Hello, Based on the attached SAS data file (in the zip file), I need to match Big R/Little R (Big_R variable, Big R = 1 and Little R = 0) based on the following: 1. Same fyear (fyear variable).  2. Same 2-digit SIC (SIC variable). 3. Same Effect (Positive or Negative) (Effect variable) 4. Same Restatement Types (Accounting, Fraud, Errors, and OtherIssues variables).  If there is a , in between restatement types, then that indicates that there is more than 1 type of restatement.  For example, if a Little R has two types of restatements (1,2) and a Big R has one type of restatement (1), then that is OK for a match.  However, if it is the opposite (where Big R has more restatement types than Little R, then that CANNOT qualify for a match).  The types have to match as well.  For example, if Big R only has 1 restatement type (1) and Little R has 3 (2,3,4), then that CANNOT qualify for a match since the restatement type of Big R is not included in the restatement types of Little R. 5. Closest in ln Assets (lnAssets variable) 6. Closest in ROA (ROA variable)   In other words, if conditions 1 through 4 are met (matches) then I need a Big R/Little R match based on conditions 5 and 6.    Thanks so much - I really appreciate this! Jadallah ... View more

Hello, I currently have a dataset with 1 observation per firm in various years.  For example, I have a firm with a 1998 observation.  Is there a way to code it where I can get lines for 1995, 1996, 1997, 1999, 2000, and 2001 observations for the same firm (3 years before and 3 years after)?  That way, instead of having 1 observation per firm, I will have 7.  Finally, I would like to delete the original observation (1998 in my example).   Thanks so much! Jadallah ... View more

Hello, I'm working on a project where I would like to see if there is a correlation between corporate social responsibility (CSR) issuance and stock return.  I have 316 cases of firms issuing CSR reports and the year (i.e., company ABC in 1994 and company XYZ in 1998).  I coded these cases as 1 in a separate CSR variable indicating that these firms initially issued a CSR report in whichever year the initial issuance was.     For these cases, I need to match them with firms that did not issue a CSR report in that year or any other year.  I need to match each firm with another firm (control firm) in the same SIC code (same industry), and with the smallest differences in total assets (AT) and return percentage (RET) in the year that the CSR reporting firm issued its initial report.   For example, a company with CIK (my ID variable) 0000001800 issued its initial CSR report in 1999.  I need to match this firm with another firm that never issued a CSR report (in 1999, before, or after), a firm that is in SIC code 2834 (which is company 0000001800's SIC code), and one that is closest to company 0000001800 in AT and RET during 1999.  For these 2 firms (0000001800 and control firm), I need to keep the data for 1998, 1999, and 2000 (year before, year of, and year after initial CSR report issuance).     How do I code to get what I need above?     Thanks a lot and best regards, Jadallah ... View more

Hello SAS support, Step 3 of the following code is not working.  I want fqtr=4 and fpi=9.  The first 2 steps work fine, but the problem is with the 3rd step (fpi=9).  SAS gives me an error message saying ERROR: WHERE clause operator requires compatible variables.  If this gets fixed, then I would think the rest of this code would work. data etr.d; set etr.combined; input permno fqtr fpi fyear rdq date9.; cards; 10001 2 9 2012 05jul2014 10001 4 9 2010 08nov2015 10002 4 9 2009 09oct2015 10003 1 9 2012 002jan2015 10004 4 9 2013 01Dec2015 ; run; data etr.e; set etr.d(where=(fqtr=4)); run; data etr.f; set etr.e(where=(fpi=9)); run; proc sort data=etr.f; by permno rdq; run; proc sort data=etr.e; by permno rdq; run; proc sort data=etr.crspmont; by permno; run; data etr.g; merge etr.f etr.e etr.crspmont; by permno; run; After the step above works, I need to calculate the 3 highlighted items from the models below.  Then, I will need to merge these 3 calculations into the etr.combined dataset by permno. Model 1 = Analyst’s Forecast Errors = β 1 Dummy ETR + β 2 SIZE + β 3 COV + β 4 EVOL + β 5 RETVOL + β 6 ROA + β 7 EP + β 8 EPxD EP + β 9 BM + β 10 SGR + β 11 Change in PPE + β 12 RET12 + β 13 ACC Analyst’s Forecast Errors = Earnings/price (E/P) ratio less the median analysts’ E/P forecast made 8 months before fiscal year-end, divided by the closing share price at the prior year-end, which is formalized below: Analyst’s Forecast Errors = ((NI/CSHO) – MEDEST)/PRCC_F 1 year before FYEAR If MEASURE =”EPS” If FISCALP = “ANN” If month(datadate) = 12 and month (statpers) ne 4 then delete; If month(datadate) = 11 and month (statpers) ne 3 then delete; If month(datadate) = 10 and month (statpers) ne 2 then delete; If month(datadate) = 9 and month (statpers) ne 1 then delete; If month(datadate) = 8 and month (statpers) ne 12 then delete; If month(datadate) = 7 and month (statpers) ne 11 then delete; If month(datadate) = 6 and month (statpers) ne 10 then delete; If month(datadate) = 5 and month (statpers) ne 9 then delete; If month(datadate) = 4 and month (statpers) ne 8 then delete; If month(datadate) = 3 and month (statpers) ne 7 then delete; If month(datadate) = 2 and month (statpers) ne 6 then delete; If month(datadate) = 1 and month (statpers) ne 5 then delete; Dummy ETR = Conditions that need to be met (FISCALP=”ANN”): Actual EPS (AEPS ≥ Forecasted EPS (FEPS) at 4 th quarter If ETR (3 rd quarter) is used (rather than ETR (4 th quarter)), then AEPS < FEPS (Pretax Income at 3 rd Quarter * (1 – ETR (3 rd Quarter)) * “Adjustment/Split Factor”)/CSHO when FQTR=3 is short of 3 rd Quarter Analyst EBS Dummy ETR =    1 if A, B, and C are met                                 0 otherwise (more formalized statements as follows): If ((PI * (1 – ETR (3 rd Quarter)) * ADJSPF)/CSHO) < MEDEST then INT1 = 1 (MEASURE=”EPS”) If ETR (4 th Quarter) < ETR (3 rd Quarter) then INT1 = 1 If ((PI * (1 – ETR (3 rd Quarter)) * ADJSPF)/CSHO) ≥ MEDEST then INT1 = 0 (MEASURE=”EPS”) If ETR (4 th Quarter) ≥ ETR (3 rd Quarter) then INT1 = 0 If (MEDEST/(1 – ETR (3 rd Quarter)) – ((PIY * (1 – ETR (3 rd Quarter)) * ADJSPF)/CSHO) is between $0.01 and $0.05 when FQTR=3 then INT1 = 1 (MEASURE=”EPS”) If (MEDEST/(1 – ETR (3 rd Quarter)) – ((PIY * (1 – ETR (3 rd Quarter)) * ADJSPF)/CSHO) is NOT between $0.01 and $0.05 when FQTR=3 then INT1 = 0 (MEASURE=”EPS”) INT1 = 1 (meaning that the conditions above when INT1=1 are met) If ETR (3 rd Quarter) then AEPS < FEPS FISCALP=”ANN” Legend: AEPS = NI/CSHO FEPS = MEDEST If MEASURE=”EPS” and ETR (3 rd Quarter) = TXTY/(NIY + TXTY)                 If FQTR = 3 ETR (4 th Quarter) = TXTY/(NIY + TXTY)                 If FQTR = 4 SIZE = Log of MKVALT 1 year before FYEAR COV = Log of NUMEST             If FPI = 9 EVOL = Standard deviation of (NI/(Average of AT 1 year before FYEAR and AT 2 years before FYEAR) for 5 years in a row For example, if we are in 2010, we need standard deviation of: 2009 NI/(average of 2007 and 2008 AT) 2008 NI/(average of 2006 and 2007 AT) 2007 NI/(average of 2005 and 2006 AT) 2006 NI/(average of 2004 and 2005 AT) 2005 NI/(average of 2003 and 2004 AT) RETVOL = Standard deviation of RET (from etr.g) during the 12-month period before RDQ                 If FPI=9 and FQTR=4 (which should already be sorted) ROA = NI/(Average of AT 1 year before FYEAR and AT 2 years before FYEAR) EP = (NI/PRCC_F) 1 year before FYEAR EP x D EP = (NI/PRCC_F 1 year before FYEAR) times a dummy variable with one if EP is negative and 0 otherwise BM = (AT – LT)/MKVALT SGR = (SALE 1 year before FYEAR – SALE 2 years before FYEAR)/SALE 2 years before FYEAR Change in PPE = (PPENT – PPENT 1 year before FYEAR)/ (Average of AT Prior Year and AT 2 years ago)             RET12 = RET (from etr.g) during the 12-month period 9 months before FYEAR                 If FPI=9 ACC = (NI 1 year before FYEAR – OANCF 1 year before FYEAR)/AT 1 year before FYEAR   Model 2: CAR = β 1 Dummy ETR + β 2 SIZE + β 3 RETVOL + β 4 BTM CAR = RET – SPRTRN (from etr.c file) cumulatively over 3 days as follows: (1 day before RDQ + RDQ  + 1 day after RDQ) If FQTR = 4 (which it already should be) Dummy ETR = Conditions that need to be met (FISCALP=”ANN”): Actual EPS (AEPS ≥ Forecasted EPS (FEPS) at 4 th quarter If ETR (3 rd quarter) is used (rather than ETR (4 th quarter)), then AEPS < FEPS (Pretax Income at 3 rd Quarter * (1 – ETR (3 rd Quarter)) * “Adjustment/Split Factor”)/CSHO when FQTR=3 is short of 3 rd Quarter Analyst EBS Dummy ETR =    1 if A, B, and C are met                                 0 otherwise (more formalized statements as follows): If ((PI * (1 – ETR (3 rd Quarter)) * ADJSPF)/CSHO) < MEDEST then INT1 = 1 (MEASURE=”EPS”) If ETR (4 th Quarter) < ETR (3 rd Quarter) then INT1 = 1 If ((PI * (1 – ETR (3 rd Quarter)) * ADJSPF)/CSHO) ≥ MEDEST then INT1 = 0 (MEASURE=”EPS”) If ETR (4 th Quarter) ≥ ETR (3 rd Quarter) then INT1 = 0 If (MEDEST/(1 – ETR (3 rd Quarter)) – ((PIY * (1 – ETR (3 rd Quarter)) * ADJSPF)/CSHO) is between $0.01 and $0.05 when FQTR=3 then INT1 = 1 (MEASURE=”EPS”) If (MEDEST/(1 – ETR (3 rd Quarter)) – ((PIY * (1 – ETR (3 rd Quarter)) * ADJSPF)/CSHO) is NOT between $0.01 and $0.05 then INT1 = 0 (MEASURE=”EPS”) INT1 = 1 If ETR (3 rd Quarter) then AEPS < FEPS Legend: AEPS = NI/CSHO FEPS = MEDEST If MEASURE=”EPS” and ETR (3 rd Quarter) = TXTY/(NIY + TXTY)                 If FQTR = 3 ETR (4 th Quarter) = TXTY/(NIY + TXTY)                 If FQTR = 4 SIZE = Log of MKVALTQ             If FQTR = 3 RETVOL = Standard deviation of 100 trading-day return (RET from etr.g) prior to 2 days before RDQ If FPI = 9 and FQTR = 4 (which should already be sorted) BTM = (ATQ – LTQ)/MKVALTQ   If FQTR = 3 After using the following code (which I used and it worked), I need to calculate cumulative abnormal return (CAR) as described above.  Then, I will need to merge this calculation into the etr.combined dataset by permo. data etr.a; set etr.combined; input permno fqtr rdq date9.; cards; 10001 2 05jul2014 10001 4 08nov2015 10002 4 09oct2015 10003 1 002jan2015 10004 4 01Dec2015 ; run; data etr.b; set etr.a(where=(fqtr=4)); orig=rdq; rdq=orig-1; output; rdq=orig+1; output; rdq=orig; output; drop orig; run; proc sort data=etr.b; by permno rdq; run; proc sort data=etr.a; by permno rdq; run; proc sort data=etr.crdarevo; by permno; run; data etr.c; merge etr.b etr.a etr.crdarevo; by permno; run; 2 things to point out about the 2 steps above: 1. In the separate SAS files I am using that you will see in the codes (etr.crspmont and etr.crdarevo), RET is the same name for monthly stock returns (in etr.crspmont) and daily stock returns (in etr.crdarevo).  1 of these will have to be renamed before merging into the etr.combined dataset because they are obviously not the same thing. 2. Maybe 2 separate merges is better (meaning creating a copy of etr.combined) because some observations will apply to the 1st calculation (analyst's forecast errors) and some observations will apply to the 2nd calculation (CAR).  These 2 calculations do not need to have the same number of observations. Please help me with coding for the 2 regressions in the large section above.  As you see, detail is provided for the calculations that I need.  I need the SAS coding for them.  The following is 1 line from the etr.combined dataset to make it easier for you to comprehend what I am trying to do.  GVKEY DATADATE FYEAR cusip9 CONSOL INDFMT DATAFMT POPSRC CURCD COSTAT CONM TIC CIK FYR AT LT PPENT NI PI REVT SALE OANCF CSHO MKVALT PRCC_C PRCC_F FYEARQ FQTR DATAFQTR DATACQTR CURCDQ RDQ ATQ LTQ NIQ TXTQ CSHPRY EPSPIY EPSPXY NIY PIY TXTY MKVALTQ crsp_cusip PERMNO DATE cusip6 TICKER MEASURE STATPERS FPI OFTIC cusip8 CNAME FISCALP NUMEST MEDEST MEANEST ACTUAL ANNDATS_ACT ADJSPF USFIRM 021542 20031231 2003 000360206 C INDL STD D USD A AAONINC AAON 0000824142 12 102.0850 34.6570 37.4500 14.2270 21.8530 148.8450 148.8450 16.4690 12.5200 243.0132 19.4100 19.4100 2003 4 2003Q4 2003Q4 USD 20040211 102.0850 34.6570 3.7400 1.2600 12.6850 1.1200 1.1200 14.2270 21.8530 7.6260 0.0000 00036020 76868 20141231 000360 AAON EPS 20040115 1 AAON 00036020 AAON ANN 2 0.21 0.21 0.2114 20040211 3.374998 1 Etr.c has the same categories (although most are blank entries in etr.c) as etr.combined.  Etr.c also has RET and SPRTRN, which I need for the CAR calculation. The most important variables in the 2 regressions are the dependent variables (of course) and the Dummy ETR independent variable in each regression (which is the same in both regressions).  For some reason, if you cannot figure out code for some of the other variables in either regression, it's fine.  Please start with the code for the 2 dependent variables and the Dummy ETR independent variable. I will call on Monday to discuss.  I'm sure this will take a good amount of time, by your help is greatly appreciated.  I am certainly inexperienced in SAS.  I hope I can do some training this summer.  In the meantime, let's try to get these 2 regressions done first. Thanks so much! Jadallah ... View more

How do I obtained fitted values for this data? ... View more