Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: More efficient processing of nested loops? |

Date |
Wed, 15 Aug 2012 18:48:58 +0100 |

More speed-ups: bysort cum_id_cum (datadate2) : gen d2_temp_2 = datadate_2[1] bysort cum_id_cum (date_t_4) : gen dt_temp_2 = date_t_4[1] On Wed, Aug 15, 2012 at 6:07 PM, Nick Cox <njcoxstata@gmail.com> wrote: > egen cum_ab_temp = total(ab_ret) if `mark', by(cum_id_cum) > replace cum_ab = r(sum) if `mark' > > should be > > egen cum_ab_temp = total(ab_ret) if `mark', by(cum_id_cum) > replace cum_ab = cum_ab_temp if `mark' > > On Wed, Aug 15, 2012 at 5:59 PM, Nick Cox <njcoxstata@gmail.com> wrote: >> The good news is that I think you are right. This code appears to be >> much more complicated than it needs to be. >> >> I can't follow your word description -- doesn't mean it's unclear, >> just means that it is too much for me to absorb -- but from looking at >> your code there are several major and minor inefficiencies. >> >> The main problems are >> >> 1. You have two outer loops tangled together, one Stata's and one >> home-made, but neither appears necessary. >> >> 2. The inner loop is a loop over one case, and so not needed. >> >> 3. -egen- calls are very inefficient to calculate constants that >> -summarize- can calculate, except that #1 implies to me that you can >> do most of the work in a few -egen- calls. >> >> 4. Some copying of values from one place to another to no obvious purpose. >> >> With some guesswork, your code to me boils down to >> >> egen d2_temp_2 = min(datadate_2), by(cum_id_cum) >> egen dt_temp_2 = min(date_t_4), by(cum_id_cum) >> egen cusip_mode = mode(cusip), by(cum_id_cum) >> local mark "date <= d2_temp_2 & date >= dt_temp_2 & cusip_mode==cusip" >> egen cum_ab_temp = total(ab_ret) if `mark', by(cum_id_cum) >> replace cum_ab = r(sum) if `mark' >> >> Note that your use of "temporary variables" is not the same as Stata's. >> >> Nick >> >> On Wed, Aug 15, 2012 at 4:26 PM, Robson Glasscock <glasscockrc@vcu.edu> wrote: >> >>> I am running Stata 12. I have written code that creates a variable >>> that is the sum of abnormal returns for each firm. The abnormal >>> returns are accumulated from three days after the firm makes its >>> earnings announcement in quarter t-4 to three days after the firm >>> makes its earnings announcement in quarter t. My problem is that it >>> takes around 2 minutes for my code to execute for each firm/quarter >>> announcement, and there are around 150,000 earnings announcements in >>> the dataset. >>> >>> I modified a panel dataset with the earnings announcement dates for >>> each firm so that each observation contains both the quarter t >>> earnings announcement date and the quarter t-4 earnings announcement >>> date (with the three days added to each per above). I then merged this >>> dataset with a second panel dataset that contains the daily returns >>> for each firm. The merged dataset has around 10.2 million >>> observations. >>> >>> Next, I created a count variable, cum_id_cum, which is a running total >>> of each earnings announcement. This variable is truly cumulative and >>> does not reset back to 1 when the next firm releases its first >>> earnings announcement. The loop contains a variable, runn, that starts >>> with a value equal to "1" and increases by 1 each time the loop is >>> processed. I'm using that to help identify the particular dates of the >>> quarter t and quarter t-4 earnings announcement so that the abnormal >>> returns are accumulated over the correct period. Datadate_2 is the >>> quarter t earnings announcement and date_t_4 is the quarter t-4 >>> earnings announcement. Cusip is the identifier for each firm. Date is >>> the date of the firm's return in the stock market for each trading >>> day. >>> >>> The big picture of my approach was to create temporary variables that >>> will equal the cusip, datadate_2, and date_t_4 when runn equals >>> cum_id_cum. These first-step temporary variables (d2_temp, dt_temp, >>> and cusip_temp) have missing values except in the observation where >>> runn equals cum_id_cum so I created second-step temporary variables >>> (cusip_temp_2, d2_temp_2, and dt_temp_2) which place the cusip, >>> datadate_2, and date_t_4 values for each particular run through the >>> entire dataset. This allows me to mark the days for each firm that are >>> between the dates of the earnings announcements and then sum up the >>> abnormal returns in a temporary variable called cum_ab_temp. The final >>> variable with the sum of the abnormal returns for each firm is cum_ab >>> and is retained in the observation where _merge==3 (from the merge >>> mentioned in the second paragraph of this post). >>> >>> My code is below. Note that I constrain it to the first 25,000 >>> cum_id_cum values due to macro size constraints: >>> >>> gen runn= 1 >>> levelsof cum_id_cum if cum_id_cum !=. & cum_id_cum <25000, local(cum_id_cum) >>> foreach 1 of local cum_id_cum{ >>> gen d2_temp= datadate_2 if runn== cum_id_cum >>> gen dt_temp= date_t_4 if runn== cum_id_cum >>> gen cusip_temp= cusip if runn== cum_id_cum >>> egen cusip_temp_2= mode(cusip_temp) >>> egen d2_temp_2= min(d2_temp) >>> egen dt_temp_2= min(dt_temp) >>> foreach x of varlist date{ >>> replace mark= 1 if `x' <= d2_temp_2 & `x' >= dt_temp_2 & cusip_temp_2==cusip >>> egen cum_ab_temp= total(ab_ret) if mark==1 >>> replace cum_ab= cum_ab_temp if datadate_2== d2_temp & dt_temp== >>> date_t_4 & cusip_temp==cusip & d2_temp !=. & dt_temp !=. >>> replace runn= runn+1 >>> drop d2_temp >>> drop dt_temp >>> drop d2_temp_2 >>> drop dt_temp_2 >>> drop cusip_temp >>> drop cusip_temp_2 >>> drop cum_ab_temp >>> replace mark=0 >>> >>> } >>> } >>> >>> I'm wondering if there is a more efficient way to do the above which >>> will result in a significantly faster processing time. My fear is that >>> the above ignores the functionality of Stata and instead uses >>> inefficient brute force. * * 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/

**Follow-Ups**:**Re: st: More efficient processing of nested loops?***From:*Robson Glasscock <glasscockrc@vcu.edu>

**References**:**st: More efficient processing of nested loops?***From:*Robson Glasscock <glasscockrc@vcu.edu>

**Re: st: More efficient processing of nested loops?***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: More efficient processing of nested loops?***From:*Nick Cox <njcoxstata@gmail.com>

- Prev by Date:
**Re: st: -gmm- and -outreg2-** - Next by Date:
**st: create a variable with estimated coefficients on dummies** - Previous by thread:
**Re: st: More efficient processing of nested loops?** - Next by thread:
**Re: st: More efficient processing of nested loops?** - Index(es):