# Tagged Questions

Quantile regression allows us to estimate the effect of a set of predictor variables over the entire distribution of the outcome variable or any particular quantile.

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### Estimate quantiles by mixed model quantile regression

lqmm::lqmm returns a 95th percentile lower than the 90th, using the data and parameterization below. The goal is only to estimate these upper percentiles, accounting for any within-person dependency ...
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### Is it valid to instrument endogenous variables one at a time?

I am a Stata user and am attempting to implement the IV Quantile regression method of Chernozhukov and Hansen (2008), for my model, which has two endogenous variables, call them "GD" and "ED". I am ...
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### Fit quantile regression

I have like 30k rows in training set and 60k in test set. Distribution of dependent variable (ascending order) looks like this: I believe I must use quantile regression here, as OLS regressions fit ...
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### When the dependent variable is Median Time of a specific event, have we to use Quantile Regression or OLS Regression works well?

I am asked to make a regression model and the dependent variable is Median Time of a specific event. I think I can consider the Median Time of that event as a random variable and therefore the ...
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### Why use quantile regression instead of splitting the data in quantiles and calculating multiple linear regressions?

Why use quantile regression instead of splitting the data in quantiles and calculating multiple linear regressions? What are the advantages and disadvantages of these methods? As far as I understand ...
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### Detrending daily precipitation and temperature data for quantile regression analysis

I am carrying out a linear quantile regression analysis to detect long term anthropogenic changes in precipitation and temperature data. The regressions are being used to compare the slopes between ...
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### Quantile regression versus OLS with dummies

I want to regress a variable Y on another variable X (with appropriate control variables and fixed effects) in a panel data setting. Two approaches come to mind: Use quantile regression; Use OLS ...
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### Conditional quantile function - Linear quantile regression model?

For instance, I know that if $Q_\tau (y|x) = \beta (\tau)^{'} x$ then the linear quantile regression can be written as $y_i = \beta^{'}x_i + \epsilon_i$ where $$Q_\tau (\epsilon_i |x_i)=0$$ However,...
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### Quantile instrumental variable models (Chernozhukov and Hansen)

I am trying to implement a quantile IV model, and I must confess that I'm not fully familiar (read: comfortable) with the theory, although I have read the Chernozhukov and Hansen paper. However, the ...
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### Can quantile regression be used to pool multiply imputed count data?

I am using the mice package in R to impute missing data in small study. The study investigates the effect of a behavioral intervention on the frequency of a particular behavior, i.e., count data that ...
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### Empirical Prediction interval for time series forecast based on quantile regression

As Gardner notes "almost all point forecasts are wrong", so prediction intervals (PI) are necessary to quantify uncertainty and help us make informed decisions. There exists theoretical PI, and in ...
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### Heteroscedastic censored regression

I am dealing with a heteroscedastic censored dataset. I tried to use the survival analysis package in R to estimate a linear model for it. So before doing that, I conducted a simulation study, where I ...
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### Is it possible to get a prediction interval for logistic regression via a latent variable?

carbocation asked how to compute prediction intervals for logistic regression. The answer was that prediction intervals don't make sense for logistic regression because the response variable only ...