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:rkoen...@illinois.edu] 
Sent: Donnerstag, 11. Juni 2015 15:33
To: Waltl, Sofie (sofie.wa...@uni-graz.at)
Cc: r-help@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    rkoen...@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.wa...@uni-graz.at) 
> <sofie.wa...@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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