Hello,   I see this question is still open after some weeks. @kedii_9 : As you probably know, PROC TRAJ is not a SAS developed procedure. The TRAJ procedure is a procedure that was written and distributed by a third party, therefore, it is not a procedure that is supported by SAS Institute. For questions or assistance regarding this procedure, you will need to contact the author directly. You can always try this communities site of course (as you have done) but if nobody answers you cannot turn to SAS Technical Support, you need to turn to the author of the 'package'.   Cheers, Koen ... View more

Ah OK, @Kurt_Bremser , I thought the questioner was after finding out how many and which observations were logically deleted (and NOT physically deleted). But I guess you're program is offering what (s)he is looking for. Cheers, Koen ... View more

Hello @baladim , The Technical Support track was initiated by you. So you were the first to get the full response (including test program). I just explain this to clarify why I only post the summary in this thread and not the full, elaborate response from TS. Koen ... View more

Hello @baladim and _ALL_, This question was solved by investigation through Technical Support. To be short on the conclusion: PACF formula in PROC ARIMA is correct. The reason on why you do not obtain exactly the same results as with a regression approach is the (different n° of) observations used (as I suggested in my posts in this thread). For example, if you take the lag of x, you don't have the full sample anymore. PACF is using the full length n of the series, so more observations, other denominator(s).   Cheers, Koen ... View more

You can also read this and have a look at the recorded webinar: What Are Best Practices for Using SAS® Survey Procedures? Q&A, Slides, and On-Demand Recording https://communities.sas.com/t5/Ask-the-Expert/What-Are-Best-Practices-for-Using-SAS-Survey-Procedures-Q-amp-A/ta-p/706830 ... View more

Hello,   There's no need to do a BON (Bonferroni) correction on top of the TUKEY correction.  To me a TUKEY correction is often a good choice (less conservative than Bonferroni). But feel free to select the best correction method for your situation: Here's the doc of the ADJUST= option (for multiple comparison p-value adjustment). https://go.documentation.sas.com/?cdcId=pgmsascdc&cdcVersion=9.4_3.5&docsetId=statug&docsetTarget=statug_introcom_sect041.htm&locale=en#statug.introcom.lsmadjust LSMEANS statement (across procedures):  https://go.documentation.sas.com/?cdcId=pgmsascdc&cdcVersion=9.4_3.5&docsetId=statug&docsetTarget=statug_introcom_sect040.htm&locale=en   If you do not know what to choose: choose ADJUST=BON.   Cheers, Koen ... View more

Hello,   Do you mean that you do not get parameter estimates for the explanatory variables smb and hml? In the picture of your dataset, the two variables (smb and hml) are constant. That's maybe / probably why. It would be better to include the LOG. That is more telling than just the code.   Cheers, Koen   ... View more

Hello,   You ask for a multiple-comparison adjustment of LS-means differences by the ADJUST= option on the LSMEANS statement, so you get it. 🙄 As method, you specify TUKEY (ADJUST=TUKEY) so it's a Tukey adjustment for the p-value. More info in the doc. https://go.documentation.sas.com/?docsetId=helpcenterwlcm&docsetTarget=home.htm&docsetVersion=1.0&locale=en   Cheers, Koen ... View more

Great! I hadn't thought about that solution. It has been a long time since I have used the <<idgroup>> in the output statement of PROC SUMMARY (or PROC MEANS). Thanks, Koen ... View more

Hello, I added the auto-regression by PROC ARIMA. The last coefficient of the AR(6) model is not equal to the partial autocorrelation coefficient (PACF) at lag 6 in the plot is about equal to the estimate of lag6_x in a PROC REG (small diff. due to other parameter estimation algorithm, sign difference due to parameter being at the other end of the equal sign). I cannot investigate any further. No time left. If nobody picks this up (everybody can easily submit my program as it uses a SASHELP dataset everyone had available), I will ask our Technical Support department tomorrow. QUIT; ods trace off; ods output SeriesCorrPanel=work.SeriesCorrPanel; ods exclude DescStats ChiSqAuto; proc arima data=sashelp.citimon /*plots=none*/; identify var=EXVUS; run; QUIT; data work.SeriesCorrPanel; set work.SeriesCorrPanel; keep LAGS_SERIES_MAX_NLAGS_ PACF_SERIES_NLAGS_NLAGS_ TIME; where LAGS_SERIES_MAX_NLAGS_=6; run; data work.for_proc_reg(keep=date EXVUS lag:); set sashelp.citimon; *lag0_EXVUS=EXVUS; lag1_EXVUS=lag1(EXVUS); lag2_EXVUS=lag2(EXVUS); lag3_EXVUS=lag3(EXVUS); lag4_EXVUS=lag4(EXVUS); lag5_EXVUS=lag5(EXVUS); lag6_EXVUS=lag6(EXVUS); run; ods output ARPolynomial=work.ARPolynomial; ods exclude ods exclude DescStats ChiSqAuto ParameterEstimates FitStatistics CorrB ResidualCorrPanel ResidualNormalityPanel ModelDescription; proc arima data=sashelp.citimon /*plots=none*/; identify var=EXVUS; run; estimate p=6; run; QUIT; data work.ARPolynomial; set work.ARPolynomial; where Term='B**(6)'; run; ods output ParameterEstimates=work.REG_ParameterEstimates; ods exclude NObs ANOVA FitStatistics; proc reg data=work.for_proc_reg plots=none; model EXVUS = lag1_EXVUS lag2_EXVUS lag3_EXVUS lag4_EXVUS lag5_EXVUS lag6_EXVUS; run; QUIT; data work.REG_ParameterEstimates; set work.REG_ParameterEstimates; where variable='lag6_EXVUS'; run; data work.merge_three_ds; LENGTH PACF_SERIES_NLAGS_NLAGS_ Estimate Coefficient 8; merge work.SeriesCorrPanel (keep=PACF_SERIES_NLAGS_NLAGS_) work.REG_ParameterEstimates(keep=Estimate) work.ARPolynomial (keep=Coefficient); run; /* end of program */ Cheers, Koen ... View more

Hello,   My MAKRIDAKIS book is much too old (from the eighties), so I cannot locate the section you mention (it's not on the same page-number for sure). I made following little program and can indeed see that for lag 6 both estimates are not equal at all (not even near). I took lag 6 as an example but you can modify the program to investigate the same for any lag. I will investigate further end-of-afternoon (Brussels time). QUIT; ods trace off; ods output SeriesCorrPanel=work.SeriesCorrPanel; ods exclude DescStats ChiSqAuto; proc arima data=sashelp.citimon /*plots=none*/; identify var=EXVUS; run; QUIT; data work.SeriesCorrPanel; set work.SeriesCorrPanel; keep LAGS_SERIES_MAX_NLAGS_ PACF_SERIES_NLAGS_NLAGS_ TIME; where LAGS_SERIES_MAX_NLAGS_=6; run; data work.for_proc_reg(keep=date EXVUS lag:); set sashelp.citimon; *lag0_EXVUS=EXVUS; lag1_EXVUS=lag1(EXVUS); lag2_EXVUS=lag2(EXVUS); lag3_EXVUS=lag3(EXVUS); lag4_EXVUS=lag4(EXVUS); lag5_EXVUS=lag5(EXVUS); lag6_EXVUS=lag6(EXVUS); run; ods output ParameterEstimates=work.REG_ParameterEstimates; ods exclude NObs ANOVA FitStatistics; proc reg data=work.for_proc_reg plots=none; model EXVUS = lag1_EXVUS lag2_EXVUS lag3_EXVUS lag4_EXVUS lag5_EXVUS lag6_EXVUS; run; QUIT; data work.REG_ParameterEstimates; set work.REG_ParameterEstimates; where variable='lag6_EXVUS'; run; data work.merge_both_ds(drop=label); LENGTH PACF_SERIES_NLAGS_NLAGS_ Estimate 8; merge work.SeriesCorrPanel work.REG_ParameterEstimates; run; /* end of program */ Cheers, Koen   ... View more

Hello @baladim , OK. I fished the MAKRIDAKIS book out of my library and will do an investigation this afternoon (Brussels time). Stay tuned! I have to add: the testing and quality assurance of SAS procedures is very detailed, very elaborate and of very high quality overall. And moreover, the ARIMA procedure is very old (some decades), thus some decades of user-testing are added on top. That's why I am sure you are (we are) overlooking something. I have no doubt PROC ARIMA is correct. But your question deserves investigation of course. More info to come this afternoon. Koen   ... View more