The quantile-regression tag has no wiki summary.
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Quantile regression with Stata
Is there a way to test the equality of quantile regression coefficients in one go using Stata? For example can I do the comparison of the coefficients of the 10th, 25th, 50th, 75th and the 90th ...
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Ways to find a confidence interval for robust and quantile regressions
I'm trying to compare a few regression models for my data. For linear regression everything is quite understandable, but robust and quantile regressions are not so obvious. I could not find almost ...
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How to calculate the weighted sum of absolute deviations to determine AIC for quantile regression
I would like to know if there is a way to calculate the sum of the weighted absolute deviations for quantile regressions with package quantreg?
I'm following the ...
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Quantile regression standard error and the OLS standard error
I was asked by a non-statistician what is the driving factor for the standard error estimates of the parameters. Here are my thoughts:
From asymptotic theory, both OLS standard error and Quantile ...
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Calulating a mean coefficient effect based on quantile regression estimates
I have used quantile regression to estimate a particular coefficient for the 10th, 20th, ... 90th percentiles. Now, I want to estimate the mean effect of the coefficient across the whole ...
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Model performance in quantile modelling
I am using quantile regression (for example via gbm or quantreg in R) - not focusing on the median but instead an upper quantile ...
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Quantile regression with one predictor
Is there any closed formula for quantile regression with only one predictor?
Motivation
I need to implement in SQL median regression with one predictor. It is quite easy to implement OLS with one ...
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202 views
What are the assumptions for quantile regression?
What assumptions must be fulfilled in quantile regression?
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Quantile regression: Which standard errors?
The summary.rq function from the quantreg vignette provides a multitude of choices for standard error estimates of quantile regression coefficients. What are the ...
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Quantile regression analyzing the conditional quantiles of one of the regressors?
Given response $Y_t$ and predictor $X_t$, we can use OLS to analyze the conditional mean; $E[Y_t | X]$. Quantile regression can be used to analyze the conditional quantile function; $Q(Y_t(\tau)|X)$. ...
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What are the red lines in quantile regression plot (quantreg package)?
Using plot.rq in the quantreg package in R, we can plot the coefficient estimate distribution, and get something like this:
...
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Quantile regression and heteroscedasticity/autocorrelation
I hear it said [1] that QR makes no distribution assumptions about its error term.
Question 1: Does this mean that heteroscedastic and serially correlated disturbances do not effect the ...
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Quantile regression with dummy variable that's equal to 0 over most $t$
I have the model $Y_t = a + b*X_t + c*D_t + e_t$, where $t \in T = \{1,...,3000\}$ and $D_t$ is a binary variable equal to $0$ over $T \backslash \{20,21,...,30\}$, and equal to $1$ over ...
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When is quantile regression worse than OLS?
Apart from some unique circumstances where we absolutely must understand the conditional mean relationship, what are the situations where a researcher should pick OLS over Quantile Regression?
I ...
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(Quantile regression) AR(1) variable in the design matrix
I'm not doing a pure QAR (quantile auto regression) but I do have a lagged dependent variable (AR(1)) as a predictor. I'm using the quantreg package in ...
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1answer
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(Quantile regression) Which standard error for heteroscedasticity & serial correlation
I have heteroscedastic and autocorrelated residuals in my multivariate quantile regression model.
What's the quantile regression standard error estimator that's robust to this? Something hopefully ...
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1answer
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Presenting the error term in a quantile regression specification
Let $Y_i$ be the response and $X_i$ be the independent variables. Whenever I've seen a quantile regression specification they'll go:
$Q_{\tau}(Y_i | X_i) = a(\tau) + b(\tau) X_i$
Or, alternatively:
...
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How to estimate CAViaR (Engle and Manganelli 2004) using non linear quantile regression?
I am tring to replicate results from Engle and Manganelli (2004). The following is one of their specifications,
$q_t(\theta)=\gamma_0+\gamma_1q_{t-1}(\theta)+\alpha|r_{t-1}|$,
q is the quantile of ...
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Optimizing regression coefficients to predict the largest outcomes
What is a sound methodology to improve the efficiency of the regression coefficients when we are interested in predicting the larger values of the marginal distribution (tails)?
For example, we want ...
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328 views
Explaining quantile regression to nonstatisticians
I recently submitted a paper, in which I used quantile regression, to a psychology journal. Although I thought I had already put enough thought in a clear exposition of quantile regression, the ...
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Logistic quantile regression – how to best convey the results
In a previous post I’ve wondered how to deal with EQ-5D scores. Recently I stumbled upon logistic quantile regression suggested by Bottai and McKeown that introduces an elegant way to deal with ...
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373 views
Quantile regression prediction
I am interested in using quantile regression for some of my models, but would like to have some clarifications on what can I achieve using this methodology. I understand I can obtain a more robust ...
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What diagnostic plots exists for quantile regression?
Following on my question for OLS, I wonder: what diagnostic plots exists for quantile regression? (and are there R implementation of them?)
A quick google search already came up with the worm plot ...
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1answer
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About interpretation of the results of quantile regression
After applying quantile regression with t=0.5,0.6 on the data set WBC(
Wisconsin Breast Cancer)with 678 observations and 9 independent
variables($inp_1,inp_2,...inp_9$) and 1 dependent variable(op) I ...
