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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 17:59:15 +0100 |

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:*Nick Cox <njcoxstata@gmail.com>

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

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