Pool

Pooled time series, cross-section object. Used when working with data with both time series and cross-section structure.

Pool Declaration

pool declare pool object.

To declare a pool object, use the pool keyword, followed by a pool name, and optionally, a list of pool members. Pool members are short text identifiers for the cross section units:

pool mypool

pool g7 _can _fr _ger _ita _jpn _us _uk

Pool Methods

ls estimate linear regression models including cross-section weighted least squares, and fixed and random effects models.

tsls linear two-stage least squares (TSLS) regression models.

Pool Views

cellipse Confidence ellipses for coefficient restrictions.

coefcov coefficient covariance matrix.

coint Johansen’s cointegration test.

describe calculate pool descriptive statistics.

fixedtest test significance of estimates of fixed effects.

label label information for the pool object.

output table of estimation results.

ranhaus Hausman test for correlation between random effects and regressors.

representations text showing equations in the model.

residcor residual correlation matrix.

residcov residual covariance matrix.

resids table or graph of residuals for each pool member.

results table of estimation results.

sheet spreadsheet view of series in pool.

testadd likelihood ratio test for adding variables to pool equation.

testdrop likelihood ratio test for dropping variables from pool equation.

uroot unit root test on a pool series.

uroot2 dependent (second generation panel) unit root tests on a pool series .

wald Wald coefficient restriction test.

Pool Procs

add add cross section members to pool.

clearhist clear the contents of the history attribute.

clearremarks clear the contents of the remarks attribute.

copy creates a copy of the pool.

define define cross section identifiers.

delete delete pool series.

displayname set display name.

drop drop cross section members from pool.

fetch fetch series into workfile using a pool.

genr generate pool series using the “?”.

makegroup create a group of series from a pool.

makemodel creates a model object from the estimated pool.

makeresids make series containing residuals from pool.

makestats make descriptive statistic series.

makesystem creates a system object from the pool for other estimation methods.

olepush push updates to OLE linked objects in open applications.

read import pool data from disk.

setattr set the value of an object attribute.

store store pool series in database/bank files.

updatecoefs update coefficient vector from pool.

write export pool data to disk.

Pool Data Members

String Values

@attr("arg") string containing the value of the arg attribute, where the argument is specified as a quoted string.

@command full command line form of the estimation command. Note this is a combination of @method, @options and @spec.

@crossids space delimited list of the Pool identifiers.

@crossidsest space delimited list of the Pool identifiers used in estimation.

@description string containing the Pool object’s description (if available).

@detailedtype returns a string with the object type: “POOL”.

@displayname returns the Pool’s display name. If the Pool has no display name set, the name is returned.

@idname(i) i-th cross-section identifier.

@idnameest(i) i-th cross-section identifier for estimated equation.

@method command line form of estimation method (“LS”, “TSLS”, etc....).

@name returns the Pool’s name.

@options command line form of pool estimation options.

@remarks string containing the pool object’s remarks (if available).

@smpl description of sample used for estimation.

@spec original Pool estimation specification.

@type returns a string with the object type: “POOL”.

@updatetime returns a string representation of the time and date at which the Pool was last updated.

Scalar Values

@aic Akaike information criterion.

@coefcov(i,j) covariance of coefficients i and j.

@coefs(i) coefficient i.

@dw Durbin-Watson statistic.

@effects(i) estimated fixed or random effect for the i-th cross-section member (only for fixed or random effects).

@f F-statistic.

@logl log likelihood.

@meandep mean of the dependent variable.

@ncoef total number of estimated coefficients.

@ncross total number of cross sectional units.

@ncrossest number of cross sectional units in last estimated pool equation.

@npers number of workfile periods used in estimation of the pool equation.

@r2 R-squared statistic.

@rbar2 adjusted R-squared statistic.

@regobs total number of observations in regression.

@schwarz Schwarz information criterion.

@sddep standard deviation of the dependent variable.

@se standard error of the regression.

@ssr sum of squared residuals.

@stderrs(i) standard error for coefficient i.

@totalobs total number of observations in the pool. For a balanced sample this is “@regobs*@ncrossest”.

@tstats(i) t-statistic value for coefficient i.

c(i) i-th element of default coefficient vector for the pool.

Vectors and Matrices

@coefcov covariance matrix for coefficients of equation.

@coefs coefficient vector.

@effects vector of estimated fixed or random effects (only for fixed or random effects estimation).

@residcov (sym) covariance matrix of the residuals.

@stderrs vector of standard errors for coefficients.

@tstats vector of t-statistic values for coefficients.

Pool Examples

To read data using the pool object:

mypool1.read(b2) data.xls x? y? z?

To delete and store pool series you may enter:

mypool1.delete x? y?

mypool1.store z?

Descriptive statistics may be computed using the command:

mypool1.describe(m) z?

To estimate a pool equation using least squares and to access the t-statistics, enter:

mypool1.ls y? c z? @ w?

vector tstat1 = mypool1.@tstats