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B34S / SAS Quick Start

Version 8 May, 2023

B34S® / SAS® Quick Start Houston H. Stokes

Abstract: This document gives a quick overview on how to load data into B34S and SAS. The objective is to show how the two programs work in a similar fashion and to allow users to get programs up and running quickly. In addition links with RATS and SCA are illustrated. Only the most basic B34S commands are discussed.

Introduction to B34S

The B34S® Data Analysis System is a full featured Econometric Package developed by Houston H. Stokes at UIC (1972-1997) with the help of others. B34S is documented in Specifying and Diagnostically Testing Econometric Models, ed. 2, Quorum 1997, by Houston H. Stokes. B34S has strengths in:

An extensive MATRIX language which allows extensive econometric calculations including nonlinear estimation to be completely coded in a 4th generation language. The MATRIX facility provides nonlinear least squares capability,, maximization and constrained maximization of a nonlinear function and nonlinear programming with a nonlinear objective function and a nonlinear constraints. Real*8, integer and complex*16 data types are supported.

Limited dependent variable problems (logit, probit, multinominal logit, ordered probit).

Pooled Data problems (Error Component Models, Dynamic Panel Data Model).

Time Series (ARIMA, Transfer function, VAR, VARMA, VMA, Kalman Filter, Frequency Decomposition of VAR Model, Spectral Analysis)

Equation specification procedures such as recursive residuals, BLUS residuals, QR factorization, PC regression and Hinich nonlinearity tests.

Nonlinear Model detection, estimation and forecasting (MARS, PISPLINE MVNLTEST)

Structural Equations Estimation (2SLS, LIML, 3SLS, I3SLS, constrained reduced form.)

Markov Analysis.

L1 and MIMIMAX estimation of panel data.

Solution of LP and QP Maximization problems.

B34S has two way links with SCA®, SAS®, SPEAKEASY®, RATS® and can make data loading files for EXCEL, MATLAB, MINITAB, SHAZAM, LIMDEP and many more systems. B34S contains a user extendable help facility. B34S menus under the Display

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Manager on the PC are developed using the B34S MAKEMENU command which makes them user extendable. Libraries of templates are supplied for most tasks.On the PC, B34S contains a data viewer, a data editor, an output and log viewer and a file editor. The user can designate an editor such as KEDIT, EDIT, NOTEPAD or KEDITW to use. B34S versions run on IBM CMS, IBM RS/6000, SUN SOLARUS, DOS and Windows 95/NT. The DOS and Windows 95/NT versions have a graphical front end (Display Manager) and high resolution graphic capability that includes line plots, 2 and 3D histograms, bar plots, contour plots and 3D surface plots. The user has the choice of either supplying the 3D surface in the form of a matrix or letting B34S determine the surface from three vectors. The B34S graphics system and front end is based on routines from the Interactor Subroutine Library. B34S DOS and Windows 95/NT versions are obtainable on-line from

www.uic.edu/~hhstokes/hhstokes/b34s.htm

provided the user has the appropriate b34s.ini authorization file. This short paper introduces the reader to the B34S system and describes how B34S relates to SAS. No advanced B34S commands are discussed although a complete list of B34S commands in provided.

Overview of SAS and B34S

B34S and SAS are complementary systems. Both programs can call each other. The SAS system is extensively documented in a large number of publications. That is both a strength and weakness. Important SAS documentation includes:

Basic System:

SAS® Language and Procedures, Introduction Version 6 ISBN 1-55544-410-5

SAS® Language: Reference, Version 6 ISBN 1-55544-381-8

SAS® Procedures Guide, Version 6 Ed. 3 ISBN 1-55544-378-8

SAS® Language and Procedures Usage 1, Version 6 ISBN 1-55544-371-0

SAS® Language and Procedures Usage 2, Version 6 ISBN 1-55544-445-8

SAS/STAT®

SAS/STAT® User’s Guide, Version 6 Volume 1 4th ed.ISBN 1-55544-376-1

SAS/STAT® User’s Guide, Version 6 Volume 2 4th ed.ISBN 1-55544-376-1

SAS/ETS®

SAS/ETS® User’s Guide, Version 6, Ed. 2, ISBN 1-55544-554-3

SAS/ETS® Software Applications Guide 1, Version 6, ISBN 1-55544-480-6

SAS/ETS® Software Applications Guide 2, Version 6, ISBN 1-55544-549-7

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SAS/GRAPH®

SAS/GRAPH® Software Usage, Version 6, ISBN 1-55544-421-0

SAS/GRAPH® Software Reference, Volume 1, ISBN 1-555444-379-6

SAS/GRAPH® Software Reference, Volume 1, ISBN 1-555444-379-6

Macro Facility

SAS® Guide to Macro Processing, Version 6 ed. 2 ISBN 1-55544-382-6

Serious SAS users should obtain the five manuals under the basic system and the two manuals under STAT as a minimum. The three manuals under ETS are needed for econometrics. Most users will not need the graphics manuals or the SAS Macro Facility manuals. The usage guides are most helpful.

B34S is documented in Specifying and Diagnostically Testing Econometric Models by Houston H. Stokes, Second Edition Quorum Books 1997. Specific help is available on-line using the B34S HELP facility. The B34S help facility on the PC is available under the B34S Display Manger. The help facility is also available with the command line. For example the command:

b34sexec help=manual newpage bottompn makeindex$ b34srun$

will place the complete manual in the b34s.log file, will provide an index and will put in page numbers. This document can be printed. Specific command such as PROBIT can be obtained by:

b34sexec help=probit$ b34srun$

The B34S user has the ability to add to the help files and create other help commands if desired. Table 1 lists the current B34S commands and their functions:

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Table One B34S commands___________________________________________________ Command DescriptionHELP Provide help, generate on-line manual.OPTIONS Set B34S run time options.REGRESSION OLS and GLS estimation. BLUS and RA analysis.LIST Display data in B34S data file.PLOT Plot and graph series in B34S data file.PROBIT Probit analysis on (0-1) dependent variables.TOBIT Tobit analysis on truncated dependent variables.LOGLIN Logit analysis on up to 4 equations at once.ECOMP Error component analysis.AUTOC Autocorrelation and cross-correlation analysis.RR Recursive residual analysis.QR QR factorization & principal comp. approach to OLSDATA Load Data into B34S without SAS/B34S interface.MPROBIT Multinomial probit analysis.MLOGLIN Multinomial logit analysis.SIMEQ 2SLS, LIML, 3SLS, I3SLS, FIML, SUR estimation.TRANSPROB Estimate Markov Probability Model.BJIDEN Box-Jenkins Identification. Spectral Analysis.BJEST Box-Jenkins ARIMA, transfer function estimation.BTIDEN Identification of VAR, VARMA models.BTEST Estimation of VAR, VARMA, VMA models.NONLIN Estimation of user-specified nonlinear models.VARFREQ Spectral decomposition of VAR models.PGMCALL Call SAS, SPEAKEASY, SCA, MATLAB, LIMDEP, RATS, MINITABPOLYSOLV Solution of polynomials.DTASSM Data manipulation utilities.OPTCONTROL Optimal control analysis.CALL Call user procedure.KFILTER Estimate State Space Model.SOURCE B34S FORTRAN source manager.SCAINPUT B34S/SCA Input option.FORECAST Automatic VAR Forecasting Model Development.MARS Multivariate Adaptive Regression Splines.PISPLINE PI Method of Fitting an underlying smooth function.GENMOD Generate Data sets with given covariance structure.HRGRAPHICS High Resolution Graphics.SORT Sort data.SPECTRAL Spectral analysis.MAKEMENU Make user menus to input commands to B34S.DMF Data Management Facility.MVNLTEST Multivariate tests for nonlinearity.CITIBASE Load Citibase data into B34S using RATS.READVBYV Reads data variable by variable.REG General OLS CommandMERGE Merge files.TRANSPOSE Transpose the data matrix.DESCRIBE Describes series. Useful to see missing values.ROBUST Robust (L1 & MINIMAX) estimation.FREQ Frequency and cross tabulations.LPMAX Solution of LP maximization problems.EXPAND Expand weighted dataMATRIX Full 4th generation programming languageSCAIO Read and write SCA® variable by variable data files _______________________________________________________________________

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Basic Program structure

Both the SAS and B34S command language is based on paragraphs. For B34S, each paragraph contains sentences ending with either $ or ;. The first sentence in each paragraph begins with B34SEXEC. The last sentence in the paragraph ends with the sentence B34SRUN$ or B34SEEND$. Keywords can be upper or lower case. As an example we load two series on age and values of cars in B34S and run a regression. A list of the data is also made.

b34sexec data$input age value$datacards$1 19953 8756 69510 3455 5952 1795b34sreturn$b34srun$b34sexec list$ b34srun$b34sexec reg$ model value = age$b34srun$

If the above listed commands are saved as test.b34, the unix command

b34s test

will produce a file test.out containing the results of the commands run and a file test.log which will contain any errors. Note that this b34s command file contains calls to three procedures: DATA, LIST, REG.

The SAS command language is also based on paragraphs. Each paragraph in SAS contains sentences ending with ;. The first sentence in the paragraph begins with the key word DATA or PROC. The last sentence in the paragraph must be RUN;. The same problem as run with b34s above in SAS would be

data test;input age value;cards;1 19953 8756 69510 3455 5952 1795;run;proc means; run;proc print; run;proc reg; model value = age;run;

The B34S DATA and READVBYV commands automatically give the means. This is not true in SAS. In the SAS example we had to make a call to PROC MEANS. In both

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B34S and SAS, data is read by observation. If the data is available only by variable, in B34S data can be read using the READVBYV command. If changes are desired in the data, the B34S DATA command can be used to transform the data. A simple example follows where three series are read with four observations per series:

b34sexec readvbyv noob=4$ input var=x$ input var=y$ input var=z$ datacards$ 1 2 3 4 66.7 55.5 43.6 33.8 88.8 77.7 33.1 99.2 b34sreturn$ b34srun$ /$ Multiply x by y and put in xtimesy b34sexec data set$ build xtimesy$ gen xtimesy=x*y$ b34seend$

This sample has been annotated with the generalized b34s comment which begins with /$ in column 1-2. The generalized comment is useful in “turning off” code. If possible, data should be loaded by observation rather than by variable. The above job illustrates the b34s data paragraph. Note that new variables have to be listed on the BUILD sentence before they can be recognized by the GEN sentence. This design was developed to reduce the possibility of errors where small spelling errors are not noticed and the data is not built correctly. In SAS data is built with the DATA paragraph. As an example consider the SAS commands

data test;input age value;valuesq=value*value;cards;1 19953 8756 69510 3455 5952 1795;run;

Datasets

SAS has the ability of having multiple datasets current at the same time. The below listed code contains two SAS datasets TEST1 and TEST2. Dataset TEST1 contains two series, TEST2 has three series.

data test1;input age value;cards;1 19953 875

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6 69510 3455 5952 1795;run;data test2;set;agesq=age*age$

run;proc means data=test1; run;proc means data=test2; run;

SAS will produce permanent datasets on disk. Assume data on X and Y are in a file SASDATA on a PC. The below listed job will load the DATA and make a SAS dataset jj.sd2 in subdirectory C:\junk

libname test 'c:\junk' ; data test.jj; infile 'c:\junk\sasdata'; input x y; proc means;

This can be read with the SAS job

libname test 'c:\junk'; proc print data=test.jj;

In B34S there is only one dataset in existence at one time. Howeverthe data in the current dataset can be modified. For example

b34sexec data$input age value$datacards$1 19953 8756 69510 3455 5952 1795b34sreturn$b34srun$b34sexec data set$

build agesq$gen agesq=age*age$b34srun$

To read B34S data from a file such as c:\mydir\age.dat and build variables,

b34sexec data file=('c:\mydir\age.dat')$input age value$b34srun$b34sexec data set$build agesq$gen agesq=age*age$

b34srun;

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b34sexec reg$ model value=age agesq$ b34srun$

The permanent form of the b34s dataset is a DMF file. There are two types, formatted and unformatted. Formatted DMF files can be moved across machines (PC to unix for example), although they load slower and are larger in size than an unformatted dmf. The b34s commands to create a formatted dmf file are:

b34sexec options open('testdmf.dmf') disp=unknown unit=60 $b34srun$

b34sexec options clean(60)$ b34srun$/$ Tests Createb34sexec options include('c:\b34slm\gas.b34')$

b34srun$b34sexec dmf inunit=60 outfmt=formatted dmfmember=one$

create disp=rewind heading('Gas data test case') comment('Loads Gas data') $ b34srun$

This job assumes there was a file gas.b34 that loaded the gas data. The dmf file testdmf.dmf can be read with the command:

b34sexec data filef=dmf file('testdmf.dmf')$b34srun$

B34s is very forgiving on command syntax. For example filef(dmf) is the same as filef=dmf. If an INPUT sentence is used while reading from a DMF, a subset of the variables can be loaded. The DMF file can be created on the PC directly from the b34s Display Manager from the menu window.

Moving Datasets Across Different Computers

A SAS command file such as test.sas or a b34s command file such as test.b34 can be edited with an editor or moved freely from one type of computer to another. An unformatted B34S DMF file cannot be moved without conversion to a formatted DMF file. The PC B34S has a sample jobs CONVDMF1 and CONVDMF2 under the TASKS command than can be used to convert unformatted to formatted and formatted to unformatted DMF files. Once the B34S DMF file is converted to a formatted DMF file, it can be moved between machines using FTP as a text file. It can even be looked at with an editor, although the format cannot be changed. The control setup for CONVDMF1 is:

/$ Converts a DMF file from unformatted to formatted/$ User needs only to change b34slet for oldfile and newfile/$ Be sure to use the form "' somename '"/$/$ This feature is useful in labels and or names in a DMF file/$ need to be changed./$/$ Job uses convention that *.dmf is an unformatted DMF file and/$ *.aaa is a formatted DMF file/$%b34slet oldfile = "'oldfile.dmf'"$%b34slet newfile = "'newfile.aaa'"$b34sexec options open(%b34seval(&oldfile)) unit=60 disp=old$ b34srun$

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b34sexec options open(%b34seval(&newfile)) unit=61 disp=unknown$ b34srun$b34sexec options clean(61)$ b34srun$b34sexec dmf unit=60 infmt=unformatted outunit=61 outfmt=formatted$convertuf$ b34srun$/$ Optionally browse new file.b34sexec dmf unit=61 infmt=formatted$ browse listnames$ b34srun$

To run this job the used needs only to set OLDFILE and NEWFILE names. The same thing goes for the conversion the other way.

The CONVDMF2 file is:

/$ Converts a DMF file from formatted to unformatted/$ User needs only to change b34slet for oldfile and newfile/$ Be sure to use the form "' somename '"/$/$ Job uses convention that *.dmf is an unformatted DMF file and/$ *.aaa is a formatted DMF file/$%b34slet oldfile = "'oldfile.aaa'"$%b34slet newfile = "'newfile.dmf'"$b34sexec options open(%b34seval(&oldfile)) unit=60 disp=old$ b34srun$b34sexec options open(%b34seval(&newfile)) form=unformatted unit=61 disp=unknown$ b34srun$b34sexec options clean(61)$ b34srun$b34sexec dmf unit=60 infmt=formatted outunit=61 outfmt=unformatted$ convertfu$ b34srun$/$ Optionally browse new file.b34sexec dmf unit=61 infmt=unformatted$ browse listnames$ b34srun$

SAS has a similar facility except that the export file has to be moved as a binary file. The next four SAS jobs illustrate the SAS export facility on a PC. Control file one loads the GAS data into a SAS file c:\junk\funny.sd2. Not all observations are shown.

libname test 'c:\junk';data test.funny;input TIME GASIN GASOUT CONSTANT ;label GASIN = "Input gas rate in cu. ft / min ";label GASOUT = "Percent CO2 in outlet gas ";cards; 1.000 -0.10900000 53.800 1.00 2.000 0.00000000 53.600 1.00 3.000 0.17800000 53.500 1.00 4.000 0.33900000 53.500 1.00 5.000 0.37300000 53.400 1.00 6.000 0.44100000 53.100 1.00 7.000 0.46100000 52.700 1.00

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8.000 0.34800000 52.400 1.00 . . . . . . . . . . . . . . . . . 295.0 -0.18200000 57.300 1.00 296.0 -0.26200000 57.000 1.00;PROC MEANS;

The next job reads the SAS internal SD2 file c:\junk\funny.sd2 and calculates means.

libname test 'c:\junk';proc means data=test.funny; run;

The next job will take the SAS SD2 file and make an export file c:\junk\export.dat. This file can be moved as a binary file to the new computer.

libname misc 'c:\junk';libname alldata xport 'c:\junk\export.dat';proc copy in=misc out=alldata memtype=data; run;

The final job will rebuild the SD2 SAS file c:\junk\funny.sd2 from the SAS export file c:\junk\export.dat. These examples were tested using PC SAS version 6.12.

* The name here is the pc or unix directory ;libname misc 'c:\junk' ;* The dataset here indicates the export file name ;libname alldata xport 'c:\junk\export.dat';proc copy in=alldata out=misc; run;

Data Building and Loading Examples using B34S

b34sexec data$ input x y z$ datacards$ 1 11 111 2 22 222 3 33 333 4 44 444 5 55 555 b34sreturn$ b34seend$

The above example could be written as

b34sexec data$ input x y z$ datacards$ 1 11 111 2 22 222 3 33 333 4 44 444 5 55 555

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b34sreturn$ b34seend$

since if NOOB is NOT set the default reading option is FILEF=@@.

If NOOB is set explicitly, FILEF=FREE by default. This will readFaster but will not read character data.

b34sexec data noob=5$ input x y z$ datacards$ 1 11 111 2 22 222 3 33 333 4 44 444 5 55 555 b34sreturn$ b34seend$

If NOOB is set and the data is all on one row or broken up, thenFILEF=@@ must be set.

b34sexec data noob=5 filef=@@$ input x y z$ datacards$ 1 11 111 2 22 222 3 33 333 4 44 444 5 55 555 b34sreturn$ b34seend$

It is to be noted that FILEF=@@ is the most flexible reading formatbut it also is the slowest. FILEF=FREE is faster, but is slower thanFILEF=FIXED. FILEF=DP is the fastest.

The next example shows data building and a regression being run.

b34sexec data noob=5$ input x1 y1 z $ build sumy1z $ gen sumy1z = y1+z$ datacards$ 11 22 33 33 44 33 333 3.0 666 22 33 11 11.2 33.4 22.2 b34sreturn$ b34seend$

b34sexec regression$ model x1 = sumy1z$ b34srun$

/$/$ we reload the data and build some more data./$ note that prior step ends with b34srun$, not b34seend$./$ optional labels (max of 40 cols) have been supplied./$b34sexec data set$

build x1ty1 x1py1 xx$

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label x1ty1 = 'x1 * y1 '$ label x1py1 = 'x1 + y1 '$ label xx = '(x1**3)/(sin(x1)+(2.0*cos(x1/y1)))'$ gen x1ty1 = x1*y1$ gen x1py1 = x1+y1$ gen xx = (x1**3)/(sin(x1)+(2.0*cos(x1/y1)))$ b34seend$

/$/$ we list the data/$b34sexec list$ b34srun$

Data loading from a file on PC

Assume X1,...,X6 are on a file MYDATA.DAT. The below listed statementswill load the series and perform a regression after building data. Sincethere is no FILEF= parameter, @@ is assumed. If more than oneobservation is placed on one card, use FILEF=@@.

b34sexec options open('mydata.dat') unit=10 disp=old$ b34seend$b34sexec data unit(10)$

input x1 x2 x3 x4 x4 x6$ build x1sq x1tx2$ gen x1sq =x1*x1$ gen x1tx2 =x1*x2$ b34seend$

b34sexec regression$ model x6=x1 x2 x3 x4 x5 x1sq $ b34seend$

The same job can be coded with the file statement as:

b34sexec data file('mydata.dat')$ input x1 x2 x3 x4 x4 x6$

build x1sq x1tx2$ gen x1sq =x1*x1$ gen x1tx2 =x1*x2$ b34seend$

b34sexec regression$ model x6=x1 x2 x3 x4 x5 x1sq $ b34seend$

Assuming mydata.dmf was a dmffile with multiple members, if memberCRIME was to be loaded:

b34sexec data filef=dmf file(‘c:\mysd\mydata.dmf’) dmfmember(crime)$

input x y z$ b34seend$

If MYDATA.DMF contained 80 series and the user wanted to load all 80series without using an INPUT statement, the correct setup would bejust to omit the IMPUT statement. If the DMF file contained more than98 series and the INPUT statement was omitted, only the first 98 serieswould be read.

The IBEGIN and IEND options on the DATA sentence control whether allobservations from the DMF file are read. Assume that the MYDATA.DMF file

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contains 2000 observations but the user wants to load only fromobservation 23 to observation 1023. The correct commands would be

b34sexec options open('c:\mysd\mydata.dmf') unit(60)$ b34seend$ b34sexec data filef=dmf unit(60) dmfmember(crime) ibegin=23 iend=1023$ input x y z$ b34seend$

B34S MATRIX Command

The power of the MATRIX command is illustrated using a few jobs.

Example # 1 shows OLS Modeling using the the commands REG, ROBUST and the MATRIX Command Language. Note the way that lags are specified.

/$ Illustrates OLS Capability under Matrix Commandb34sexec options ginclude('b34sdata.mac') member(gas); b34srun;b34sexec reg; model gasout=gasin; b34srun;b34sexec robust; model gasout=gasin; b34srun;b34sexec reg; model gasout=gasin{0 to 1} gasout{1}; b34srun;b34sexec matrix;call loaddata;call olsq(gasout gasin:print :diag);call graph(%res :heading 'Residual');call graph(%y %yhat :heading 'Fitted and Actual');call olsq(gasout gasin{0 to 1} gasout{1} :print);call names;call print('Model of ',%yvar);call tabulate(%names,%lag,%coef,%se,%t);call tabulate(gasout,%y,%yhat,%res);call graph(%res,:heading 'Residuals');call graph(%y,%yhat :heading 'Fitted and Actual Values.');

maxi=24;

do i=1,maxi;call olsq(gasout gasin{0 to i} gasout{1 to i}:print);call print(acf(%res,24));enddo;b34srun;

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Example # 2 shows OLS done three ways:

b34sexec options ginclude('gas.b34'); b34srun;/$/$ OLS is done three ways: 1. with reg/$ 2. with matrix/$ 3. with olsq/$b34sexec reg; model gasout=gasin; b34srun;

b34sexec matrix;call loaddata;

x=matrix(norows(gasin),2:);x(,1)=vfam(gasin);call setcol(x,2,1.0);xpx=transpose(x)*x;call names;beta=vfam((1./xpx)*transpose(x)*mfam(gasout));call print(xpx,beta);fgasout=vfam(x*mfam(beta));resid=vfam(gasout)-vfam(fgasout);call names; ss=resid*resid;sigma=ss/dfloat(norows(resid)-2);se=dsqrt(diag(sigma*(1.0/xpx))); t=afam(beta)/afam(se);n=namelist(gasin,const);call print('Gasout = a + b*Gasin','RSS',ss);call tabulate(n,beta,se,t);call tabulate(gasout fgasout resid);call graph(resid:heading 'Residuals from Gas Data');

call olsq(gasout,gasin :print);call tabulate(%y,%yhat,%res);b34srun;

B34S Display Manager System

The B34S Display Manager, available currently only on the PCversions of B34S, allows the user to control and modify the execution ofthe B34S program from a windows type environment. B34S versions 7.xx and beyond running on DOS, Windows 95 and Windows NT use the Interactor Windows and Graphic library. The Display Manager provides a way to select the file to run, view the b34s.log and b34s.out file and make changes to the command file. The B34S has a built in editor or allows the user to call an editor such as KEDIT, KEDITW, EDIT or NOTEPAD. The B34S GRAPHICS and MENU Display Manager commands allow access to a number of menus that provide fill-in blanks to perform a number of simple tasks. The usual way the Display Manager is run is to edit and submit b34s command files. Once the command files are run the log and out files can be viewed, edited or renamed and saved. The Display Manager provides access to libraries of b34s command file code which can be modified and rerun. The MENU and GRAPHICS commands can either be used to enter commands from menus or to build command files which can be saved in the B34S Program Buffer (under the Tasks menu). The PC file c:\b34slm\ratspgm.mac contains a number of B34S/RATS jobs that are useful in building command files than will pass daat to RATS386. The file c:\b34slm\b34stest.mac provides a number of test jobs that illustrate

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most of the B34S commands. The file c:\b34slm\b34sdata.mac contains a large number of useful datasets. Other files are c:\b34slm\berndt.mac which contains a large number of famous datasets and c:\b34slm\greene.mac which contains all the datasets from Econometric Analysis by William Greene Third Edition 1997. All files ending in .MAC are mac files. They can be read with b34s commands such as:

b34sexec options include(‘c:\b34slm\b34sdata.mac’) member(gas)$ b34srun$

B34S mac files have the structure

==name1..........................====name2..........................==

and are designed to be built by users with editors such as kedit.

SAS version 6.12 provides a facility where the user can edit and submit SAS jobs. SAS has no preprogrammed menu facility. Both B34S and SAS have a Macro Capability whereby it is possible to have a programming language to write code that in turn is executed. These advanced features are not discussed here. In SAS the AF facility provides the user the capability of generating menus. In B34S the MAKMENU command provides a similar capability. Most users never have to deal with programming these advanced features in either system. All the menus under the MENU or GRAHICS commands of the Display Manager are actually B34S MAKEMENU jobs. This way the B34S menus are not a part of the b34s program. They are b34s command files that execute and in turn generate more b34s commands. The major advantage of this design is that the user can build and modify b34s menus and “customize” the software without having to change the B34S program itself.

Missing Data

Both B34S and SAS have missing data capability. SAS will read . as a missing value and all SAS procedures will ignore this value. The way that SAS has been programmed has caused some confusion. Assume the followingSAS job

data test1;input age value make;cards;1 1995 03 875 16 695 010 345 05 595 12 1795 0. 1900 18 785 .;run;proc reg;model value = age;

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model value = make;run;proc reg;model value = age;run;

proc reg; model value = make;

run;

If would appear that the user has run the same two regression two times. However, this is not the case. In the first call to proc reg; SAS will drop two observations since if both value and make are not missing, then two observations must be dropped. The second and third call to proc reg will have one more observation since that is the maximum number possible. This “feature” of SAS makes it difficult to compare regressions when there are missing data unless all regression are run at one time or the data file is explicitly built to remove missing data.

B34S supports missing values in a different manner. If on the B34S DATA paragraph the parameter FILEF=@@ is used, then B34S will interpret na NA and . as missing values for non-character data. The B34S missing value code will be assigned to this series for that observation. The MISSING=r parameter on the B34S OPTIONS paragraph allows the user to select missing values. If the B34S OPTIONS option KEEPMISS is in effect (the default) these values are left in the dataset unless explicitly deleted using GEN statements under the DATA paragraph.If DROPMISS is in effect, then missing data which are read will have theobservation automatically dropped. If this is not desired, then the GENstatement

GEN X=DIFMISSING(X)$

can be used to selectively drop observations where X is missing.

An existing dataset containing missing data can be automaticallyadjusted to remove observations containing missing data with thestatements:

B34SEXEC OPTIONS DROPMISS$ B34SRUN$ B34SEXEC DATA SET$ B34SRUN$

An alternative is to set DROPMISS on the DATA sentence. Either way,DROPMISS will be in effect until explicitly changed back to the defaultwith the KEEPMISS command.

If missing data is left in the dataset, then the current release ofB34S allows this data to be passed to the B34S procedures. While thismay be desired with such procedures as HRGRAPHICS to graphically tell the location of missing data, the REGRESSION command will give highly misleading results if missing data are passed. Users should inspect means and maximum and minimum values of series to detect whether missing data is in the dataset.

The FILEF=@@ option on the DATA paragraph is slower than theFILEF=FREE option. Hence setting READMISS or using FILEF=@@ explicitlyis slower. Users should inspect their data closely to make sure thatthe data that is in the sample is what was desired.

If MAXLAG is set on the DATA paragraph and in addition a GEN

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statement such as

GEN D=DIFMISSING(X)$

is found, then the data file will be adjusted BEFORE any MAXLAG takeseffect. In other words, before MAXLAG advances the dataset, DIFMISSINGcompletely removes the observation from the dataset. MAXLAG is intendedto allow the user control over lags. DIFMISSING allows cross sectionusers to adjust the dataset for missing data points. Users must use theLIST paragraph to inspect the data to make sure that file is what isdesired. Under the Display Manager data can be viewed or edited.

The MISSING parameter on the OPTIONS sets the default missing value.The absolute value of any value supplied will be used. It is recommendedthat the default of .1D+32 be used.

The above discussion applies only to non character data. The missingvalue codes . NA and na are kept in the dataset for character datasince NA or na might be data that is valid. If the user wants to removethese values, explicit GEN statements should be used. The below listedsample job illustrates how this could be done.

/$ Here dropmiss will not remove 'missing' character data b34sexec data dropmiss$ input x z$ character x z$ datacards$ 11 a7 2. a8 . a9 na a0 NA a99 b34sreturn$ b34seend$ b34sexec list$ b34seend$ b34sexec data set$ build test$ character test$ gen test ='OK'$ gen if(x.eq.'.' )test='foundit'$ gen if(x.eq.'NA')test='foundit'$ gen if(x.eq.'na')test='foundit'$ b34seend$ b34sexec list$ b34seend$ b34sexec data set$ gen if(test.eq.'foundit')test=difmissing(missing())$ b34seend$ b34sexec list$ b34seend$

The above job illustrates IF statements and character data in B34S. Another alternative would be to directly assign the missing value codeto the character variable and use another data step to remove thisvalue. The disadvantage of this approach is that when listing characterdata where the missing value code is explicitly entered, displayproblems can occur is the LIST command is used.

If the data set read contains missing values coded with a value that isnot the B34S missing value code, then the parameter RMISSING on the DATAparagraph can be used to automatically recode the values. It isimportant that all significant digits be used. The below listed exampleshows how this might be done. The coded missing value of -9999 will be

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automatically replaced with the B34S missing value code.

B34SEXEC DATA RMISSING=-9999$ INPUT X Y$ DATACARDS$ 11 22 33 -9999 44 55 B34SRETURN$ B34SEEND$

Problem. Assume that the user has a dataset that contains somevariables that are missing on some period and some in other periods. Thegoal is to use the data that is available in a regression. The solutionis: - 1. Load the data in B34S with KEEPMISS in effect. - 2. Make a B34S DMF file. - 3. Load from the DMF just the series that are desired.

The above logic gets around the problem with missing data that occurs with the way SAS handles the problem what was discussed earlier. What must be stressed is that there is no really good way to handle the problem. The important thing is to get the desired results of the analysis.

Use of the B34S DMF file to handle missing data is illustrated with the sample job shown next:

/$ Shows how to load data you want if missing values b34sexec data keepmiss$ input x y z$ datacards$ 1 2 . 11 22 . 11 22 33 . 44 55 . 55 66 b34sreturn$ b34srun$ b34sexec list$ b34srun$ b34sexec options open('test.dmf') unit=60 form=(unformatted) disp=unknown $ b34srun$ b34sexec dmf dmfmember(checkpoint) outfmt=unformatted$ create disp=rewind$ b34srun$ /$ Run b34s with part of dataset b34sexec data filef=dmf file('test.dmf') dropmiss$ input x y$ b34srun$ b34sexec list$ b34srun$ /$ Run with the other part b34sexec data filef=dmf file('test.dmf') dropmiss$ input y z$ b34srun$ b34sexec list$ b34srun$

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/$ Run with both parts b34sexec data filef=dmf file('test.dmf') dropmiss$ input x y z$ b34srun$ b34sexec list$ b34srun$

The first DATA step loads all series keeping the missing data. This datais saved in the file 'test.dmf' with the DMF step. The next two DATAsteps only load the data than is needed. The final data step runs where all series are loaded and not missing.

The B34S DMF facility allows the user to selectively load data asneeded.

The B34S DESCRIBE command can be used to determine the extent ofmissing data in a file. The commands

b34sexec describe$ b34seend$

will list means, variances, medians, average deviation, skewness andkurtosis for all series where there are > 2 observations withoutmissing data.

The command

b34sexec describe$ group(x,y,z)$ group(x,y)$ b34seend$

indicates for how many observations x, y, and z are not missingand for how many observations x and y are not missing. This command gives the user some idea of where the data is missing and what overlaps there are in the data. To use this command effectively, the KEEPMISS option has to be in effect on the b34s a loading step.

Lags in SAS and B34S

The B34S REG and ROBUST commands provide a quick way to enter lags and test model dynamic specification. For example the code

b34sexec options include(‘c:\b34slm\gas.b34’)$b34srun$b34sexec reg$model gasout = gasin{0 to 60} gasout{1 to 60}$bispec iturno iauto$b34srun$

estimates a model with 60 lags on gasout and gasin and does Hinich nonlinearity tests. Other than the B34S REG and ROBUST commands, other B34S commands are not designed to automatically exclude data that is missing. If there are missing data in the B34S dataset, the B34S DATA command or the B34S EXPAND command with the appropriate weight variable must be used to remove the data points that are not desired. In SAS in order to run proc reg with lags would require the user to

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build 60 lags of gasout and 60 lags of gasin in the SAS datastep. The best way to proceed in SAS would be to use PROC ARIMA which is harder to setup.

SAS and B34S DATA Commands

The SAS DATA command is much more powerful and comprehensive than the B34S DATA step. Both B34S and SAS share a number of similar features.

If statements in B34S are:

gen if(x .eq. y)kk=1.0$gen if(y .lt. k)then$gen v=1.0$gen q=2.0$gen endif$

In SAS the code would be

if x = y then kk=1.0;if y < K then;v=1.0;q=2.0;endif;

The SAS datastep allows the user to easily subset the data and to build almost any structure. A large number of data building commands are available. Many people think that the data handling capability of SAS is the most valuable feature of the system. The developer of B34S suggests that SAS be used to build and maintain any datasets where there are over 98 variables or where extensive data building is needed. Once the data is built, SAS can be set to call B34S for advanced processing.

SAS and B34S Commands

A number of commands in B34S and SAS work almost if not the same.

In SAS

PROC SORT;BY TEST;RUN;

in B34S is

B34SEXEC SORT;BY TEST;B34SRUN$

Not all SAS/B34S commands work the same. SAS is a massive program built by a major company. B34S is a much smaller program built by one person with the help of others. B34S gets a great deal of its power by allowing links to other software systems such that as far as the B34S user is concerned, the programs are linked. There are a large number of procedures in either program where there is not a close substitute in the other program. For example the B34S MARS, PISPLINE, BTEST, MPROBIT, RR, VARFREQ, MLOGLIN and ROBUST commands do not have close, or in most cases, any substitutes in SAS. On the SAS side, PROC IML,

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PDLREG, CATMOD, FSP and a large number of other commands do not have B34S variants. While the B34S FREQ command will work similar to SAS, the SAS FREQ command is much more comprehensive. Table Two Lists the Command in SAS and a close command in B34S.

Table Two SAS and B34S Commands________________________________________________________________________SAS Command B34S Command________________________________________________________________________SORT SORTFREQ FREQPRINT LISTMEANS DESCRIBE (plus datastep)REG REG, ROBUST, REGRESSIONSTATESPACE KFILTERAUTOREG REGRESSIONSYSLIN SIMEQARIMA BJIDEN, BJESTSPECTRA SPECTRALAF MAKEMENUPLOT PLOTGRAPH HRGRAPHICSIML MATRIX________________________________________________________________________

Advanced Capabilities

B34S provides two way communications with RATS, MATLAB, SCA, SPEAKEASY and other software. In this mode of operation the b34s user can pass vectors of data and commands to the program and return results to the b34s output file. The file ratspgm.mac contains a number of sample setups. The below listed B34S commands show how an ARCH model is estimated in RATS and the results returned to B34S. The code is set to run on unix and windows 98/nt without change.

b34sexec options ginclude('gas.b34')$ b34srun$b34sexec options open('rats.dat') unit(28) disp=unknown$ b34srun$b34sexec options open('rats.in') unit(29) disp=unknown$ b34srun$b34sexec options clean(28)$ b34srun$b34sexec options clean(29)$ b34srun$b34sexec pgmcall$ rats passastspcomments('* ', '* Data passed from b34s(r) system to rats', '* ') $pgmcards$*set seriesn = gasoutcompute iter = 100,isiter=100** arch with ma** see page 5-31 rats manual*smpl(series=seriesn)set v = 1.0set ra = 0.0nonlin b0 b1 b2 mu1 a0 a1

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frml regresid = seriesn - b0 - b1*seriesn{1}-b2*seriesn{2} $ + mu1*ra{1}* note: to add more series on the right place added lines before* + mu1 * ra{1} linefrml archvar = a0+a1*regresid(t-1)**2frml archlogl = (v=archvar(t)),(ra(t)=regresid(t)), $ -.5*(log(v)+ra(t)**2/v)linreg seriesn# constant seriesn{1} seriesn{2}compute b0=%beta(1),b1=%beta(2),b2=%beta(3)compute a0=%seesq, a1=.05, mu1=0.0* maximize(method=simplex,recursive,iterations=iter) archlogl 3 *nlpar(subiterations=isiter)maximize(method=bhhh,recursive,iterations=iter) archlogl 3 *smpl(series=ra)statistics raset rssa = ra(t)*ra(t)statistics rssa smpl(series=rssa) compute sumsqra = %sum(rssa) display 'sum of squares of ra' sumsqra*b34sreturn$b34srun$b34sexec options close(28)$ b34srun$b34sexec options close(29)$ b34srun$b34sexec options dodos('rats386 rats.in,rats.out') dounix('rats rats.in rats.out')$ b34srun$b34sexec options npageout writeout('output from rats',' ',' ') copyfout('rats.out') dodos('erase rats.in','erase rats.out','erase rats.dat') dounix('rm rats.in','rm rats.out','rm rats.dat') $ b34srun$

B34S communicates with SPEAKEASY by either directing passing data or by creating SCA FSAVE files. Since both programs can read and write these files, two way communication is possible. SPEAKEASY links can be easily done with the B34SI/O command in the B34S display manager.

Links to SCA

The next file shows b34s calling sca, using the automatic ARIMA capability in SCA, reloading the residuals and using the b34s matrix command to draw a high resolution graph. This control file is available in the examples.mac file.

b34sexec options ginclude('gas.b34'); b34srun;

b34sexec options open('sca.dat') disp=unknown unit(28)$ b34srun$b34sexec options open('sca.cmd') disp=unknown unit(29)$ b34srun$b34sexec options clean(28)$ b34srun$b34sexec options clean(29)$ b34srun$b34sexec options dodos('erase c:\junk\scadata.fsv'); b34srun;b34sexec pgmcall$ sca scafname=mydata$pgmcards$/$#==myrun

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--- these commands are required to load the b34s data.--- assign file 18. attrib access(read). external 'sca.dat'. call procedure is mydata. file is 18.------ User can place any sca commands after here. A number--- of sample setups can be uncommented--- print age, ecg, chd, cat, wt--- crosstab cat, age.--- crosstab age, ecg, chd, cat. weight is wt.--- print gasin, gasout.--- acf gasin.--- regress variables are gasout gasin--- SCA Expert commands--- iarima gasout. hold residuals(res)--- estim utsmodel. method is exact.--- forecast utsmodel.--- VAR Identification--- ccm gasin, gasout. maxlag is 12.--- stepar gasin, gasout. arfits are 1 to 6. @--- rccm are 1,2. output level(detailed).--- fsave series1. file is 'my.fsv'. dataset is test.------iarima gasout.estim utsmodel. method is exact. hold residuals(testres).print testres.fsave testres. file is 'scadata.fsv'. dataset is test.stop.return/$#==b34sreturn$b34srun$b34sexec options close(28)$ b34srun$b34sexec options close(29)$ b34srun$b34sexec optionsdodos('scaw32 10000 /f:sca.cmd /p:myrun /o:sca.out')$ b34srun$b34sexec options npageout writeout('output from sca',' ',' ') copyfout('sca.out') dodos('erase sca.cmd','erase sca.out','erase sca.dat') $/$/$ Data from SCA pulled back into MATRIX facility for further work/$b34sexec matrix;call getsca('scadata.fsv', :member test);call print(testres);testres=goodrow(testres);call graph(testres);b34srun$

B34S links to LIMDEP, MATLAB, MINITAB, EXCEL etc will not be shown in this document. Sample jobs are supplied in the examples.mac file.

Calling B34S under SAS

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The SAS system has extensive and very powerful data building capability. While at one time there is a maximum of 98 b34s active variables, in SAS there is effectively no limit to the number of variables that can be active at one time. In b34s the DMF file can save up to 9999 series but not all can be loaded at once. This suggests that it is desirable to be able to call b34s under SAS for specialized calculations but use SAS for data building. The SAS macro CB34SM.SAS was built to perform the link. The next job setup illustrates this capability

* Simple job showing a branch from SAS to B34S ;* One control file is built with data in the file;%include 'c:\b34slm\cb34sm.sas';data junk;input x y;cards;11 2233 4455 6699 7777 88;proc means;* ;* Clean files ********************************** ;* ;options noxwait;run;data _null_;command ='erase myjob.b34';call system(command);* ;* End of clean step **************************** ;* ;* Place B34S commands next after %readpgm ;%readpgmcards;b34sexec list$ var x$ b34seend$b34sexec regression$ model y = x$ b34seend$b34sexec rr$ model y=x$ b34seend$b34sexec describe$ b34seend$b34sexec reg$ model y=x$ b34seend$b34sexec options dispmoff$ b34srun$;run; %cb34sm(data=junk, var=x y, u8='myjob.b34', u3='myjob.b34', options=dropmiss)options noxwait;run;

With this setup the user must run the file myjob.b34 which contains b34s controlcommands and data. The next sample shows b34s running under SAS.

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* Simple job showing a branch from SAS to B34S;* One control file is built with data in the file;%include 'c:\b34slm\cb34sm.sas';data junk;input x y;cards;11 2233 4455 6699 7777 88;proc means;* ;* Clean files ********************************** ;* ;options noxwait;run;data _null_;command ='erase myjob.b34';call system(command);* ;* End of clean step **************************** ;* ;* Place B34S commands next after %readpgm ;%readpgmcards;b34sexec list$ var x$ b34seend$b34sexec regression$ model y = x$ b34seend$b34sexec rr$ model y=x$ b34seend$b34sexec describe$ b34seend$b34sexec reg$ model y=x$ b34seend$b34sexec options dispmoff$ b34srun$;run; %cb34sm(data=junk, var=x y, u8="myjob.b34", u3="myjob.b34", options=dropmiss)options noxwait;run;* ;* This step calls b34s and copies files ;* ;data _null_;command ='c:\b34slm\b34sw.exe myjob.b34';call system(command);command ='copy b34s.log myjob.log';call system(command);command ='copy b34s.out myjob.out';call system(command);run;endsas;

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Discussion of the commands in the job.

1. The below listed command loads the SAS macro cb34sm to process the SAS/B34S link in the usual manner.

%include 'c:\b34slm\cb34sm.sas';

2. Before loading the b34s control files the next series of commands erases any jobs of the same name

* ; * Clean files ********************************** ; * ; options noxwait; run; data _null_; command ='erase myjob.b34'; call system(command); * ; * End of clean step **************************** ; * ;

3. Next the user must place all B34S commands after the SAS macro %readpgm. Note the last b34s command

b34sexec options dispmoff$ b34seend$

turns off the display manager under windows 95 /NT

If this command is not used, B34S will process all commands and return to the display manager.

* ; * Place B34S commands next after %readpgm ; %readpgm cards; b34sexec list$ var x$ b34seend$ b34sexec regression$ model y = x$ b34seend$ b34sexec rr$ model y=x$ b34seend$ b34sexec describe$ b34seend$ b34sexec reg$ model y=x$ b34seend$ b34sexec options dispmoff$ b34srun$ ; run; %cb34sm(data=junk, var=x y, u8='myjob.b34', u3='myjob.b34', options=nohead) options noxwait; run;

4. Steps 1-3 have made the b34s command file myjob.b34

If the user wants to execute b34s under SAS, add the following commands to the job.

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* ; * This step calls b34s and copies files ; * ; data _null_; command ='c:\b34slm\b34sw.exe myjob.b34'; call system(command); command ='copy b34s.log myjob.log'; call system(command); command ='copy b34s.out myjob.out'; call system(command); run; endsas;

The next example places b34s commands in one file and data in another. It should be used for large datasets since in this case it is not desirable to have the data in the command file. The control file is myjob.b34

Discussion.

* Simple job showing a branch from SAS to B34S ;* One control file is built with data in the file;* Data and B34S control statements are in 1 file;* The job TSASB342.SAS shows same job with data in different file;%include 'c:\b34slm\cb34sm.sas';data junk;input x y;cards;11 2233 4455 6699 7777 88;proc means;* ;* Clean files ********************************** ;* ;options noxwait;run;data _null_;command ='erase myjob.b34';call system(command);command ='erase mydata';call system(command);* ;* End of clean step **************************** ;* ;* Place B34S commands next after %readpgm ;%readpgmcards;b34sexec list$ var x$ b34seend$b34sexec regression$ model y = x$ b34seend$

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b34sexec rr$ model y=x$ b34seend$b34sexec describe$ b34seend$b34sexec reg$ model y=x$ b34seend$b34sexec options dispmoff$ b34srun$;run; %cb34sm(data=junk, var=x y, u8='mydata', u3='myjob.b34', options=nohead)options noxwait;run;* ;* This step calls b34s and copies files ;* ;data _null_;command ='c:\b34slm\b34sw.exe myjob.b34';call system(command);command ='copy b34s.log myjob.log';call system(command);command ='copy b34s.out myjob.out';call system(command);run;endsas;

The only difference between this job and the example above is thatthe lines

command ='erase mydata';call system(command);

and

%cb34sm(data=junk, var=x y, u8='mydata', u3='myjob.b34',have been changed.

The line 'erase mydata' is needed to prevent the new data from beingappended to the old data.

The lines

%cb34sm(data=junk, var=x y, u8='mydata', u3='myjob.b34',

place the data in mydata and tell b34s to read from mydata.

Be sure and use ' not ".

It is possible to have multiple b34s datasets built in SAS. The next setup uses the control file ft03f001.dat which is the default. This job shows the minimum needed to run the program.

* Shows SAS calling B34S where two steps are passed;* The job TSASB34S.SAS shows a simple job;* The job TSASB341.SAS shows two calls to cb34sm ;* The job TSASB342.SAS shows BY variable processing;

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* The job TSASB343.SAS shows data and control cards in 1 file;* ;* Clean files ********************************** ;* ;options noxwait;run;data _null_;command ='erase ft03f001.dat';call system(command);command ='erase ft08f001';call system(command);* ;* End of clean step **************************** ;* ;%include 'c:\b34slm\cb34sm.sas';data junk;input x y;cards;11 2233 4455 6699 7777 88;proc means;* Place B34S commands next after %readpgm ;%readpgmcards;b34sexec list$ var x$ b34seend$b34sexec regression$ model y = x$ b34seend$b34sexec rr$ model y=x$ b34seend$;run;%cb34sm(data=junk, var=x y, options=nohead)

* pass # 2 ..... data is built;

data new; set junk;xx=x*x;yy=y**3;proc means;%readpgmcards;b34sexec list$ b34seend$b34sexec regression$ model y = x xx yy$ b34seend$b34sexec rr$ model y=x xx yy$ b34seend$;%cb34sm(data=new, var=x y xx yy, options=nohead)run;

Warning. The data cleaning step is needed!!!!!

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The next job shows both data and commands in one file. The default name of ft03f001 is used.

* Simple job showing a branch from SAS to B34S ;* ;* Clean files ********************************** ;* ;options noxwait;run;data _null_;command ='erase ft03f001.*';call system(command);* ;* End of clean step **************************** ;* ;%include 'c:\sasb34s\cb34sm.sas';* Place B34S commands next after %readpgm ;data junk;input x y;cards;11 2233 4455 6699 7777 88;proc means;* Place B34S commands next after %readpgm ;%readpgmcards;b34sexec list$ var x$ b34seend$b34sexec regression$ model y = x$ b34seend$b34sexec rr$ model y=x$ b34seend$;run;%cb34sm(data=junk, u8='ft03f001', u3='ft03f001', var=x y, options=nohead)

In the next sample job BY variable processing is done using two files. This job illustrates the power of the SAS system.

* Shows calling B34S under SAS when BY variable processing is used;* This job uses two files;* ;* Clean files ********************************** ;* ;options noxwait;run;data _null_;command ='erase ft03f001.dat';call system(command);

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command ='erase ft08f001';call system(command);* ;* End of clean step **************************** ;* ;%include 'c:\b34slm\cb34sm.sas';data junk;input x y;cards;1 72 52 41 61 32 63 63 73 41 82 4;proc means;* Place B34S commands next after %readpgm ;%readpgmcards;b34sexec list$ b34seend$;run;%cb34sm(data=junk, var=x y, options=nohead, by=x)

The above examples show how the power of the SAS system can be used to build data which in turn is passed to B34S for processing. The above jobs provide templates that can be modified and used in actual applications. Since B34S can call LIMDEP and RATS, it is possible to make a three-way program branch to these other systems.

B34S Calling SAS

The next sample job shows B34S loading the gas data and calling SAS. SAS output is copied back into the b34s output file.

b34sexec options include('c:\b34slm\gas.b34')$ b34srun$/$ Loads B34S data in sas command file TESTSAS.SAS/$ User can optionally add SAS commands after PGMCARDS$/$ Under Windows 95 / nt need to place a pause after SAS command/$ This allows SAS to finish before b34s starts to look for the/$ files/$B34SEXEC OPTIONS OPEN('TESTSAS.SAS') UNIT(29) DISP=UNKNOWN$ B34SRUN$B34SEXEC OPTIONS CLEAN(29) $ B34SEEND$B34SEXEC PGMCALL IDATA=29 ICNTRL=29$ SAS $PGMCARDS$proc means; run;proc reg;

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model gasout = gasin; run;proc autoreg; model gasout=gasin / method=ml nlag=3; run;B34SRETURN$B34SRUN $B34SEXEC OPTIONS CLOSE(29)$ B34SRUN$B34SEXEC OPTIONS SYSTEM('SAS TESTSAS' 'pause ')$ B34SRUN$B34SEXEC OPTIONS NPAGEOUT NOHEADER WRITEOUT(' ','Output from SAS',' ',' ') WRITELOG(' ','Output from SAS',' ',' ') COPYFOUT('TESTSAS.LST') COPYFLOG('TESTSAS.LOG') SYSTEM('ERASE TESTSAS.SAS','ERASE TESTSAS.LST', 'ERASE TESTSAS.LOG') $ B34SRUN$B34SEXEC OPTIONS HEADER$ B34SRUN$

Summary

The B34S is a full-featured econometric system that works well with other software systems such as SAS, MATLAB, RATS, SCA, SPEAKEASY and LIMDEP. B34S has a number of unique features which make it a powerful complementary program that can be used with these other systems. The B34S control language is powerful and flexible and shares many of the design features of SAS.

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B34S (r) / SAS(r) Quick Start - University of Illinois at...

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