scholarly journals Panel Data Quantile Regression for Treatment Effect Models

2020 ◽  
Author(s):  
Takuya Ishihara
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Treatment Effect ◽  
Treatment Effect Models

scholarly journals Dynamic Quantile Panel Data Analysis of Stock Returns Predictability

2016 ◽  
Vol 8 (2) ◽  
pp. 115 ◽  
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.

scholarly journals Quantile Regression for Panel Data: An Empirical Approach for Knowledge Spillovers Endogeneity

2017 ◽  
Vol 9 (7) ◽  
pp. 106 ◽  
Author(s):  
Luigi Aldieri ◽  
Concetto Paolo Vinci
Keyword(s):  
Theoretical Model ◽  
Panel Data ◽  
Quantile Regression ◽  
Knowledge Spillovers ◽  
Spillover Effect ◽  
Technology Spillovers ◽  
Significant Heterogeneity ◽  
The Core ◽  
Endogenous Regressors ◽  
Different Levels

The aim of this paper is to investigate the extent to which knowledge spillovers effects are sensitive to different levels of innovation. We develop a theoretical model in which the core of spillover effect is showed and then we implement the empirical model to test for the results. In particular, we run the quantile regression for panel data estimator (Baker, Powell, & Smith, 2016), to correct the bias stemming from the endogenous regressors in a panel data sample. The findings identify a significant heterogeneity of technology spillovers across quantiles: the highest value of spillovers is observed at the lowest quartile of innovation distribution. The results might be interpreted to provide some useful implications for industrial policy strategy.

Quantile regression for panel data models with fixed effects under random censoring

2019 ◽  
Vol 49 (18) ◽  
pp. 4430-4445
Author(s):  
Dai Xiaowen ◽  
Jin Libin ◽  
Tian Yuzhu ◽  
Tian Maozai ◽  
Tang Manlai
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Fixed Effects ◽  
Data Models ◽  
Panel Data Models ◽  
Random Censoring

Treatment Effects and Informative Missingness with an Application to Bank Recapitalization Programs

2014 ◽  
Vol 104 (5) ◽  
pp. 212-217 ◽  
Author(s):  
Angela Vossmeyer
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Sample Selection ◽  
Financial System ◽  
Bayesian Framework ◽  
Lender Of Last Resort ◽  
Treatment Effect Models ◽  
Bank Recapitalization

This article develops a Bayesian framework for estimating multivariate treatment effect models in the presence of sample selection. The methodology is applied to a banking study that evaluates the effectiveness of lender of last resort (LOLR) policies and their ability to resuscitate the financial system. This paper employs a novel bank-level dataset from the Reconstruction Finance Corporation, and jointly models a bank's decision to apply for a loan, the LOLR's decision to approve the loan, and the bank's performance a few years after the disbursements. This framework offers practical estimation tools to unveil new answers to important regulatory questions.

Comparison of International Differences in the Volatility of Economic Growth and Non-Performing Loan Ratio: A Statistical Study Based on the Quantile Regression Model

2017 ◽  
Vol 21 (6) ◽  
pp. 1094-1101
Author(s):  
Song Qin ◽  
Zhenlei Wang ◽  
◽  
◽  
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Quantile Regression ◽  
Regression Models ◽  
Banking Sector ◽  
Statistical Study ◽  
Quantile Regression Model ◽  
The Us ◽  
The Impact ◽  
The Relationship

What is the level of non-performing loans in China’s banking sector and in different countries? Has the relationship between economic growth and the non-performing loan ratio changed? Is there a difference in the effect of the economic growth of different economies on the rate of non-performing loans in the banking sector? This study analyzes the relationship between economic growth and the non-performing loan ratios and characteristics of 13 countries from 2005-2014 based on quantile regression models with panel data. The results showed that the relationship between economic growth and the non-performing loan ratio was positive before the financial crisis in 2008 but was negative after 2008. The non-performing loan ratio in Canada, Mexico, and the US was low before 2008 and high after 2008. The impact of economic growth on the non-performing loan ratio was more significant for countries with a high non-performing loan ratio than for countries with a low non-performing loan ratio.

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