Conditional Quantile Regression Analysis
Foreign Aid, Terrorism and Growth: Conditional Evidence from Quantile Regression
... 21 clarifying why and how terrorism may interact with foreign aid to exert a negative effect on economic growth. It would also be motivating to position a study on a post-2011 sample to assess how recent terrorism trends ...
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Using Quantile Regression for Duration Analysis
... Quantile regression model the changes of quantiles of the conditional distribution of the du- ration in response to changes of the ...duration analysis, re- searchers are often interested in ...
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Using Quantile Regression for Duration Analysis
... models. Quantile regression do not impose a proportional effect of the covariates on the ...estimated quantile regression coefficients change sign across quantiles and we show that this holds ...
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Innovation and market value: A quantile regression analysis
... The regression methodology of this literature has typically been based on standard least- squares ...use regression estimators that implicitly focus on the ‘average effect for the average firm’ by giving ...
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Specification Analysis of Structural Quantile Regression Models
... by conditional quantile restrictions involving instrumental ...each quantile improves power performance while at the same time serving to facilitate the simulation of critical values via the ...
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Innovation Sources and Productivity: A Quantile Regression Analysis.
... The effect of innovative activity on growth and productivity at firm level has received much attention in recent years. The increased availability of micro-level data from innovation surveys in the EU has led to a ...
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Partial quantile regression
... response. Quantile regression (QR) estimates the quantiles of the response distribution by regression functions of the covariates, and so gives a fuller description of the response than does the ...
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Conditional autoregressive valu at risk by regression quantile: Estimatingmarket risk for major stock markets
... the quantile of future portfolio values conditional on current information, Engle and Manganelli (2004) propose a new approach to quantile estimation that does not require any of the extreme ...
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Nonparametric Inferences on Conditional Quantile Processes
... nonparametric quantile regression model considered in this paper and provides informal details regarding a natural estimation procedure for members of that ...of quantile regression model ...
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Quantile regression through quantization
... (α < 1/2). Such reference curves and prediction intervals are widely used, e.g. in economics, ecology, or lifetime analysis. In medicine, they are used to provide reference growth curves for children’s height ...
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Bayesian spatial quantile regression
... spatial quantile regression model that does not assume normality and allows the covariates to affect the entire conditional distribution, rather than just the ...The conditional distribu- tion ...
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CAViaR: Conditional Value at Risk by Quantile Regression
... a quantile of future portfolio values conditional on current information, we propose a new approach to quantile estimation which does not require any of the extreme assumptions invoked by existing ...
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Objective Bayesian analysis on the quantile regression
... Bayesian analysis on the two-piece location-scale models, which contain several well-known sub- distributions, such as the asymmetric Laplace distribution, the -skew normal distribution, and the skewed Student-t ...
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Conditional determinants of FDI in fast emerging economies: an instrumental quantile regression approach
... This paper has complemented the underlying literaure on FDI in developing countries by assesing the determinants of FDI in nine fast growing developing countries, notably: the BRICS (Brazil, Russia, India, China & South ...
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Bayesian analysis of a Tobit quantile regression model
... the quantile regression vector that lead to proper posterior distributions with finite ...alternative quantile regression models is also developed and ...
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Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression
... vector regression and the pinball loss for quantile ...Approximation analysis is conducted for these algorithms by means of a variance-expectation bound when a noise condition is satisfied for the ...
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Bayesian Quantile Regression.
... the quantile function of the regression equation with the spline coefficients being linear, it is enough to track the posterior mean of the spline coefficients for the estimation pur- ...estimated ...
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Bayesian quantile regression
... Recent work by Schennach(2005) has opened the way to a Bayesian treat- ment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for ...
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Local Quantile Regression
... Quantile regression estimates for a class of linear and partially linear errors-in-variables model. Ann[r] ...
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Generalized quantile regression
... v(x) | . Nach Vorbild der asymptotischen Theorie konstruieren wir simultane Kon- fidenzb¨ ander um die gesch¨ atzte Expektilfunktion. Wir entwickeln einen funk- tionalen Datenanalyseansatz um eine Familie von ...
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