help xtdcce2 v. 4.6 - 09. January 2024
SJ18-3: st0536
SJ21-3: st0536_1

Title

xtdcce2 - estimating heterogeneous coefficient models using common correlated effects in a dynamic panel
with a large number of observations over groups and time periods.

Syntax

xtdcce2 depvar [indepvars] [varlist2 = varlist_iv] [if] [in] ,
crosssectional(varlistcr1 [,options1])
[
globalcrosssectional(varlistcr2 [,options1])
clustercrosssectional(varlistcr3 [,options1])
pooled(varlist)
cr_lags(string)
nocrosssectional
ivreg2options(string)
e_ivreg2
ivslow
noisily
lr(varlist)
lr_options(string)
pooledconstant
pooledvce(string)
reportconstant
noconstant
trend
pooledtrend
jackknife
recursive
mgmissing
exponent
xtcse2options(string)
nocd
showindividual
fullsample
fast
fast2
blockdiaguse
nodimcheck
useqr
useinvsym
NOOMITted]

Options for cross-sectional averages

options1 Description

cr_lags(numlist) number of lags of cross-section averages

clustercr(varlist) name of cluster variable(s), only for clustercrosssectional

rcce use regularized CCE, see Juodis (2022) with the ER criterion from Ahn and Horenstein (2013) to select the number of eigenvectors for static panels, see details.

rcce(options) as rcce but with further options options, see Options.

where varlist2 are endogenous variables and varlist_iv the instruments.

Data has to be xtset before using xtdcce2; see tsset.
varlists may contain time-series operators, see tsvarlist, or factor variables, see fvvarlist
and note on collinearity issues.

xtdcce2 requires the moremata package.

For a speed optimized version see xtdcce2fast.

Contents

Description

Options

Econometric and Empirical Model

Econometric Model

Empirical Model

Coefficient of Determination (R2)

Issues with Collinearity

Large panels and speed

Saved Values

Postestimation commands

predict

estat

bootstrap

Examples

References

About

Description

xtdcce2 estimates a heterogeneous coefficient model in a large panel with dependence between cross sectional units.
A panel is large if the number of cross-sectional units (or groups) and the number of time periods are going to infinity.

It fits the following estimation methods:

i) The Mean Group Estimator (MG, Pesaran and Smith 1995).

ii) The Common Correlated Effects Estimator (CCE, Pesaran 2006),

iii) The Dynamic Common Correlated Effects Estimator (DCCE, Chudik and Pesaran 2015), and

For a dynamic model, several methods to estimate long run effects are possible:

a) The Pooled Mean Group Estimator (PMG, Shin et. al 1999) based on an Error Correction Model,

b) The Cross-Sectional Augmented Distributed Lag (CS-DL, Chudik et. al 2016) estimator which directly estimates the long run coefficients
from a dynamic equation, and

c) The Cross-Sectional ARDL (CS-ARDL, Chudik et. al 2016) estimator using an ARDL model.

For a further discussion see Ditzen (2019).
Additionally xtdcce2 tests for cross sectional dependence (see xtcd2) and
estimates the exponent of the cross sectional dependence alpha (see xtcse2).
xtdcce2 also implements the regularized CCE estimator from Juodis (2022) for static panels.
It also supports instrumental variable estimations (see ivreg2).

Options

crosssectional(varlistcr1 [,cr_lags(#) rcce[(criterion(string) scale npc(real)]]) defines the variables which are added as cross sectional averages to the equation.
Variables in crosssectional() may be included in pooled(), exogenous_vars(), endogenous_vars() and lr().
Variables in crosssectional() are partialled out, the coefficients not estimated and reported.

crosssectional(_all) adds all variables as cross sectional averages.
No cross sectional averages are added if crosssectional(_none) is used, which is equivalent to nocrosssectional.
crosssectional() is a required option but can be substituted by nocrosssectional.

If cr(..., cr_lags()) is used, then the global option cr_lags() (see below) is ignored.

rcce[criterion(string) scale nps(real)] invokes regularized cross-section averages from Juodis (2022) for static panels.
The number of common factors in the cross-section averages is estimated and
then the respective number of eigenvectors from the cross-section averages is used. For more details see details.

Detailed Options are:

criterion(er|gr) specifies criterion to identify number of common factors using the ER or GR criterion from Ahn and Horenstein (2013), see xtnumfac.
scale scales cross-section averages, see Juodis (2022).
npc(real) specifies number of eigenvectors without estimating it. Cannot be combined with criterion.

globalcrosssectional(varlistcr1 [,cr_lags(#)]) define global cross-section averages.
global cross-section averages are cross-section averages based on observations which are excluded using if statements.
If cr(..., cr_lags()) is used, then the global option cr_lags() (see below) is ignored.

clusterosssectional(varlistcr1 [,cr_lags(#)] clustercr(varlist)) are clustered or local cross-section averages.
That is, the cross-section averages are the same for each realisation of the variables defined in clustercr().
For example, we have data observations regions of multiple countries, defined by variable country
Now we want to add cross-section averages for each country.
We can define those by using the option clustercr(varlist , clustercr(country)).

If cr(..., cr_lags()) is used, then the global option cr_lags() (see below) is ignored.

pooled(varlist) specifies variables which estimated coefficients are constrained to be equal across all cross sectional units.
Variables may occur in indepvars.
Variables in exogenous_vars(), endogenous_vars() and lr() may be pooled as well.

cr_lags(integers) sets the number of lags of the cross sectional averages.
If not defined but crosssectional() contains a varlist, then only contemporaneous cross sectional averages are added but no lags.
cr_lags(0) is the equivalent.
The number of lags can be different for different variables, where the order is the same as defined in cr().
For example if cr(y x) and only contemporaneous cross-sectional averages of y but 2 lags of x are added,
then cr_lags(0 2).

nocrosssectional suppresses adding any cross sectional averages
Results will be equivalent to the Mean Group estimator.

pooledconstant restricts the constant term to be the same across all cross sectional units.

reportconstant reports the constant term. If not specified the constant is partialled out.

noconstant suppresses the constant term.

mgmissing if it is not possible to estimate individual coefficient for a cross-section
because of missing data or perfect collinearity, individual coefficient is excluded for MG estimation.
Coefficient will still be displayed as zero in e(bi).

pooledvce(type) specifies the variance estimator for pooled regression.
The default is the non-parametric variance estimator from Pesaran (2006).
type can be nw for Newey West heteroscedasticity autocorrelation robust standard errors (Pesaran 2006) or
wpn for fixed T adjusted standard errors from Westerlund et. al (2019).

xtdcce2 supports IV regressions using ivreg2.
The IV specific options are:

ivreg2options(string) passes further options to ivreg2, see ivreg2, options.

e_ivreg2 posts all available results from ivreg2 in e() with prefix ivreg2_, see ivreg2, macros.

noisily displays output of ivreg2.

ivslow: For the calculation of standard errors for pooled coefficients an auxiliary regressions is performed.
In case of an IV regression, xtdcce2 runs a simple IV regression for the auxiliary regressions.
this is faster.
If option is used ivslow, then xtdcce2 calls ivreg2 for the auxiliary regression.
This is advisable as soon as ivreg2 specific options are used.

xtdcce2 is able to estimate long run coefficients.
Three models are supported:
The pooled mean group models (Shin et. al 1999), similar to xtpmg, the CS-DL (see xtdcce2, csdl)
and CS-ARDL method (see xtdcce2, ardl) as developed in Chudik et. al 2016.
No options for the CS-DL model are necessary.

lr(varlist) specifies the variables to be included in the long-run cointegration vector.
The first variable(s) is/are the error-correction speed of adjustment term.
The default is to use the pmg model.
In this case each estimated coefficient is divided by the negative of the long-run cointegration vector (the first variable).
If the option ardl is used, then the long run coefficients are estimated as the sum over the coefficients relating to a variable,
divided by the sum of the coefficients of the dependent variable.

lr_options(string), options for the long run coefficients. Options are:

ardl estimates the CS-ARDL estimator. For further details see xtdcce2, ardl.
nodivide coefficients are not divided by the error correction speed of adjustment vector. Equation (7) is estimated, see xtdcce2, pmg.
xtpmgnames coefficient names in e(b_p_mg) (or e(b_full)) and e(V_p_mg) (or e(V_full)) match the name convention from xtpmg.
nominus does not subtract -1 from the coefficient of the dependent variable.

trend adds a linear unit specific trend. May not be combined with pooledtrend.

pooledtrend adds a linear common trend. May not be combined with trend.

Two methods for small sample time series bias correction are supported:

jackknife applies the jackknife bias correction method. May not be combined with recursive.

recursive applies the recursive mean adjustment method. May not be combined with jackknife.

exponent	uses xtcse2 to estimate the exponent of the cross-sectional
dependence of the residuals. A value above 0.5 indicates cross-sectional dependence,
see xtcse2.

xtcse2options passes options to xtcse2, see xtcse2, options.

nocd suppresses calculation of CD test. For details about the CD test see xtcd2.

showindividual reports unit individual estimates in output.

fullsample uses entire sample available for calculation of cross sectional averages.
Any observations which are lost due to lags will be included in calculating the
cross sectional averages (but are not included in the estimation itself).
The option does not remove any if statements.
This means, that if an if removes certain cross-sectional units from
the estimation sample, xtdcce2 will not use those (as specified by if),
even if fullsample is used.

fast omit calculation of unit specific standard errors.

fast2 use xtdcce2fast instead of xtdcce2.

blockdiaguse uses mata blockdiag
rather than an alternative algorithm.
mata blockdiag is slower, but might produce more stable results.

nodimcheck Does not check for dimension.
Before estimating a model, xtdcce2 automatically checks if the
time dimension within each panel is long enough to run a mean group regression.
Panel units with an insufficient number are automatically dropped.

xtdcce2 checks for collinearity in three different ways.
It checks if matrix of the cross-sectional averages is of full rank.
After partialling out the cross-sectional averages, it checks if the entire model
across all cross-sectional units exhibits multicollinearity.
The final check is on a cross-sectional level.
The outcome of the checks influence which method is used to invert matrices.
If a check fails xtdcce2 posts a warning message.
The default is cholinv and invsym
if a matrix is of rank-deficient.
For a further discussion see collinearity issues.

The following options are available to alter the behaviour of xtdcce2
with respect to matrices of not full rank:

useqr calculates the generalized inverse via QR decomposition. This was the default for rank-deficient matrices for xtdcce2 pre version 1.35.
useinvsym calculates the generalized invers via mata invsym.
noomitted no omitted variable checks on the entire model.

Econometric and Empirical Model

Econometric Model

Assume the following dynamic panel data model with heterogeneous coefficients:

(1) y(i,t) = b0(i) + b1(i)*y(i,t-1) + x(i,t)*b2(i) + x(i,t-1)*b3(i) + u(i,t)
u(i,t) = g(i)*f(t) + e(i,t)

where f(t) is an unobserved common factor loading, g(i) a heterogeneous factor loading, x(i,t) is a (1 x K) vector
and b2(i) and b3(i) the coefficient vectors.
The error e(i,t) is iid and the heterogeneous coefficients b1(i), b2(i) and b3(i) are randomly distributed around a common mean.
It is assumed that x(i,t) is strictly exogenous.
In the case of a static panel model (b1(i) = 0) Pesaran (2006) shows that mean of the coefficients 0, b2 and b3
(for example for b2(mg) = 1/N sum(b2(i)))
can be consistently estimated by adding cross sectional means of the dependent and all independent variables.
The cross sectional means approximate the unobserved factors.
In a dynamic panel data model (b1(i) <> 0) pT lags of the cross sectional means are added to achieve consistency (Chudik and Pesaran 2015).
The mean group estimates for b1, b2 and b3 are consistently estimated as long as N,T and pT go to infinity.
This implies that the number of cross sectional units and time periods is assumed to grow with the same rate.
In an empirical setting this can be interpreted as N/T being constant.
A dataset with one dimension being large in comparison to the other would lead to inconsistent estimates, even if both dimension are large in numbers.
For example a financial dataset on stock markets returns on a monthly basis over 30 years (T=360) of 10,000 firms would not be sufficient.
While individually both dimension can be interpreted as large, they do not grow with the same rate and the ratio would not be constant.
Therefore an estimator relying on fixed T asymptotics and large N would be appropriate.
On the other hand a dataset with lets say N = 30 and T = 34 would qualify as appropriate, if N and T grow with the same rate.

The variance of the mean group coefficient b1(mg) is estimated as:

var(b(mg)) = 1/N sum(i=1,N) (b1(i) - b1(mg))^2

or if the vector pi(mg) = (b0(mg),b1(mg)) as:

var(pi(mg)) = 1/N sum(i=1,N) (pi(i) - pi(mg))(p(i)-pi(mg))'

Empirical Model

The empirical model of equation (1) without the lag of variable x is:

(2) y(i,t) = b0(i) + b1(i)*y(i,t-1) + x(i,t)*b2(i) + sum[d(i)*z(i,s)] + e(i,t),

where z(i,s) is a (1 x K+1) vector including the cross sectional means at time s and the sum is over s=t...t-pT.
xtdcce2 supports several different specifications of equation (2).

xtdcce2 partials out the cross sectional means internally.
For consistency of the cross sectional specific estimates, the matrix z = (z(1,1),...,z(N,T)) has to be of full column rank.
This condition is checked for each cross section.
xtdcce2 will return a warning if z is not full column rank.
It will, however, continue estimating the cross sectional specific coefficients and then calculate the mean group estimates,
see collinearity issues.
The mean group estimates will be consistent.
For further reading see, Chudik, Pesaran (2015, Journal of Econometrics), Assumption 6 and page 398.

xtdcce2 evaluates the rank condition further.
The rank condition can fail if the constant is partialled out and one or more variables are binary.
In this case xtdcce2 restarts, however forces the constant to be calculated.

The following models can be estimated:

i) Mean Group

If no cross sectional averages are added (d(i) = 0), then the estimator is the Mean Group Estimator as proposed by Pesaran and Smith (1995).
The estimated equation is:

(3) y(i,t) = b0(i) + b1(i)*y(i,t-1) + x(i,t)*b2(i) + e(i,t).

Equation (3) can be estimated by using the nocross option of xtdcce2. The model can be either static (b(1) = 0) or dynamic (b(1) <> 0).

See example

ii) Common Correlated Effects

The model in equation (3) does not account for unobserved common factors between units.
To do so, cross sectional averages are added in the fashion of Pesaran (2006):

(4) y(i,t) = b0(i) + x(i,t)*b2(i) + d(i)*z(i,t) + e(i,t).

Equation (4) is the default equation of xtdcce2.
Including the dependent and independent variables in crosssectional() and setting cr_lags(0) leads to the same result.
crosssectional() defines the variables to be included in z(i,t).
Important to notice is, that b1(i) is set to zero.

See example

iii) Dynamic Common Correlated Effects

If a lag of the dependent variable is added, endogeneity occurs and adding solely contemporaneous cross sectional averages is not sufficient any longer to achieve consistency.
Chudik and Pesaran (2015) show that consistency is gained if pT lags of the cross sectional averages are added:

(5) y(i,t) = b0(i) + b1(i)*y(i,t-1) + x(i,t)*b2(i) + sum [d(i)*z(i,s)] + e(i,t).

where s = t,...,t-pT. Equation (5) is estimated if the option cr_lags() contains a positive number.

See example

iv) Pooled Estimators

Equations (3) - (5) can be constrained that the parameters are the same across units. Hence the equations become:

(3-p) y(i,t) = b0 + b1*y(i,t-1) + x(i,t)*b2 + e(i,t),
(4-p) y(i,t) = b0 + x(i,t)*b2 + d(i)*z(i,t) + e(i,t),
(5-p) y(i,t) = b0 + b1*y(i,t-1) + x(i,t)*b2 + sum [d(i)*z(i,s)] + e(i,t).

Variables with pooled (homogenous) coefficients are specified using the pooled(varlist) option.
The constant is pooled by using the option pooledconstant.

In case of a pooled estimation xtdcce2 offers the estimation of three different type of standard errors.
The default is the non-parametric variance estimator from Pesaran (2006), Eq. 67 - 69.
The non-parametric estimator takes the difference between the mean group and individual coefficients into account.
Therefore a mean group regression is performed in the background.
The first alternative estimator is the Newey West type heteroscedasticity and auto correlation robust standard errors from
Pesaran (2006), Eq. 51, 52 and 74.
The length of the window size is defined as round(4*(T/100)^(2/9)) and follows the convention in
for HAC standard errors (see Bai and Ng, 2004).
Another alternative is the fixed-T variance estimator from Westerlund et. al (2019), Eq. 10 and 11.
This estimator heteroscedasticity robust and allows for panels with a fixed time dimension.

See example

v) Instrumental Variables

xtdcce2 supports estimations of instrumental variables by using the ivreg2 package.
Endogenous variables (to be instrumented) are defined in varlist2 and their instruments are defined in varlist_iv.

See example

vi) Cross-Section Augmented Error Correction Models (CS-ECM)

Two version of the CS-ECM are supported.
The Pooled Mean Group model, which is an intermediate between the mean group and a pooled estimation, Shin et. al (1999) differentiate between homogenous long run and heterogeneous short run effects.
Therefore the model includes mean group as well as pooled coefficients.
Equation (1) (without the lag of the explanatory variable x and for a better readability without the cross sectional averages) is transformed into an ARDL model:

(6) y(i,t) = phi(i)*(y(i,t-1) - w0(i) - x(i,t)*w2(i)) + g1(i)*[y(i,t)-y(i,t-1)] + [x(i,t) - x(i,t-1)] * g2(i) + e(i,t),

where phi(i) is the cointegration vector, w(i) captures the long run effects and g1(i) and g2(i) the short run effects.
Shin et. al estimate the long run coefficients by ML and the short run coefficients by OLS.
xtdcce2 estimates a slightly different version by OLS:

(7) y(i,t) = o0(i) + phi(i)*y(i,t-1) + x(i,t)*o2(i) + g1(i)*[y(i,t)-y(i,t-1)] + [x(i,t) - x(i,t-1)] * g2(i) + e(i,t),

where w2(i) = - o2(i) / phi(i) and w0(i) = - o0(i)/phi(i).
Equation (7) is estimated by including the levels of y and x as long run variables
using the lr(varlist) and pooled(varlist) options and
adding the first differences as independent variables.
xtdcce2 estimates equation (7) but automatically calculates estimates for w(i) = (w0(i),...,wk(i)).
The advantage estimating equation (7) by OLS is that it is possible to use IV regressions and add cross sectional averages to account for dependencies between units.
The variance/covariance matrix is calculated using the delta method,
for a further discussion, see Ditzen (2018).

See example

The restriction of homogenous long run relationships can be relaxed.
This is then an unrestricted CS-ECM.
The results are equivalent to an CS-ARDL if only one lag is used.

vii) Cross-Section Augmented Distributed Lag (CS-DL)

Chudik et. al (2016) show that the long run effect of variable x on variable y in equation (1) can be directly estimated.
Therefore they fit the following model, based on equation (1):

(8) y(i,t) = w0(i) + x(i,t) * w2(i) + delta(i) * (x(i,t) - x(i,t-1)) + sum [d(i)*z(i,s)] + e(i,t)

where w2(i) is the long effect and sum [d(i)*z(i,s)] the cross-sectional averages with
an appropriate number of lags.
To account for the lags of the dependent variable, the corresponding number of first differences are added.
If the model is an ARDL(1,1), then only the first difference of the explanatory variable is added.
In the case of an ARDL(1,2) model, the first and the second difference are added.
The advantage of the CS-DL approach is, that no short run coefficients need to be estimated.

A general ARDL(py,px) model is estimated by:

(8) y(i,t) = w0(i) + x(i,t) * w2(i) + sum(l=1,px) delta(i,l) * (x(i,t-l) - x(i,t-l-1)) + sum [d(i)*z(i,s)] + e(i,t)

The mean group coefficients are calculated as the unweighted averages of all cross-sectional specific coefficient estimates.
The variance/covariance matrix is estimated as in the case of a Mean Group Estimation.

See example

viii) Cross-Section Augmented ARDL (CS-ARDL)

As an alternative approach the long run coefficients can be estimated by first estimating the short run coefficients and then the long run coefficients.
For a general ARDL(py,px) model including cross-sectional averages such as:

(9) y(i,t) = b0(i) + sum(l=1,py) b1(i,l) y(i,t-l) + sum(l=0,px) b2(i,l) x(i,t-l) + sum [d(i)*z(i,s)] + e(i,t),

the long run coefficients for the independent variables are calculated as:

(10) w2(i) = sum(l=0,px) b2(i,l) / ( 1 - sum(l=1,py) b1(i,l))

and for the dependent variable as:

(11) w1(i) = 1 - sum(l=1,py) b1(i,l).

This is the CS-ARDL estimator in Chudik et. al (2016).
The variables belonging to w(1,i) need to be enclosed in parenthesis, or tsvarlist need to be used.
For example coding lr(y x L.x) is equivalent to lr(y (x lx)), where
lx is a variable containing the first lag of x (lx = L.x).

The disadvantage of this approach is, that py and px need to be known.
The variance/covariance matrix is calculated using the delta method, see Ditzen (2019).

See example

ix) Regularized CCE

The CCE approach can involve a large number of cross-section averages which is larger than the number of factors and
can lead to a non-trivial bias for the pooled and mean group estimator, see Karabiyik et. al. (2017).
Juodis (2022) propose a solution for linear static panels which uses singular value decomposition to remove redundant singular values in the cross-section averages.
The so-called rCCE method involves the following steps:

1. Calculate cross-sectional averages.

2. Estimate number of common factors using the ER or GR criterion from Ahn and Horenstein (2013).

3. Replace the cross-sectional averages with eigenvectors from the cross-section averages. The eigenvectors are the eigenvectors of the largest eigenvalues and the number is obtained in step 2.

The method requires bootstrapped standard errors, see bootstrapping.

See example

Coefficient of Determination (R2)

xtdcce2 calculates up to three different coefficients of determination (R2).
It calculates the standard un-adjusted R2 and the adjusted R2 as common in the literature.
If all coefficients are either pooled or heterogeneous, xtdcce2 calculates
an adjusted R2 following Holly et. al (2010); Eq. 3.14 and 3.15.
The R2 and adjusted R2 are calculated even if the pooled or mean group adjusted
R2 is calculated.
However the pooled or mean group adjusted R2 is displayed instead of the adjusted R2
if calculated.

In the case of a pure homogenous model, the adjusted R2 is calculated as:

R2(CCEP) = 1 - s(p)^2 / s^2

where s(p)^2 is the error variance estimator from the pooled regressions
and s^2 the overall error variance estimator. They are defined as

s(p)^2 = sum(i=1,N) e(i)'e(i) / [N ( T - k - 2) - k],

s^2 = 1/(N (T -1)) sum(i=1,N) sum(t=1,T) (y(i,t) - ybar(i) )^2.

k is the number of regressors, e(i) is a vector of residuals
and ybar(i) is the cross sectional specific mean of the dependent variable.

For mean group regressions the adjusted R2 is the mean of the cross-sectional
individual R2 weighted by the overall error variance:

R2(CCEMG) = 1 - s(mg)^2 / s^2

s(mg)^2 = 1/N sum(i=1,N) e(i)'e(i) / [T - 2k - 2].

Collinearity Issues

(Multi-)Collinearity in a regression models means that two or more
explanatory variables are linearly dependent.
The individual effect of a collinear explanatory variable on the dependent variable
cannot be differentiated from the effect of another collinear explanatory variable.
This implies it is impossible to estimate the individual coefficient of the collinear explanatory variables.
If the explanatory variables are stacked into matrix X, one or more variables (columns) in x
are collinear, then X'X is rank deficient.
Therefore it cannot be inverted and the OLS estimate of beta = inverse(X'X)X'Y does not exist.

In a model in which cross-sectional dependence in which dependence is approximated
by cross-sectional averages, collinearity can easily occur.
The empirical model (2) can exhibit collinearity in four ways:

1. In the cross-sectional averages (z(i,s)) stacked in Z are collinear.
2. The cross-sectional averages and the explanatory variables are collinear.
3. In the global set of model of explanatory variables (the constant, y(i,t-1), x(i,t), x(i,t-1) stacked in X) are collinear for all i.
4. In a cross-sectional unit specific model of explanatory variables (the constant, y(i,t-1), x(i,t), x(i,t-1) stacked in X(i)) are collinear for some i.

xtdcce2 checks all types of collinearity and according to the
prevalent type decides how to continue and invert (X'X).
It uses as a default cholinv.
If a matrix is rank deficient it uses invsym, where variables (columns)
are removed from the right.
If X = (X1 X2 X3 X4) and X1 and X4 are collinear, then X4 will be removed.
This is done by invsym, specifying the order in which columns are dropped.

Older versions of xtdcce2 used qrinv for rank deficient matrices.
However results can be unstable and no order of which columns to be dropped can be specified.
The use of qrinv for rank deficient matrices can be enforced
with the option useqr.

xtdcce2 takes the following steps if:

1. Z'Z is not of full rank

Before partialling out xtdcce2 checks of Z'Z is of full rank.
In case Z'Z is rank deficient, then xtdcce2 will return a warning.
Cross-section unit specific estimates are not consistent, however the mean group
estimates are.
See Chudik, Pesaran (2015, Journal of Econometrics), Assumption 6 and page 398.

2. The cross-sectional averages and the explanatory variables are collinear.

In this case regressors from the right are dropped,
this means the cross-sectional averages are dropped.
This case corresponds to the first because the cross-sectional averages
are regressors for the partialling out.

3. X'X is collinear for all i.

xtdcce2 uses _rmcoll to remove any
variables which are collinear on the global level.
A message with the list of omitted variables will be posted.
A local of omitted variables is posted in e(omitted_var)
and the number in e(K_omitted).

4. X(i)'X(i) is collinear for some i.

xtdcce2 automatically drops variables (columns) from the right
for those cross-sectional units with collinear variables (columns).
An error message appears. More details can be obtained using
the option estat ebistructure by showing a matrix with a detailed
break down on a cross-section - variable level.

Results obtained with xtdcce2 can differ from those obtained
with reg or xtmg.
The reasons are that xtdcce2, partialles out the
cross-sectional averages and enforces the use of doubles, both is
not done in xtmg.
In addition it use as a default a different algorithm to invert matrices.

The use of factor variables together with cross-sectional averages
can lead to collineaity issues. For example in a dataset with the time identifier
year adding a time dummy using i.year implicitly creates a dummy for
each year. These variables will be collinear with the cross-sectional averages.
xtdcce2 tries to control for collinearity but might fail when using factor
variables. In general care is required when combining factor variables and
cross-sectional averages.

Large Panels and Speed issues

The common correlated effects estimators are designed for large panels.
This means the number of cross-sectional units and the time periods xtdcce2
is converging to infinity, resulting in a large number of observations.
xtdcce2 has some limitations in its functionality with such large panels,
partly from its design, partly limitations arising from Stata.
While the former slows down the estimation, the latter makes an estimation impossible.

Before an estimation xtdcce2 performs many checks such as collinearity checks,
panel sizes or missing values.
Often those checks rely on loops over either cross-sectional units, time periods or involve
inverting matrices.
With a large number of observations those checks become very time consuming and xtdcce2
becomes slow.
This especially becomes a problem when running simulations or bootstraps.

Stata has limitations on the size of Stata matrices.
xtdcce2 uses Stata matrices to post estimation results, such as a (N_g x N_g)
matrix of covariances for the cross-sectional unit specific coefficients, or the
(N_g x 1) matrix itself.
If the number of cross-sectional units is very large, xtdcce2 cannot post
this matrix and aborts with an error.
The same problems arise when xtdcce2 uses Stata commands such as rmcoll
to check for collinearities.
In addition collinearity checks can be computationally intensive for large panels and slow
an estimation significantly down.

To circumvent the speed and the size issue,
xtdcce2 contains a program called xtdcce2fast.
xtdcc2fast can only estimate a mean group model and does no collinearity checks.
Only the mean group coefficients are saved in e(), individual coefficients
are saved in a mata matrix.

Saved Values

xtdcce2 stores the following in e():

Scalars
e(N) number of observations
e(N_g) number of groups (cross sectional units)
e(T) number of time periods
e(K_mg) number of regressors (excluding variables partialled out)
e(N_partial) number of partialled out variables
e(K_omitted) number of omitted variables (global level)
e(N_pooled) number of pooled (homogenous) coefficients
e(mss) model sum of squares
e(rss) residual sum of squares
e(F) F statistic
e(rmse) root mean squared error
e(df_m) model degrees of freedom
e(df_r) residual degree of freedom
e(r2) R-squared
e(r2_a) R-squared adjusted
e(r2_pmg) pooled or mean group R-squared adjusted
e(cd) CD test statistic
e(cdp) p-value of CD test statistic
e(Tmin) minimum time (only unbalanced panels)
e(Tbar) average time (only unbalanced panels)
e(Tmax) maximum time (only unbalanced panels)

Macros
e(tvar) name of time variable
e(idvar) name of unit variable
e(depvar) name of dependent variable
e(indepvar) name of independent variables
e(omitted) omitted variables
e(lr) variables in long run cointegration vector
e(pooled) pooled (homogenous) coefficients
e(cmd) command line
e(cmdline) command line including options
e(insts) instruments (exogenous) variables (only IV)
e(istd) instrumented (endogenous) variables (only IV)
e(omitted_var) variable list with omitted variables (global)
e(omitted_var_i) matrix of omitted variables on cross-section - variable level
e(version) xtdcce2 version, if xtdcce2, version used
e(cr_lags structure of lags of cross-section averages
e(csa) cross-section averages
e(gcr_lags) structure of lags of global cross-section averages
e(gcsa) global cross-section averages
e(ccr_lags) structure of lags of clustered/local cross-section averages
e(ccsa) clustered/local cross-section averages
e(ccsa_cluster) cluster variable(s) for clustered cross-section averages

Matrices
e(b) coefficient vector
e(V) variance-covariance matrix
e(bi) coefficient vector of individual and pooled coefficients
e(Vi) variance-covariance matrix of individual and pooled coefficients
e(alpha) estimates of the exponent of cross-sectional dependence
e(alpha) estimates of the standard error exponent of cross-sectional dependence

Estimated long run coefficients of the ARDL model are marked with the prefix lr_.

Functions
e(sample) marks estimation sample

Postestimation Commands

predict and estat can be used after xtdcce2.
Bootstrapping standard errors and confidence intervals is possible using estat bootstrap.

predict

The syntax for predict is:

predict [type] newvar [if] [in] [ options ]

Options Description

xb calculate linear prediction on partialled out variables
xb2 calculate linear prediction on non partialled out variables
stdp calculate standard error of the prediction
residuals calculate residuals (e(i,t))
cfresiduals calculate residuals including the common factors (u(i,t))
coefficients a variable with the estimated cross section specific values for all coefficients is created. The name of the new variable is newvar_varname.
se as coefficient, but with standard error instead.
partial create new variables with the partialled out values.
replace replace the variable if existing.

Option xb2 is equivalent to calculate the coefficients and then multiply
the explanatory variables with it, while xb first partialles out
the cross sectional averages and then multiplies the coefficients.

The following Table summarizes the differences with the command line xtdcce2 y x , nocross:

xb xb2
1. predict coeff, coeff 1. predict coeff, coeff
2. predict partial, partial 2. gen xb2 = coeff_x * x
3. gen xb = coeff_x * partial_x

xtdcce2 is able to calculate both residuals from equation (1).
predict newvar , residuals calculates e(i,t).
That is, the residuals of the regression with the cross sectional averages partialled out.
predict newvar , cfresiduals calculates u(i,t) = g(i)*f(g) + e(i,t).
That is, the error including the cross-sectional averages.
Internally, the fitted values are calculated and then subtracted from the dependent variable.
Therefore it is important to note, that if a constant is used, the constant needs to be reported using the xtdcce2 option reportconstant.
Otherwise the u(i,t) includes the constant as well (u(i,t) = b0(i) + g(i)*f(g) + e(i,t)).

The following table summarizes this, where y and x are the original variables,
and ytilde and xtilde are y and x with the common factors partialled out:

y(i,t) = x(i,t) * beta(i) + gamma(i)*f(t) + e(i,t}
ytilde(i,t) = xtilde(i,t)* beta(i) + gamma(i)*f(t) + e(i,t}

Variable predict option
x(i,t)*beta(i) xb2
xtilde(i,t)*beta(i) xb
e(i,t} residuals
gamma(i)*f(t) + e(i,t) cfresiduals

estat

estat can be used display the structure or values of individual coefficients and
to create a box, bar or range plot.

To display the individual coefficients ordered by the cross-sectional units:

estat ebi

estat will save the matrix displayed as
r(blockmatrix).

estat ebistructure displays the structure for each cross-sectional unit.
It indicates if a coefficient was estimated, dropped, set to zero and if the coefficient
is pooled.

The syntax for the box, bar or range plot is:

estat graphtype [varlist] [if] [in] [, combine(string) individual(string) nomg cleargraph]

graphtype Description

box box plot; see graph bar
bar bar plot; see graph box
rcap range plot; see twoway rcap

Options Description

individual(string) passes options for individual graphs (only bar and rcap); see twoway_options
combine(string) passes options for combined graphs; see twoway_options
nomg mean group point estimate and confidence interval are not included in bar and range plot graphs
cleargraph clears the option of the graph command and is best used in combination with the combine() and individual() options
dropzero does not display coefficients with zeros in bar or rcap graphs.

The name of the combined graph is saved in r(graph_name).

Bootstrapping

xtdcce2 can bootstrap confidence intervals and standard errors.
It supports two types of bootstraps: the wild bootstrap and the cross-section bootstrap.
The syntax is:

estat bootstrap , [options]

Options Description

reps(integer) Number of repetitions. Default 100.
seed(string) Set seed, see seed.
wild Use wild bootstrap rather than cross-section bootstrap.
cfresdiduals Use residuals including common factors for wild bootstrap.
percentile Bootstrap confidence intervals.
showindividual show unit specific results.

estat bootstrap implements two types of bootstraps, the wild bootstrap and the cross-section bootstrap.
The cross-section bootstrap is the default.

The cross-section bootstrap draws with replacement from the cross-sectional dimension.
That is it draws randomly cross-sectional units with their entire time series.
It then estimates the model using xtdcce2.
The cross-section bootstrap has been proposed in Westerlund et. al. (2019) or Goncalves and Perron (2014).

The wild bootstrap is a slower from of the wild bootstrap implemented in boottest (Roodman et. al. 2019).
It reweighs the residuals with Rademacher weights from the initial regression, recalculates the dependent variable and then
runs xtdcce2.

The default is to bootstrap standard errors and then use the bootstrapped standard errors to calculate the confidence intervals.
Option percentile directly bootstraps confidence intervals.

Examples

An example dataset of the Penn World Tables 8 is available for download here.
The dataset contains yearly observations from 1960 until 2007 and is already tsset.
To estimate a growth equation the following variables are used:
log_rgdpo (real GDP), log_hc (human capital), log_ck (physical capital) and log_ngd (population growth + break even investments of 5%).

Mean Group Estimation

To estimate equation (3), the option nocrosssectional is used.
In order to obtain estimates for the constant, the option reportconstant is enabled.

xtdcce2 d.log_rgdpo L.log_rgdpo log_hc log_ck log_ngd , nocross reportc.

Omitting reportconstant leads to the same result, however the constant is partialled out:

xtdcce2 d.log_rgdpo L.log_rgdpo log_hc log_ck log_ngd , nocross.

Common Correlated Effect

Common Correlated effects (static) models can be estimated in several ways.
The first possibility is without any cross sectional averages related options:

xtdcce2 d.log_rgdpo log_hc log_ck log_ngd , cr(_all) reportc.

Note, that as this is a static model, the lagged dependent variable does not occur and only contemporaneous cross sectional averages are used.
Defining all independent and dependent variables in crosssectional(varlist) leads to the same result:

xtdcce2 d.log_rgdpo log_hc log_ck log_ngd , reportc cr(d.log_rgdpo log_hc log_ck log_ngd).

The default for the number of cross sectional lags is zero, implying only contemporaneous cross sectional averages are used.
Finally the number of lags can be specified as well using the cr_lags option.

xtdcce2 d.log_rgdpo log_hc log_ck log_ngd , reportc cr(d.log_rgdpo log_hc log_ck log_ngd) cr_lags(0).

All three command lines are equivalent and lead to the same estimation results.

To check if residuals still exhibit cross-sectional dependence, the estimation
of the exponent of cross-sectional dependence can be estimated as well.

xtdcce2 d.log_rgdpo log_hc log_ck log_ngd , reportc cr(d.log_rgdpo log_hc log_ck log_ngd) cr_lags(0) exponent.

Alterntively the exponent can be estimated afterwards using xtcse2.

Dynamic Common Correlated Effect

The lagged dependent variable is added to the model again.
To estimate the mean group coefficients consistently, the number of lags is set to 3:

xtdcce2 d.log_rgdpo L.log_rgdpo log_hc log_ck log_ngd , reportc cr(d.log_rgdpo L.log_rgdpo log_hc log_ck log_ngd) cr_lags(3).

Using predict

predict, [options] can be used to predict the linear prediction, the residuals, coefficients and the partialled out variables.
To predict the residuals, options residuals is used:

predict residuals, residuals

The residuals do not contain the partialled out factors, that is they are e(i,t) in equation (1) and (2).
To estimate u(i,t), the error term containing the common factors, option cfresiduals is used:

predict uit, cfresiduals

In a similar fashion, the linear prediction (option xb, the default) and the standard error of the prediction can be obtained.
The unit specific estimates for each variable and the standard error can be
obtained using options coefficients and se.
For example, obtain the coefficients for log_hc from the regression above and calculate the mean, which should be the same as the
mean group estimate:

predict coeff, coefficients

sum coeff_log_hc.

The partialled out variables can be obtained using

predict partial, partial.

Then a regression on the variables would lead to the same results as above.

If the option replace is used, then the newvar is replaced if it exists.

Pooled Estimations

All coefficients can be pooled by including them in pooled(varlist).
The constant is pooled by using the pooledconstant option:

xtdcce2 d.log_rgdpo L.log_rgdpo log_hc log_ck log_ngd , reportc cr(d.log_rgdpo L.log_rgdpo log_hc log_ck log_ngd) pooled(L.log_rgdpo log_hc log_ck log_ngd) cr_lags(3) pooledconstant.

Instrumental Variables

Endogenous variables can be instrumented by using options endogenous_vars(varlist) and exogenous_vars(varlist).
Internally ivreg2 estimates the individual coefficients.
Using the lagged level of physical capital as an instrument for the contemporaneous level, leads to:

xtdcce2 d.log_rgdpo L.log_rgdpo log_hc log_ck log_ngd (log_ck = L.log_ck), reportc cr(d.log_rgdpo L.log_rgdpo log_hc log_ck log_ngd) cr_lags(3) ivreg2options(nocollin noid).

Further ivreg2 options can be passed through using ivreg2options. Stored values in e() from ivreg2options can be posted using the option fulliv.

Error Correction Models (Pooled Mean Group Estimator)

Variables of the long run cointegration vector are defined in lr(varlist), where the first variable is the error correction speed of adjustment term.
To ensure homogeneity of the long run effects, the corresponding variables have to be included in the pooled(varlist) option.
Following the example from Blackburne and Frank (2007) with the jasa2 dataset
(the dataset is available at here from Pesaran's webpage).

xtdcce2 d.c d.pi d.y if year >= 1962 , lr(L.c pi y) p(L.c pi y) cr(_all) cr_lags(2)

xtdcce2 internally estimates equation (7) and then recalculates the long run coefficients, such that estimation results for equation (8) are obtained.
Equation (7) can be estimated adding nodivide to lr_options().
A second option is xtpmgnames in order to match the naming convention from xtpmg.

xtdcce2 d.c d.pi d.y if year >= 1962 , lr(L.c pi y) p(L.c pi y) cr(_all) cr_lags(2) lr_options(nodivide)

xtdcce2 d.c d.pi d.y if year >= 1962 , lr(L.c pi y) p(L.c pi y) cr(_all) cr_lags(2) lr_options(xtpmgnames)

Cross-Section Augmented Distributed Lag (CS-DL)

Chudik et. al (2013) estimate the long run effects of public debt on output growth
(the data is available here
on Kamiar Mohaddes' personal webpage).
In the dataset, the dependent variable is d.y and the independent variables are the inflation rate (dp) and debt to GDP ratio (d.gd).
For an ARDL(1,1,1) only the first difference of dp and d.gd are added as further covariates.
Only a contemporaneous lag of the cross-sectional averages (i.e. cr_lags(0)) of the dependent variable and
3 lags of the independent variables are added.
The lag structure is implemented by defining a numlist rather than a number in cr_lags().
For the example here cr_lags(0 3 3) is used, where the first
number refers to the first variable defined in cr(), the second to the second etc.

To replicate the results in Table 18, the following command line is used:

xtdcce2 d.y dp d.gd d.(dp d.gd), cr(d.y dp d.gd) cr_lags(0 3 3) fullsample

For an ARDL(1,3,3) model the first and second lag are of the first differences are added by putting L(0/2) in front of the d.(dp d.gd):

xtdcce2 d.y dp d.gd L(0/2).d.(dp d.gd), cr(d.y dp d.gd) cr_lags(0 3 3) fullsample

Note, the fullsample option is used to reproduce the results in Chudik et. al (2013).

Cross-Section Augmented ARDL (CS-ARDL)

Chudik et. al (2013) estimate besides the CS-DL model a CS-ARDL model.
To estimate this model all variables are treated as long run coefficients and thus added to varlist in lr(varlist).
xtdcce2 first estimates the short run coefficients and the calculates then long run coefficients,
following Equation 10.
The option lr_options(ardl) is used to invoke the estimation of the long run coefficients.
Variables with the same base (i.e. forming the same long run coefficient) need to be either
enclosed in parenthesis or tsvarlist operators need to be used.
In Table 17 an ARDL(1,1,1) model is estimated with three lags of the cross-sectional averages:

xtdcce2 d.y , lr(L.d.y dp L.dp d.gd L.d.gd) lr_options(ardl) cr(d.y dp d.gd) cr_lags(3) fullsample

xtdcce2 calculates the long run effects identifying the variables by their base.
For example it recognizes that dp and L.dp relate to the same variable.
If the lag of dp is called ldp, then the variables need to be enclosed in parenthesis.

Estimating the same model but as an ARDL(3,3,3) and with enclosed parenthesis reads:

xtdcce2 d.y , lr((L(1/3).d.y) (L(0/3).dp) (L(0/3).d.gd) ) lr_options(ardl) cr(d.y dp d.gd) cr_lags(3) fullsample

which is equivalent to coding without parenthesis:

xtdcce2 d.y , lr(L(1/3).d.y L(0/3).dp L(0/3).d.gd) lr_options(ardl) cr(d.y dp d.gd) cr_lags(3) fullsample

Regularized CCE and bootstrapping

The regularized CCE approach is only possible for static models.
To estimate a static model of growth on human, physical captial and population growth, we can use:

xtdcce2 log_rgdpo log_hc log_ck log_ngd , cr(log_rgdpo log_hc log_ck log_ngd, rcce)

xtdcce2 selects the first and second eigenvector of the cross-section averages and adds it as a variable.
The selection criterion is the ER criterion from Ahn and Horenstein (2013).
To use the GR criterion instead, the option criterion(gr) is used:

xtdcce2 log_rgdpo log_hc log_ck log_ngd , cr(log_rgdpo log_hc log_ck log_ngd, rcce(criterion(gr)))

Three regularized cross-section averages are added.
To ensure standard errors are correct, a bootstrap is run with a fixed seed:

estat bootstrap, seed(123)

To run a wild bootstrap and bootstrap confidence intervals, the options wild and percentile are added:

estat bootstrap, seed(123) wild percentile

Instead of specifing the criteria to estimate the number of eigenvectors of the rcce approach, we can hard set it using the option npc():

xtdcce2 log_rgdpo log_hc log_ck log_ngd , cr(log_rgdpo log_hc log_ck log_ngd, rcce(npc(3)))

References

Ahn, S. C., & Horenstein, A. R. 2013.
Eigenvalue ratio test for the number of factors.
Econometrica, 81(3), 1203–1227.

Baum, C. F., M. E. Schaffer, and S. Stillman 2007.
Enhanced routines for instrumental variables/generalized method of moments estimation and testing.
Stata Journal 7(4): 465-506

Blackburne, E. F., and M. W. Frank. 2007.
Estimation of nonstationary heterogeneous panels.
Stata Journal 7(2): 197-208.

Chudik, A., K. Mohaddes, M. H. Pesaran, and M. Raissi. 2013.
Debt, Inflation and Growth: Robust Estimation of Long-Run Effects in Dynamic Panel Data Model.

Chudik, A., and M. H. Pesaran. 2015.
Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors.
Journal of Econometrics 188(2): 393-420.

Chudik, A., K. Mohaddes, M. H. Pesaran, and M. Raissi. 2016.
Long-Run Effects in Large Heterogeneous Panel Data Models with Cross-Sectionally Correlated Errors
Essays in Honor of Aman Ullah. 85-135.

Ditzen, J. 2018.
Estimating Dynamic Common Correlated Effcts in Stata.
The Stata Journal, 18:3, 585 - 617.

Ditzen, J. 2021.
Estimating long run effects and the exponent of cross-sectional dependence: an update to xtdcce2.
The Stata Journal 21:3.

Eberhardt, M. 2012.
Estimating panel time series models with heterogeneous slopes.
Stata Journal 12(1): 61-71.

Holly, S., Pesaran, M. H., Yamagata, T. 2010.
A spatio-temporal model of house prices in the USA.
Journal of Econometrics 158: 160 - 172.

Feenstra, R. C., R. Inklaar, and M. Timmer. 2015.
The Next Generation of the Penn World Table. American Economic Review. www.ggdc.net/pwt

Goncalves, S., & Perron, B. 2014.
Bootstrapping factor-augmented regression models.
Journal of Econometrics, 182(1), 156–173.

Jann, B. 2005.
moremata: Stata module (Mata) to provide various functions.
Available from http://ideas.repec.org/c/boc/bocode/s455001.html.

Juodis, A. 2022.
A regularization approach to common correlated effects estimation.
Journal of Applied Econometrics, 37(4), 788– 810.

Karabıyık, H., Reese, S., & Westerlund, J. 2017.
On the role of the rank condition in cce estimation of factor-augmented panel regressions.
Journal of Econometrics, 197(1), 60–64.

Pesaran, M. H. 2006.
Estimation and inference in large heterogeneous panels with a multifactor error structure.
Econometrica 74(4): 967-1012.

Pesaran, M. H., and R. Smith. 1995.
Econometrics Estimating long-run relationships from dynamic heterogeneous panels.
Journal of Econometrics 68: 79-113.

Roodman, D., Nielsen, M. Ø., MacKinnon, J. G., & Webb, M. D. 2019.
Fast and wild: Bootstrap inference in Stata using boottest.
The Stata Journal, 19(1), 4–60.

Shin, Y., M. H. Pesaran, and R. P. Smith. 1999.
Pooled Mean Group Estimation of Dynamic Heterogeneous Panels.
Journal of the American Statistical Association 94(446): 621-634.

Westerlund, J., Perova, Y., Norkute, M. 2019. CCE in fixed-T panels.
Journal of Applied Econometrics: 1-6.

Author

Jan Ditzen (Free University of Bozen-Bolzano)

Email: jan.ditzen@unibz.it

Web: www.jan.ditzen.net

I am grateful to Arnab Bhattacharjee, David M. Drukker, Markus Eberhardt, Tullio Gregori,
Sebastian Kripfganz,
Erich Gundlach,
Achim Ahrens,
Kyle McNabb,
Sean Holly and Mark Schaffer, to the participants of the
2016 Stata Users Group meeting in London, 2018 Stata User Group meeting in Zuerich,
and two anonymous referees of The Stata Journal for many valuable comments and suggestions.
All remaining errors are my own.

The routine to check for positive definite or singular matrices was provided by Mark Schaffer, Heriot-Watt University, Edinburgh, UK.

xtdcce2 was formally called xtdcce.

Please cite as follows:

Ditzen, J. 2018. xtdcce2: Estimating dynamic common correlated effects in Stata. The Stata Journal. 18:3, 585 - 617.

The latest versions can be obtained via net from https://github.com/JanDitzen/xtdcce2.

Version History

Version 4.5 to 4.6 - May 2023

- bug fix in rcce option when using unbalanced panels

- bug fix when combining absorb() with csa (thanks to Vasilis Sarafidis for pointing it out)

Version 4.4 to 4.5 - May 2023

- supports new reghdfe version

Version 4.3 to 4.4 - May 2023

- fixed bug in trend option

Version 4.2 to 4.3 - May 2023

- fixed bug in var/cov estimation when using pooled coefficients and R matrix is zero

Version 4.1 to 4.2 - April 2023

- fixed bug in xtcd2 and xtcse2

Version 4. to 4.1 - March 2023

- fixed bug when using different lag lengths for CSA

Version 3.01 to 4 - Feb 2023

- bootstrap support

- added option mgmissing

- added option rcce

- added option fast2

- fixed error when pooled and ardl used.

Version 3.0 to 3.01

- error if abbreviation is cr() used fixed.

Version 2.0 to 3.0

- improved support for factor variables.

- fix for mm_which2.

- message for large panels.

- error in calculation for variances of cross-sectional unit specific coefficients

- fix predict program: partial now only in-sample and bug fixed when xb2 and reportc was used (thanks to Tullio Gregori for the pointers).

- added global and local cross-sectional averages

Version 1.34 to 2.0

- Bug fix in calculation of minimal T dimension, added option nodimcheck.

- Speed improvements (thanks to Achim Ahrens for the suggestions).

- Bug fixes for jackknife (thanks to Collin Rabe for the pointer).

- Bug fix in predict and if (thanks for Deniey A. Purwanto and Tullio Gregori for the pointers).

- Bug fix if binary variable used and constant partialled out.

- Bug fixed in calculation of R2, added adjusted R2 for pooled and MG regressions.

- Newey West and Westerlund, Petrova, Norkute standard errors for pooled regressions.

- invsym for rank deficient matrices.

- Added xtcse2 support.

Version 1.33 to Version 1.34

- small bug fixes in code and help file.

Version 1.32 to Version 1.33

- bug in if statements fixed.

- noomitted added, bug in cr(_all) fixed.

- added option "replace" and "cfresiduals" to predict.

- CS-DL and CS-ARDL method added.

- Output as in Stata Journal Version.

Version 1.31 to Version 1.32

- bug number of groups fixed

- predict, residual produced different results within xtdcce2 and after if panel unbalanced or trend used (thanks to Tullio Gregori for the pointer).

- check for rank condition.

- several bugs fixed.

Version 1.2 to Version 1.31

- code for regression in Mata

- corrected standard errors for pooled coefficients, option cluster not necessary any longer. Please rerun estimations if used option pooled()

- Fixed errors in unbalanced panel

- option post_full removed, individual estimates are posted in e(bi) and e(Vi)

- added option ivslow.

- legacy control for endogenous_var(), exogenous_var() and residuals().

Also see

See also: xtdcce2fast, xtcd2, xtcse2, ivreg2, xtmg, xtpmg, moremata