[R] Quantile regression model with nonparametric effect and interaction

Waltl, Sofie (sofie.waltl@uni-graz.at) sofie.waltl at uni-graz.at
Thu Jun 11 16:05:57 CEST 2015


The idea is to move from regional dummies interacted with time dummies (model 1) to a smooth spline (defined on longitudes and latitudes) interacted with time dummies (model 2), i.e.,

Model 1: Log p ~ X\beta + REGION*YEAR
Model 2: Log p ~ X\beta + f(long, lat)*YEAR

Estimating the f's in a loop therefore does not really help...

-----Original Message-----
From: Roger Koenker [mailto:rkoenker at illinois.edu] 
Sent: Donnerstag, 11. Juni 2015 15:33
To: Waltl, Sofie (sofie.waltl at uni-graz.at)
Cc: r-help at r-project.org
Subject: Re: [R] Quantile regression model with nonparametric effect and interaction

The main effect trend seems rather dangerous,  why not just estimate the f's in a loop?

url:    www.econ.uiuc.edu/~roger            Roger Koenker
email    rkoenker at uiuc.edu            Department of Economics
vox:     217-333-4558                University of Illinois
fax:       217-244-6678                Urbana, IL 61801


> On Jun 11, 2015, at 1:57 AM, Waltl, Sofie (sofie.waltl at uni-graz.at) <sofie.waltl at uni-graz.at> wrote:
> 
> Dear all,
> 
> I would like to estimate a quantile regression model including a 
> bivariate nonparametric term which should be interacted with a dummy variable, i.e., log p ~ year + f(a,b):year.
> I tried to use Roger Koenker's quantreg package and the functions rqss and qss but it turns out that interactions are not possible in this setting. Also weights are not implemented yet to build a work-around. I am looking for something like the by-statement in Simon Wood's package mgcv. Does anything comparable exist?
> I am grateful for any help on this issue.
> 
> Kind regards,
> S. Waltl
> 
> 
> 	[[alternative HTML version deleted]]
> 
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