scholarly journals QUANTILE REGRESSION OF GERMAN STANDARD FARMLAND VALUES: DO THE IMPACTS OF DETERMINANTS VARY ACROSS THE CONDITIONAL DISTRIBUTION?

2018 ◽  
Vol 50 (4) ◽  
pp. 453-477 ◽  
Author(s):  
FRIEDERIKE LEHN ◽  
ENNO BAHRS
Keyword(s):  
Quantile Regression ◽  
Conditional Distribution ◽  
Price Caps ◽  
Price Increases ◽  
German Standard ◽  
Farmland Prices ◽  
Political Intervention ◽  
Price Determinants ◽  
Farmland Values

Abstract Because of considerably increased farmland prices, not only in Germany, the question arises whether farmland is still affordable for farmers. Hence, there is a call for price caps. If farmland prices are to be capped by political intervention, identifying the main farmland price determinants especially for the highest prices is essential. Using quantile regression for German standard farmland values, we find heterogeneous relationships across the estimated quantiles for several covariates. Nonagricultural factors are often more pronounced at the upper tail of the conditional distribution. We recommend focusing primarily on factors in the upper quantiles to prevent further farmland price increases.

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.

Quantile Regression for Panel Data and Factor Models

2021 ◽  
Author(s):  
Carlos Lamarche
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Conditional Distribution ◽  
Educational Achievement ◽  
Theoretical Work ◽  
Average Effect ◽  
High Achieving Students ◽  
Time Specific ◽  
Heterogeneous Effect ◽  
The Relationship

For nearly 25 years, advances in panel data and quantile regression were developed almost completely in parallel, with no intersection until the work by Koenker in the mid-2000s. The early theoretical work in statistics and economics raised more questions than answers, but it encouraged the development of several promising new approaches and research that offered a better understanding of the challenges and possibilities at the intersection of the literatures. Panel data quantile regression allows the estimation of effects that are heterogeneous throughout the conditional distribution of the response variable while controlling for individual and time-specific confounders. This type of heterogeneous effect is not well summarized by the average effect. For instance, the relationship between the number of students in a class and average educational achievement has been extensively investigated, but research also shows that class size affects low-achieving and high-achieving students differently. Advances in panel data include several methods and algorithms that have created opportunities for more informative and robust empirical analysis in models with subject heterogeneity and factor structure.

Exploring the Tropospheric Response to Stratospheric Variability Using Lagged Quantile Regression

2021 ◽  
Author(s):  
Kathrin Finke ◽  
Abdel Hannachi
Keyword(s):  
Quantile Regression ◽  
Conditional Distribution ◽  
Polar Vortex ◽  
Supplementary Information ◽  
Reanalysis Dataset ◽  
Stratospheric Polar Vortex ◽  
Response Variable ◽  
Tropospheric Circulation ◽  
The Impact ◽  
Stratospheric Variability

&lt;p&gt;Stratospheric variability has become increasingly popular due to its potential impact on the tropospheric circulation. Extreme states of the stratospheric polar vortex have been associated with reoccurring tropospheric weather patterns more than 2-3 weeks after the initial stratospheric signal. Standard linear regression methods used to assess the statistical stratosphere-troposphere connection estimate the distribution's mean effect of a stratospheric variable as a predictor on a tropospheric response variable. However, &amp;#160;supplementary information of the impact of extreme stratospheric behavior is hidden in the tails of the distribution, revealing a different behavior than the mean. Therefore, we use quantile regression, a method that enables us to model the complete conditional distribution of the response variable. This presentation explores various quantiles of the conditional distribution to investigate the impact of stratospheric variability on the tropospheric circulation using the ERA5 reanalysis dataset. Comparison between (lagged) linear and (lagged) quantile regression reveals significant differences making the latter method a neat tool that offers valuable information about the statistical connection between the stratosphere and the troposphere.&lt;/p&gt;

scholarly journals The impact of air pollution on birthweight: evidence from grouped quantile regression

2021 ◽  
Author(s):  
Martina Pons
Keyword(s):  
Air Pollution ◽  
Quantile Regression ◽  
Conditional Distribution ◽  
Group Level ◽  
Average Effect ◽  
Lower Tail ◽  
Individual Level ◽  
Distributional Effect ◽  
The Impact ◽  
Particulate Matter Pollution

AbstractEstimates of the average effect of pollution on birthweight might not provide a complete picture if more vulnerable infants are disproportionately more affected. To address this, I focus on the distributional effect of particulate matter pollution (PM$$_{2.5}$$ 2.5 ) on birthweight. To estimate the impact, this paper uses grouped quantile regression, a methodology developed by Chetverikov et al. (Econometrica 84(2): 809–833, 2016), which allows estimating the impact of a group-level treatment on an individual-level outcome when there are group-level unobservables. The analysis reveals nonhomogeneous effects indicating that pollution disproportionately affects infants in the lower tail of the conditional distribution, whereas average effects suggest only minimal and not economically significant impact of pollution on birthweight. The findings are also consistent across different specifications.

scholarly journals The impact of natural amenity on farmland values: A quantile regression approach

2013 ◽  
Vol 33 ◽  
pp. 151-160 ◽  
Author(s):  
Hiroki Uematsu ◽  
Aditya R. Khanal ◽  
Ashok K. Mishra
Keyword(s):  
Quantile Regression ◽  
Regression Approach ◽  
Farmland Values ◽  
The Impact ◽  
Natural Amenity
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