184—Appendix A. Object, View and Procedure Reference
Sym Procs
displayname......... |
set display name (p. 276). |
fill ....................... |
fill the elements of the matrix (p. 293). |
read ..................... |
import data from disk (p. 414). |
setformat ............. |
set the display format for the sym spreadsheet (p. 456). |
setindent.............. |
set the indentation for the sym spreadsheet (p. 462). |
setjust.................. |
set the justification for the sym spreadsheet (p. 463). |
setwidth............... |
set the column width in the sym spreadsheet (p. 468). |
write.................... |
export data to disk (p. 545). |
Sym Data Members
(i,j)...................... |
(i,j)-th element of the matrix. Simply append “(i,j)” to the matrix |
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name (without a “.”). |
Sym Examples
The declaration:
sym results(10)
results=3
creates the 10 × 10 matrix RESULTS and initializes each value to be 3. The following assignment statements also create and initialize sym objects:
sym copymat=results
sym covmat1=eq1.@coefcov
sym(3,3) count
count.fill 1,2,3,4,5,6,7,8,9,10
Graphs, covariances, and statistics may be generated for the columns of the matrix:
copymat.line
copymat.cov
copymat.stats
You can use explicit indices to refer to matrix elements:
scalar diagsum=cov1(1,1)+cov1(2,2)+cov(3,3)
System
System of equations for estimation.
System Declaration
system ................. |
declare system object (p. 497). |
System—185
Declare a system object by entering the keyword system, followed by a name:
system mysys
To fill a system, open the system and edit the specification view, or use append. Note that systems are not used for simulation. See “Model” (p. 170).
System Methods
3sls....................... |
three-stage least squares (p. 196). |
fiml ...................... |
full information maximum likelihood (p. 296). |
gmm..................... |
generalized method of moments (p. 310). |
ls.......................... |
ordinary least squares (p. 345). |
sur ....................... |
seemingly unrelated regression (p. 493). |
tsls ....................... |
two-stage least squares (p. 515). |
wls ....................... |
weighted least squares (p. 543). |
wtsls..................... |
weighted two-stage least squares (p. 547). |
System Views
cellipse ................. |
Confidence ellipses for coefficient restrictions (p. 236). |
coefcov ................. |
coefficient covariance matrix (p. 244). |
derivs ................... |
derivatives of the system equations (p. 273). |
endog ................... |
table or graph of endogenous variables (p. 285). |
grads .................... |
examine the gradients of the objective function (p. 315). |
label ..................... |
label information for the system object (p. 330). |
output .................. |
table of estimation results (p. 380). |
residcor ................ |
residual correlation matrix (p. 421). |
residcov ................ |
residual covariance matrix (p. 421). |
resids.................... |
residual graphs (p. 422). |
results .................. |
table of estimation results (p. 423). |
spec...................... |
text representation of system specification (p. 479). |
wald ..................... |
Wald coefficient restriction test (p. 530). |
System Procs
append ................. |
add a line of text to the system specification (p. 205). |
displayname ......... |
set display name (p. 276). |
makeendog ........... |
make group of endogenous series (p. 351). |
makemodel........... |
create a model from the estimated system (p. 358). |
makeresids ........... |
make series containing residuals from system (p. 359). |
updatecoefs........... |
update coefficient vector(s) from system (p. 521). |
186—Appendix A. Object, View and Procedure Reference
System Data Members
Scalar Values (individual equation data)
@coefcov(i, j) ..... |
covariance of coefficients i and j. |
@coefs(i)............. |
coefficient i. |
@dw(k)............... |
Durbin-Watson statistic for equation k. |
@eqncoef(k)........ |
number of estimated coefficients in equation k. |
@eqregobs(k) ...... |
number of observations in equation k. |
@meandep(k)...... |
mean of the dependent variable in equation k. |
@ncoef(k) ........... |
total number of estimated coefficients in equation k. |
@r2(k) ................ |
R-squared statistic for equation k. |
@rbar2(k) ........... |
adjusted R-squared statistic for equation k. |
@sddep(k)........... |
standard deviation of dependent variable in equation k. |
@se(k) ................ |
standard error of the regression in equation k. |
@ssr(k) ............... |
sum of squared residuals in equation k. |
@stderrs(i) .......... |
standard error for coefficient i. |
@tstats(i)............. |
t-statistic for coefficient i. |
c(i) ...................... |
i-th element of default coefficient vector for system (if applicable). |
Scalar Values (system level data) |
|
@aic.................... |
Akaike information criterion for the system (if applicable). |
@detresid ............ |
determinant of the residual covariance matrix. |
@hq .................... |
Hannan-Quinn information criterion for the system (if applicable). |
@jstat.................. |
J-statistic — value of the GMM objective function (for GMM esti- |
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mation). |
@logl .................. |
value of the log likelihood function for the system (if applicable). |
@ncoefs............... |
total number of estimated coefficients in system. |
@neqn................. |
number of equations. |
@regobs .............. |
number of observations in the sample range used for estimation |
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(“@regobs” will differ from “@eqregobs” if the unbalanced sam- |
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ple is non-overlapping). |
@schwarz............ |
Schwarz information criterion for the system (if applicable). |
@totalobs ............ |
sum of “@eqregobs” from each equation. |
Vectors and Matrices
@coefcov ............. |
covariance matrix for coefficients of equation. |
@coefs................. |
coefficient vector. |
@stderrs .............. |
vector of standard errors for coefficients. |
Table—187
@tstats ................. |
vector of t-statistic values for coefficients. |
System Examples
To estimate a system using GMM and to create residual series for the estimated system:
sys1.gmm(i,m=7,c=.01,b=v)
sys1.makeresids consres incres saveres
To test coefficients using a Wald test:
sys1.wald c(1)=c(4)
To save the coefficient covariance matrix:
sym covs=sys1.@coefcov
Table
Table object. Formatted two-dimensional table for output display.
Table Declaration
freeze |
...................freeze tabular view of object (p. 303). |
table ..................... |
create table object (p. 482). |
To declare a table object, use the keyword table, followed by an optional row and column dimension, and then the object name:
table onelement
table(10,5) outtable
If no dimension is provided, the table will contain a single element.
Alternatively, you may declare a table using an assignment statement. The new table will be sized and initialized, accordingly:
table newtable=outtable
Lastly, you may use the freeze command to create tables from tabular views of other objects:
freeze(newtab) ser1.freq
Table Views
label ..................... |
label information for the table object (p. 330). |
sheet .................... |
view the table (p. 469). |
Table Procs |
|
comment .............. |
adds or removes a comment in a table cell (p. 247). |