# [R] Prediction with two fixed-effects - large number of IDs

David Winsemius dwinsemius at comcast.net
Sun Jun 18 04:36:40 CEST 2017

```> On Jun 17, 2017, at 12:01 PM, Jeff Newmiller <jdnewmil at dcn.davis.ca.us> wrote:
>
> I have no direct experience with such horrific models, but your formula is a mess and Google suggests the biglm package with ffdf.
>
> Specifically, you should convert your discrete variables to factors before you build the model, particularly since you want to use predict after the fact, for which you will need a new data set with the exact same levels in the factors.
>
> Also, your use of I() is broken and redundant.  I think formulas
>
> lny ~ id + year + x1 + I(x1^2) + x2 + I(x2^2)
>
> or
>
> lny ~ id + year + x1^2 + x2^2

This was offered as a formula to `felm` (but with no data example), a package with which I have no experience either, but if experience with `lm` and `glm` is any guide, an inferentially safer approach might be:

lny ~ id + year + poly(x1,2) + poly(x2,2)

--

David

>
> would obtain the intended prediction results.
>
> --
> Sent from my phone. Please excuse my brevity.
>
> On June 17, 2017 11:24:05 AM PDT, Miluji Sb <milujisb at gmail.com> wrote:
>> Dear all,
>>
>> I am running a panel regression with time and location fixed effects:
>>
>> ###
>>
>> reg1 <- lm(lny ~ factor(id) + factor(year) + x1+ I(x1)^2 + x2+ I(x2)^2
>> ,
>> data=mydata, na.action="na.omit")
>> ###
>>
>> My goal is to use the estimation for prediction. However, I have 8,500
>> IDs,
>> which is resulting in very slow computation. Ideally, I would like to
>> do
>> the following:
>>
>> ###
>> reg2 <- felm(lny ~ x1+ I(x1)^2 + x2+ I(x2)^2 | id + year , data=mydata,
>> na.action="na.omit")
>> ###
>>
>> However, predict does not work with felm. Is there a way to either make
>> lm
>> faster or use predict with felm? Is parallelizing an option?
>>
>> Any help will be appreciated. Thank you!
>>
>> Sincerely,
>>
>> Milu
>>
>> 	[[alternative HTML version deleted]]
>>
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>
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