression for ordinal (FBQROR) models. However, its estimation is not straightforward. We address estimation issues and propose an efficient Markov chain Monte Carlo (MCMC) procedure based on Gibbs sampling and joint Metropolis–Hastings algorithm. The advantages of the proposed model are demonstrated in multiple simulation studies and implemented to analyze public opinion on homeownership as the best long-term investment in the United States following the Great Recession.
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James Mitchell, Aubrey Poon and Gian Luigi Mazzi
This chapter uses an application to explore the utility of Bayesian quantile regression (BQR) methods in producing density nowcasts. Our quantile regression modeling strategy is…
Abstract
This chapter uses an application to explore the utility of Bayesian quantile regression (BQR) methods in producing density nowcasts. Our quantile regression modeling strategy is designed to reflect important nowcasting features, namely the use of mixed-frequency data, the ragged-edge, and large numbers of indicators (big data). An unrestricted mixed data sampling strategy within a BQR is used to accommodate a large mixed-frequency data set when nowcasting; the authors consider various shrinkage priors to avoid parameter proliferation. In an application to euro area GDP growth, using over 100 mixed-frequency indicators, the authors find that the quantile regression approach produces accurate density nowcasts including over recessionary periods when global-local shrinkage priors are used.
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John Galakis, Ioannis Vrontos and Panos Xidonas
This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.
Abstract
Purpose
This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.
Design/Methodology/Approach
The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian stochastic method is developed including model selection and estimation of the tree structure parameters. The framework is applied on numerous U.S. asset pricing models, using alternative mimicking factor portfolios, frequency of data, market indices, and equity portfolios.
Findings
The findings reveal strong evidence that asset returns exhibit asymmetric effects and non- linear patterns to different common factors, but, more importantly, that there are multiple thresholds that create several partitions in the common factor space.
Originality/Value
To the best of the authors' knowledge, this paper is the first to explore and apply a tree-structured and quantile regression framework in an asset pricing context.
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Mohammad Arshad Rahman and Angela Vossmeyer
This chapter develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its…
Abstract
This chapter develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.
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Shianghau Wu and Jiannjong Guo
In this paper, the authors aim to propose to find the variables that affect the Taiwanese people’s satisfaction level of the general public with the government.
Abstract
Purpose
In this paper, the authors aim to propose to find the variables that affect the Taiwanese people’s satisfaction level of the general public with the government.
Design/methodology/approach
The authors intend to utilize the Bayesian quantile regression to explore the variables that affect the satisfaction of the general public at specific quantiles of Taiwanese satisfaction with the government and rough set classification to explore key variables related to the satisfaction level. Then they make the comparison of the classification among the two methods to obtain the performance of the classification.
Findings
The experiment result shows the major factors which have the positive relationship with the people who have higher satisfaction level with the central government. These factors include satisfaction with the uncorrupted performance of the central government; the evaluation of household’s economic condition one year after the present time; the satisfaction with the Taiwanese central government’s measures on food safety and the satisfaction with the 12 years primary education reform.
Originality/value
The study’s originality hinges on the application of Bayesian quantile regression and rough set classification to the analysis of the Taiwanese satisfaction with the government. It offers more insights on the key variables related to different satisfaction level and the classification performance between the two methods.
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Dejan Živkov and Jasmina Đurašković
This paper aims to investigate how oil price uncertainty affects real gross domestic product (GDP) and industrial production in eight Central and Eastern European countries (CEEC).
Abstract
Purpose
This paper aims to investigate how oil price uncertainty affects real gross domestic product (GDP) and industrial production in eight Central and Eastern European countries (CEEC).
Design/methodology/approach
In the research process, the authors use the Bayesian method of inference for the two applied methodologies – Markov switching generalized autoregressive conditional heteroscedasticity (GARCH) model and quantile regression.
Findings
The results clearly indicate that oil price uncertainty has a low effect on output in moderate market conditions in the selected countries. On the other hand, in the phases of contraction and expansion, which are portrayed by the tail quantiles, the authors find negative and positive Bayesian quantile parameters, which are relatively high in magnitude. This implies that in periods of deep economic crises, an increase in the oil price uncertainty reduces output, amplifying in this way recession pressures in the economy. Contrary, when the economy is in expansion, oil price uncertainty has no influence on the output. The probable reason lies in the fact that the negative effect of oil volatility is not strong enough in the expansion phase to overpower all other positive developments which characterize a growing economy. Also, evidence suggests that increased oil uncertainty has a more negative effect on industrial production than on real GDP, whereas industrial share in GDP plays an important role in how strong some CEECs are impacted by oil uncertainty.
Originality/value
This paper is the first one that investigates the spillover effect from oil uncertainty to output in CEEC.
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Avraam Papastathopoulos, Christos Koritos and Charilaos Mertzanis
For more than 40 years, researchers have examined an exhaustive set of attributes as price determinants in tourism and hospitality. In extending this rich research stream, this…
Abstract
Purpose
For more than 40 years, researchers have examined an exhaustive set of attributes as price determinants in tourism and hospitality. In extending this rich research stream, this study aims to propose and empirically assess a new set of hotel attributes, namely, faith-based attributes that allow tourists to continue following the activities and rituals guided by their religions while on vacation.
Design/methodology/approach
Using the Bayesian quantile regression for the first time in the field of hotel pricing, the hedonic pricing models examine both internal and external faith-based attributes, namely, halal services, which cater to the needs of Muslim tourists, in a sample of 805 hotels across the top three non-Muslim country destinations (Singapore, Thailand and Japan).
Findings
By exploring the effects of faith-based (halal) attributes available in hotels located in the biggest cities of the above-mentioned destinations, this study provides evidence for the significant role of faith-based (halal) attributes in determining hospitality prices.
Practical implications
This study’s findings offer a resource for several implications for tourism and hospitality scholars, practitioners and policymakers, especially within the field of Muslim/halal tourism, to develop action plans and strategies.
Originality/value
This study is the first to introduce a novel set of faith-based hospitality attributes and empirically assess their impact on hospitality price formation. Additionally, it contributes to the hedonic pricing method by being the first to use the Bayesian quantile regression.
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Debajit Dutta, Subhra Sankar Dhar and Amit Mitra
Stochastic volatility models are of great importance in the field of mathematical finance, especially for accurately explaining the dynamics of financial derivatives. A quantile…
Abstract
Stochastic volatility models are of great importance in the field of mathematical finance, especially for accurately explaining the dynamics of financial derivatives. A quantile-based estimator for the location parameter of a stochastic volatility model is proposed by solving an optimization problem. In this chapter, the asymptotic distribution of the estimator is derived without assuming that the density function of the noise is positive around the corresponding population quantile. We also discuss a Bayesian approach to the quantile estimation problem and establish a result regarding the nature of the posterior distribution.
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Rolando Gonzales and Andrea Rojas-Hosse
The purpose of this paper is to analyze the effects of inflationary shocks on inequality, using data of selected countries of the Middle East and North Africa (MENA).
Abstract
Purpose
The purpose of this paper is to analyze the effects of inflationary shocks on inequality, using data of selected countries of the Middle East and North Africa (MENA).
Design/methodology/approach
Inflationary shocks were measured as deviations from core inflation, based on a genetic algorithm. Bayesian quantile regression was used to estimate the impact of inflationary shocks in different levels of inequality.
Findings
The results showed that inflationary shocks substantially affect countries with higher levels of inequality, thus suggesting that the detrimental impact of inflation is exacerbated by the high division of classes in a country.
Originality/value
The study contributes to the literature about the relationship between inflation and inequality by proposing that not only the sustained increase in prices but also the inflationary shocks – the deviations from core inflation – contribute to the generation of inequality. Also, to the best of the authors knowledge, the relationship between inflation shocks and inequality in the MENA region has never been analyzed before, thus creating a research gap to provide additional empirical evidence about the sources of inequality. Additionally, the authors contribute with a methodological approach to measure inflationary shocks, based on a semelparous genetic algorithm.
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