Gerard Pauline and Walter Morris Pace University

Transcription

1 USING SAS/ETS SOFTWARE FOR INSTRUCTION AND RESEARCH AT PACE UNIVERSITY Gerard Pauline and Walter Morris Pace University This paper will be divided into two sections. The first section will describe how data is extracted from the Citibase and Compustat II databases here at Pace University. Section two will explain how students use various SAS/ETS procedures to analyze the extracted data. ' SECTION 1 Our original intention was to find a way to efficiently access, and more importantly, analyze data from the Compustat II database. The only means of doing this, before the SAS/ETS product was acquired, was to go through a set of FORTRAN programs or a product known as TROLL. Neither method proved very satisfying for our user population, particularly those interested in doing research. The 1982 SAS/ETS Guide contained an example of a macro program that accessed one ofthe Compustat files. This was used as a basis to develop individual macros for the other Compustat files (the files have different record layouts). The ETS product also contained a procedure to access data from Citibase, known as PROC CITIBASE. Although easy to use, it did not provide facilities to specify an exact periodicity (i.e to 8603) or to compact data (transform a time series from one format to another), as did the TROLL product. The time series transformation could easily be accomplished in the DATA step, but was confusing for inexperienced users. We decided to try to develop a program that would allow access to any of the 6 Compustat files, as well as to Citibase. This program would also have to incorporate exact periodicity specification and data compacting for Citibase data. Above all, it had to be easy and straightforward to use. The result of this is a macro program named ACCESS, which accomplishes all of the specified criteria. Data extraction is controlled by supplying control parameters to the macro. There are a total of 11 macro control parameters, of which 4 are required regardless of which database is being accessed, and 6 of the 11 are common to both databases. The following is a list of the macro's control parameters: 116

2 DB specifies the database, eith r CITIBASE or COMPUSTAT TYPE designates the data type for Citibase (Q-quarterly, M-monthly, A-annual) or the Compustat file (INA-industrial annual, INO industrial quarterly, RSC-research, OTC-over the counter, etc.) OUTPUT dataset to hold the extracted data SELECT specifies the variables for extraction BEGYR the date/year to start data selection ENDYR the date/year to end data selection SELCRIT specifies a condition on which data will be selected PRT YES/NO toggle, prints the extracted data COMPACT YES/NO toggle, specifies if the data is to be compacted CM specifies the method of data compacting: monthly to annual format by summation, monthly to quarterly format by averaging, quarterly to annual format by summation, quarterly to annual format by selecting the value of the 4th quarter COMDAT A specifies the dataset to hold the compacted data When extracting data for a specific period of time, the user may enter that period as a range by using both the BEGYR and ENDYR parameters together. As a start date to begin data selection only BEGYR would be used. To select data up to a certain point only ENDYR would be used. Not specifying a time period results in all available data being selected. To keep the process of accessing the different databases 'transparent' to the user, as many of the control parameters as possible are made common to accessing both databases. Regardless of which database is being accessed, the DB, TYPE, OUTPUT and SELECT parameters must be specified. The parameters PRT, COMPACT, CM and COMDATA are used only when accessing Citibase and the SELCRIT parameter is used only when accessing Compustat. The macro checks to see if the required control parameters have been supplied; if they haven't been, a message is written to the SASLOG. 117

3 The macro utilizes 'KEYWORD' parameters that have the form KEYWORD=XXX. This allows the various parameters to appear in any order. The parameters may appear on one line or across several lines and are separated by commas; spacing is unimportant. The macro may be invoked as many times as necessary in one program. The macro resides in a file called SASMAC, which also contains other macros. The macro is made available to the user's program by using an Include statement to call the SASMAC file into the user's program, as opposed to using the Autocall facility. This allows for easier maintenance and access if job submission is through CMSBATCH. To increase execution speed and conserve disk space (the 3 files take up 12 megabytes), the Citibase data has been converted to SAS data format. The data is stored in 3 files; monthly, quarterly and annual data. The conversion to SAS data format has the added advantage of storing the variable labels with the data. The files are updated by a maintenance program that is run on the new tape. The program uses PROC CITIBASE to create the individualdatasets and the UPDATE facility to update the permanent disk files with the new data. A test program validates the updates. The six Compustat files currently reside in their original format (real binary), although in the future they may be converted to SAS data format. The reason for not yet doing so is that SAS format and real binary format are similar except for the generational information that SAS stores. Unlike Citibase, the Compustat updates completely replace the old files. The following are examples of using the ACCESS macro. The first example accesses Citibase twice, the first time to extract quarterly data, the second time to extract monthly data. The second example is a sample of extracting data from Compustat. Note that the Include statement to call the SASMAC file is not shown (it must be specified before using the macro). %ACCESS(DB=citibase, TYPE=q, OUTPUT =xqd1, BEGYR=75q1, ENDYR=79q4, SELECT =gnp gyd) %ACCESS(DB=citibase, TYPE=m, OUTPUT =Xmd, SELECT =fm1, BEGYR=jan75, ENDYR=dec79, COMPACT =yes, CM=M2, COMDA TA=xqd2) DATA COMBO; MERGE XQD1 XOD2; BY DATE; PROC REG DATA=COMBO; MODEL FM1 =GNP GYD; 118

4 %ACCESS(DB=compustat, TYPE=inq, OUTPUT =amxdata, SELCRIT=CNUM="025816", SELECT=CONAME YEAR V22-V26 V14 V19) PROC PRINT DATA=AMXDATA; In the Citibase example, the first time the macro is invoked, data for the 1st quarter 1975 through 4th quarter 1979 is selected for the variables GNP and GYD. The second invocation selects monthly data for the period January 1975 through December 1979 for the variable FM1. Since FM1 is only available in monthly format, it is compacted to quarterly format for compatibility with the variables GNP and GYD. The two datasets, XQD1 (GNP,GYD) and XOD2 (FM1) are then merged. In the second example, the Compustat Industrial Quarterly (TYPE=INQ) is accessed for data pertaining to American Express (CNUM="025816") for the 9 specified variables. In the near future, we hope to extend the macro to access our other large databases: International Financial Statistics (IFS), Panel Survey of Income Dynamics (PSI D) and the University of Chicago's Center for Research in Security Prices (CRSP) files. Unfortunately, space constraints will not permit a discussion of the coding techniques used in the Access macro or a listing of the code. For further information, interested parties should contact the Department of Academic Computing. SECTION 2 \ It goes without saying that economics is generally thought of as a difficult and sometimes a rather "dismal" science. Indeed, most students have a negative recollection of their first, which in all likelihood, turns out to be their last economics course taken in college. What have teaching economists done to rectify this situation; that is, have teachers been able to breathe some new life into the dismal science? From a purely pedagogical perspective, even though no new theories have burst upon the scene, the profession has made major advances in promoting existing ideas through the extensive use of computer methodologies. In the areas of data base management as well as statistical programming, and more importantly, their interactions, have helped move the teaching of economics into a new and exciting environment. In the areas of macroeconomics, microeconomics, managerial economics, labor economics, international economics, and the obvious example of econometrics, the interfacing of large complex data bases with canned statistical programs has introduced a fresh teaching approach into these subjects. 119

5 Here at Pace three major data bases have been linked with the SAS statistical programs providing users with several unique opportunities to relate economic theory with economics reality (i.e., see ACCESS MACRO at the beginning of the paper). CITIBASE is an economic data set that collects approximately 5,000 economic time series from to present. In macroeconomics classes these data together with several SAS procedures have been useful in helping student estimate and graph consumption functions, investment functions, and money demand functions (i.e., PROC REG). At the conclusion of the course, these three functions are linked together in a multiplier-accelerator-liquidity preference setup, the equations are estimated, simulations are computed, and the results are graphed (i.e., PROC SYSNLIN, PROC SIMNLIN). Students are constantly reminded of the interplay of the economic theory with data behind these equations, and are accorded the capability of playing their results off against various alternative policies. For examp!e, the models are structured in such a manner as to answer questions relating to differing approaches to fiscal and monetary policy and how they impact on the relevant simulated variables. In short, students can place themselves in the drivers seat with regards to economic policy making and observe the results of various decisions made in these two areas. Pace has access to the longitudinal data base referred to as the Panel Survey of Income Dynamics (PSID). The PSID is a national probability sample of 7,000 heads of households that includes data gathered from 1968-present. In each survey year approximately 300 variables are collected on each household, some of the information is repeated for all 16 years and some is collected only for a specific year. The data have been extremely useful in econometrics classes as well as classes in labor economics. In the labor classes intense theoretical interest centers around the notion of human capital theory. Since the PSID is linked to the SAS programs students again have a unique opportunity to test theory against practice. Here students typically gather data for one year and test the relationships of earnings with schooling, training, race, sex, etc. (i.e., PROC FREO, PROC MEANS, PROC TABULATE, PROC REG). Because of the nature of longitudinal data, the capability exists to test these relationships over an extended time span to determine if, for example, the returns to a college education have shifted over time (an area of particular importance to college students). In either case, at the conclusion of the course students have a hands on feel for the extremely abstract human capital theory. These are just two examples of how the economics department uses the computer methodologies in a classroom environment. It is clear that the Econometrics courses use a wide variety of statistical programs with these data bases including PROC FACTOR, PROC FORECAST, and PROC ARIMA, in addition to the ones previously described. However, space does not permit us to explore these possibilities. Admittedly, most of the SAS applications discussed here are in the areas of "number crunching", but with our recent acquisition of SAS GRAPH the department looks forward to exploring new horizons in this critical area. 120