Public Versus Private Sector Wage Gap in Egypt: Evidence From Quantile Regression on Panel Data

This study unfurls the non-linear behaviour of regional house prices in the United Kingdom by employing quarterly observations spanning the period 1992Q1–2017Q4. Our enquiry aims at examining UK house prices within a multivariable framework and, more specifically, by employing panel quantile regression with fixed effect. In brief, the empirical findings obtained from these methodologies indicate that the UK house prices are influenced at lower and upper quantiles, and that precisely they are influenced by variables such as income, private sector housing starts and employment. We highly support that there is a strong heterogeneity among UK regions and that asymmetry may be one of the keys of the ripple effect. Particularly, the income shows a positively significant performance at lower and higher regional house prices. Moreover, the variables private sector housing starts and employment rate are statistically significant for house prices. Leveraging for first-time panel quantile regression for the case of regional house prices in the UK, policymakers will have a profound understanding of regional house prices. JEL Classifications: C22, R21, R31

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On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study

Econometrics ◽

10.3390/econometrics3030654 ◽

2015 ◽

Vol 3 (3) ◽

pp. 654-666 ◽

Cited By ~ 8

Author(s):

Antonio Galvao ◽

Gabriel Montes-Rojas

Keyword(s):

Monte Carlo ◽

Panel Data ◽

Quantile Regression ◽

Monte Carlo Study ◽

Bootstrap Inference

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Shrinkage quantile regression for panel data with multiple structural breaks

Canadian Journal of Statistics ◽

10.1002/cjs.11659 ◽

2021 ◽

Author(s):

Liwen Zhang ◽

Zhoufan Zhu ◽

Xingdong Feng ◽

Yong He

Keyword(s):

Panel Data ◽

Quantile Regression ◽

Structural Breaks ◽

Multiple Structural Breaks

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Dynamic Quantile Panel Data Analysis of Stock Returns Predictability

International Journal of Economics and Finance ◽

10.5539/ijef.v8n2p115 ◽

2016 ◽

Vol 8 (2) ◽

pp. 115 ◽

Cited By ~ 1

Author(s):

Bülent Guloglu ◽

Sinem Guler Kangalli Uyar ◽

Umut Uyar

Keyword(s):

Panel Data ◽

Quantile Regression ◽

Stock Returns ◽

Conditional Distribution ◽

Extreme Values ◽

Dynamic Panel Data ◽

Regression Technique ◽

Heterogeneous Structure ◽

Dynamic Panel ◽

Different Parts

<p>This paper analyses the effect of financial ratios on stock returns using quantile regression for dynamic panel data with fixed effects. Eighty three firms of manufacturing industry, which were traded on the Borsa Istanbul for 2000-2014 period, are covered in the study. The most of financial variables have heterogeneous structure so they generally include extreme values. Thus, panel quantile regression technique, suggested by Koenker (2004), is used. Since the technique yields robust estimator in the case of extreme values the Gaussian estimators will be biased and not efficient. The sensitivity of relationship, on the other hand, can be studied for different parts of the stock returns’ conditional distribution by using quantile regression technique. However, because of that the lagged of dependent variable is used as an explanatory variable in dynamic panel models, fixed effect estimators will be biased. Thereby, in this study the instrumental variable approach suggested by Chernozhukov and Hansen (2006) is used to produce unbiased and consistent estimators.</p>The results show that the stock returns respond to the changes on the financial leverage ratio, the dividend yield, the market-to-book value ratio, financial beta and the total active profitability variables differently for the different parts of the stock returns’ conditional distribution. They also indicate that, at high quantiles, return fluctuations in the current period will be more effective for investors’ transaction attitudes on stocks for the next period.

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