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From |
ron alfieri <ron.alfieri18@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Panel data: large number of linear time trends |

Date |
Thu, 10 May 2012 23:53:47 -0400 |

Thanks Austin! On Thu, May 10, 2012 at 10:05 AM, Austin Nichols <austinnichols@gmail.com> wrote: > ron alfieri <ron.alfieri18@gmail.com> > You are using different samples in different detrending regressions. > It is easy to constrain samples, though: > > clear all > prog mydetrend, rclass byable(recall) > version 10.1 > syntax varlist [if] [in], DETrend(varname) > tempvar eps > marksample touse > regress `varlist' if `touse' > predict double `eps' if e(sample), res > replace `detrend' = `eps' if e(sample) > end > > webuse grunfeld > replace invest = . in 4 > replace invest = . in 6 > replace mvalue = . in 8 > replace mvalue = . in 13 > replace invest = . in 6 > replace invest = . in 7 > replace invest = . in 11 > replace invest = . in 15 > replace invest = . in 21 > > g i_dtr = . > g mv_dtr = . > g m=mvalue if !mi(invest) > g i=invest if !mi(mvalue) > by company: mydetrend i year, det(i_dtr) > by company: mydetrend m year, det(mv_dtr) > areg mv_dtr i_dtr, abs(company) > reg mvalue c.invest c.year##i.company > > > On Wed, May 9, 2012 at 8:15 PM, ron alfieri <ron.alfieri18@gmail.com> wrote: >> Thank you Austin! It seems that the differences are due to my panel >> being unbalanced. Using the prior example you can see that both >> methods produce different results when dropping some observations to >> make the panel unbalanced. >> >> clear all >> prog mydetrend, rclass byable(recall) >> version 10.1 >> syntax varlist [if] [in], DETrend(varname) >> tempvar eps >> marksample touse >> regress `varlist' if `touse' >> predict double `eps' if e(sample), res >> replace `detrend' = `eps' if e(sample) >> end >> >> webuse grunfeld >> replace invest = . in 4 >> replace invest = . in 6 >> replace mvalue = . in 8 >> replace mvalue = . in 13 >> replace invest = . in 6 >> replace invest = . in 7 >> replace invest = . in 11 >> replace invest = . in 15 >> replace invest = . in 21 >> >> g i_dtr = . >> g mv_dtr = . >> by company: mydetrend invest year, det(i_dtr) >> by company: mydetrend mvalue year, det(mv_dtr) >> areg mv_dtr invest, abs(company) >> areg mv_dtr i_dtr, abs(company) >> reg mvalue c.invest c.year##i.company >> >> >> If you can run the interacted version, e.g. >> reg mvalue c.invest c.year##i.company >> in the link cited, why wouldn't you? >> >> Because I have too many zip codes to include them all as covariates. >> >> Thanks again. >> >> On Wed, May 9, 2012 at 4:43 PM, Austin Nichols <austinnichols@gmail.com> wrote: >>> ron alfieri <ron.alfieri18@gmail.com>: >>> You don't show what you typed, and it is not clear what you mean by: >>> "an interaction between the fixed effect for each zip code and a >>> linear time trend" >>> --if you mean you interacted a full set of dummies with time, then I >>> would expect the same point estimates in both. >>> >>> Are you neglecting to mention other covariates perhaps? >>> >>> If you can run the interacted version, e.g. >>> reg mvalue c.invest c.year##i.company >>> in the link cited, why wouldn't you? >>> >>> On Wed, May 9, 2012 at 3:26 PM, ron alfieri <ron.alfieri18@gmail.com> wrote: >>>> I am trying to estimate a panel data model with a large number of >>>> unit-specific linear time trends (one for each zip code). >>>> >>>> I am using the method proposed here: >>>> >>>> http://www.stata.com/statalist/archive/2012-02/msg01108.html >>>> >>>> Using a subset of my data, I tried using your method and then compared >>>> the results to the results from a model where I include zip-code >>>> specific time trends by adding as covariates an interaction between >>>> the fixed effect for each zip code and a linear time trend. >>>> >>>> The results are very similar, but not identical. >>>> >>>> This is how I am interpreting the differences. When de-trending the >>>> data for one zip-code at a time your code uses only the data points >>>> from that zip code. However, all data points are used when estimating >>>> zip-code specific trends by adding as covariates the interactions >>>> between the fixed effect for each zip code and a linear trend (with >>>> “all data points” I mean even the data points where these interactions >>>> take the value of zero that are not used when doing it one zip code at >>>> a time). >>>> >>>> I would appreciate any comments on whether I am interpreting the >>>> differences between these two methods correctly. If anyone has an >>>> insight on whether one of the methods is more “appropriate” than the >>>> other that would be great. >>>> >>>> Aaron > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Panel data: large number of linear time trends***From:*ron alfieri <ron.alfieri18@gmail.com>

**Re: st: Panel data: large number of linear time trends***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: Panel data: large number of linear time trends***From:*ron alfieri <ron.alfieri18@gmail.com>

**Re: st: Panel data: large number of linear time trends***From:*Austin Nichols <austinnichols@gmail.com>

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