Help file: ivlasso and pdslasso

help pdslasso, help ivlasso                                                                 pdslasso v1.1


    pdslasso and ivlasso --
                 Programs for post-selection and post-regularization OLS or IV estimation and inference


        pdslasso depvar regressors (hd_controls) [weight] [if exp] [in range] [ , partial(varlist)
              pnotpen(varlist) aset(varlist) post(method) robust cluster(var) fe noftools
              rlasso[(name)] sqrt noisily loptions(options) olsoptions(options) noconstant ]

        ivlasso depvar regressors [(hd_controls)] (endog=instruments) [if exp] [in range] [ ,
              partial(varlist) pnotpen(varlist) aset(varlist) post(method) robust cluster(var) fe
              noftools rlasso[(name)] sqrt noisily loptions(options) ivoptions(options) first idstats
              sscset ssgamma(real) ssgridmin(real) ssgridmax(real) ssgridpoints(integer 100)
              ssgridmat(name) noconstant ]

        Note: pdslasso requires rlasso to be installed; ivlasso also requires ranktest.  See help
              rlasso and help ranktest or click on ssc install lassopack or ssc install ranktest to

        Note: the fe option will take advantage of the ftools package (if installed) for the
              fixed-effects transform; the speed gains using this package can be large.  See help
              ftools or click on ssc install ftools to install.

        Note: ivlasso also supports the simpler pdslasso syntax.

    Options               Description
    partial(varlist)       controls and instruments to be partialled-out prior to lasso estimation
    pnotpen(varlist)       controls and instruments always included, not penalized by lasso
    aset(varlist)          controls and instruments in amelioration set, always included in post-lasso
    post(method)           pds, lasso or plasso; which estimation results are to be posted in e(b) and
    robust                 heteroskedastic-robust VCE; lasso penalty loadings account for
    cluster(var)           cluster-robust VCE; lasso penalty loadings account for clustering
    fe                     fixed-effects model (requires data to be xtset)
    noftools               do not use FTOOLS package for fixed-effects transform (slower; rarely used)
    rlasso[(name)]         store and display intermediate lasso and post-lasso results from rlasso with
                            optional prefix name (if just rlasso is specified the default prefix is
                            _ivlasso_ or _pdslasso_)
    sqrt                   use sqrt-lasso instead of standard lasso
    noisily                display step-by-step intermediate rlasso estimation results
    loptions(options)      lasso options specific to rlasso estimation; see help rlasso
    olsoptions(options)    (pdslasso only) options specific to PDS OLS estimation of structural
    ivoptions(options)     (ivlasso only) options specific to PDS OLS or IV estimation of structural
    first                  (ivlasso only) display and store first-stage results for 2SLS
    idstats                (ivlasso only) request weak-identification statistics for 2SLS
    noconstant             suppress constant from regression (cannot be used with aweights or pweights)

    Sup-score test        Description
    (ivlasso only)        
    sscset                 request sup-score weak-identification-robust confidence set
    ssgamma(real)          significance level for sup-score weak-identification-robust tests and
                            confidence intervals (default=0.05, 5%)
    ssgridmin(real)        minimum value for grid search for sup-score weak-identification-robust
                            confidence intervals (default=grid centered at OLS estimate)
    ssgridmax(real)        maximum value for grid search for sup-score weak-identification-robust
                            confidence intervals (default=grid centered at OLS estimate)
    ssgridpoints(real)     number of points in grid search for sup-score weak-identification-robust
                            confidence intervals (default=100)
    ssgridmat(name)        user-supplied Stata r x k matrix of r jointly hypothesized values for the k
                            endogenous regressors to be tested using the sup-score test
    ssomitgrid(name)       supress display of sup-score test results with user-supplied grid
    ssmethod(name)         "abound" (default) = use conservative critical value (asymptotic bound)
                            c*sqrt(N)*invnormal(1-gamma/(2p)); "simulate" = simulate distribution to
                            obtain p-values for sup-score test; "select" = reject if rlasso selects any


        predict [type] newvar [if] [in] [, resid xb ]

    pdslasso and ivlasso may be used with time-series or panel data, in which case the data must be
    tsset or xtset first; see help tsset or xtset.

    aweights and pweights are supported; see help weights.  pweights is equivalent to aweights +

    All varlists may contain time-series operators or factor variables; see help varlist.


    Computational notes
    Examples of usage
    Saved results
    Citation of pdslasso and ivlasso


    pdslasso and ivlasso are routines for estimating structural parameters in linear models with many
    controls and/or instruments.  The routines use methods for estimating sparse high-dimensional
    models, specifically the lasso (Least Absolute Shrinkage and Selection Operator, Tibshirani 1996)
    and the square-root-lasso (Belloni et al. 2011, 2014).  Two approaches are implemented in pdslasso
    and ivlasso:

          1. The "post-double-selection" (PDS) methodology of Belloni et al. (2012, 2013, 2014, 2015,
               2016), denoted "PDS methodology" below.

          2. The "post-regularization" methodology of Chernozhukov, Hansen and Spindler (2015), denoted
               "CHS methodology" below.

    The implemention of these methods in pdslasso and ivlasso uses the separate Stata program rlasso,
    which provides lasso and sqrt-lasso estimation with data-driven penalization; see rlasso for

    The intution behind the methodology is most clearly seen from the PDS methodology applied to the
    case where a researcher has an outcome variable y, a structural or causal variable of interest d,
    and a large set of potential control variables x1, x2, x3, ....  The problem the researcher faces
    is that the "right" set of controls is not known.  In traditional practice, this presents her with
    a difficult choice:  use too few controls, or the wrong ones, and omitted variable bias will be
    present; use too many, and the model will suffer from overfitting.

    The PDS methodology uses the lasso estimator to select the controls.  Specifically, the lasso is
    used twice:  (1) estimate a lasso regression with y as the dependent variable and the control
    variables x1, x2, x3, ... as regressors; (2) estimate a lasso regression with d as the dependent
    variable and again the control variables x1, x2, x3, ... as regressors.  The lasso estimator
    achieves a sparse solution, i.e., most coefficients are set to zero.  The final choice of control
    variables to include in the OLS regression of y on d is the union of the controls selected selected
    in steps (1) and (2), hence the name "post-double selection" for the methodolgy.  The PDS
    methodology can be employed to select instruments as well as controls in instrumental variables

    The CHS methodology is closely related.  Instead of using the lasso-selected controls and
    instruments in a post-regularization OLS or IV estimation, the selected variables are used to
    construct orthogonalized versions of the dependent variable, the exogenous and/or endogenous causal
    variables of interest and to construct optimal instruments from the lasso-selected IVs.  The
    orthogonalized versions are based either on the lasso or post-lasso estimated coefficients; the
    post-lasso is OLS applied to lasso-selected variables.  See Chernozhukov et al. (2015) for details.

    The set of variables selected by the lasso and used in the OLS post-lasso estimation and in the PDS
    structural estimation can be augmented by variables that were penalized but not selected by the
    lasso.  The penalized variables that are used in this way to augment the post-lasso and PDS
    estimations are called the "amelioration set" and can be specified with the aset(varlist) option.
    This option affects only the CHS post-lasso-based and PDS estimations; the CHS lasso-based
    orthogonalized variables are unaffected.  See Chernozhukov et al. (2014) for details.

    pdslasso and ivlasso report the PDS-based and the two (lasso and post-lasso) CHS-based estimations.
    If the sqrt option is specified, instead of the lasso the sqrt-lasso estimator is used; see rlasso
    for further details and references.

    If the IV model is weakly identified (the instruments are only weakly correlated with the
    endogenous regressors) Belloni et al. (2012, 2013) suggest using weak-identification-robust
    hypothesis tests and confidence sets based the Chernozhukov et al. (2013) sup-score test.  The
    intuition behind the sup-score test is similar to that of the Anderson-Rubin (1949) test.  Consider
    the simplest case (a single endogenous regressor d and no exogenous regressors or controls) where
    the null hypothesis is that the coefficient on d is H0:beta=b0.  If the null is true, then the
    structural residual is simply e=y-b0*d.  Under the additional assumption that the instruments are
    valid (orthogonal to the true disturbance), they should be uncorrelated with e.

    The sup-score tests reported by ivlasso are in effect high-dimensional versions of the
    Anderson-Rubin test.  The test is implemented in rlasso; see help rlasso for details.
    Specifically, ivlasso reports sup-score tests of statistical significance of the instruments where
    the dependent variable is e=y-b0*d, the instruments are regressors, and b0 is a hypothesized value
    of the coefficient on d; a large test statistic indicates rejection of the null H0:beta=b0.  The
    default is to use a conservative (asymptotic bound) critical value as suggested by Belloni et al.
    (2012, 2013) (option ssmethod(abound)).  Alternative methods are to use p-values obtained by
    simulation via a multiplier bootstrap (option ssmethod(simulate)), or to estimate a lasso
    regression with the instruments as regressors, and if (no) instruments are selected we (fail to)
    reject the null H0:beta=b0 at the gamma significance level (option ssmethod(select)).

    A 100*(1-gamma)% sup-score-based confidence set can be constructed by a grid search over the range
    of hypothesized values of beta.  ivlasso reports the result of the sup-score test of the null
    H0:beta=0 with the idstats option, and in addition, for the single endogenous regressor case only,
    reports sup-score confidence sets with the sscset option.  For the multiple-endogenous regressor
    case, sets of jointly hypothesized values for the componets of beta can be tested using the
    ssgridmat(name) option.  The matrix provided in the option should be an r x k Stata matrix, where
    each row contains a set of values that together specify a null hypothesis for the coefficients of
    the k endogenous regressors.  This option allows the user to specify a grid search in multiple

Computational notes

    The various options available for the underlying calls to rlasso can be controlled via the option
    loptions(rlasso option list).  The rlasso option center, to center moments in heteroskedastic and
    cluster-robust loadings, will be a commonly-employed option.  This can be specified by

    Another rlasso option that may often be used is to "pre-standardize" the data to have unit variance
    prior to computing the lasso coefficients with the prestd option.  This is a computational
    alternative to the rlasso default of standardizing "on the fly" (i.e., incorporating the
    standardization into the lasso penalty loadings).  This is specified by lopt(prestd).  The results
    are equivalent in theory.  The prestd option can lead to improved numerical precision or more
    stable results in the case of difficult problems; the cost is (a typically small) computation time
    required to standardize.

    rlasso implements a version of the lasso with data-dependent penalization and, for the
    heteroskedastic and clustered cases, regressor-specific penalty loadings; see rlasso for details.
    Note that specification of robust or cluster(.) as options to pdslasso or ivlasso automatically
    implies the use of robust or cluster-robust lasso penalty loadings.  Penalty loadings and VCE type
    can be separately controlled via the olsoptions(.) (for pdslasso) or ivoptions(.) (for ivlasso) vs.
    loptions(rlasso option list); for example, olsoptions(cluster(clustvar)) + loptions(robust) would
    use heteroskedastic-robust penalty loadings for the lasso estimations and a cluster-robust
    covariance estimator for the PDS and CHS estimations of the structural equation.

    Either the partial(varlist) option or the pnotpen(varlist) option can be used for variables that
    should not be penalized by the lasso.  By the Frisch-Waugh-Lovell Theorem for the lasso (Yamada
    2017), the estimated lasso coefficients are the same in theory whether the unpenalized regressors
    are partialled-out or given zero penalty loadings, so long as the same penalty loadings are used
    for the penalized regressors in both cases.  Although the options are equivalent in theory,
    numerical results can differ in practice because of the different calculation methods used; see
    rlasso for further details.  The constant, if present, is always unpenalized or partialled-out By
    default the constant (if present) is not penalized if there are no regressors being partialled out;
    this is equivalent to mean-centering prior to estimation.  The exception to this is if aweights or
    aweights are specified, in which case the constant is partialled-out.  The partial(varlist) option
    always partials out the constant (if present) along with the variables specified in varlist; to
    partial out just the constant, specify partial(_cons).  Partialling-out of controls is done by
    ivlasso; partialling-out of instruments is done in the lasso estimation by rlasso.

    The lasso and sqrt-lasso estimations are obtained via numerical methods (coordinate descent).
    Results can be unstable for difficult problems (e.g., if the scaling of variables covers a wide
    range of magnitudes).  Using variables that are all measured on a similar scale will help (as
    usual).  Partialling-out variables is usually preferable to specifying them as unpenalized.  See 
    rlasso for discussion of the various options for controlling the numerical methods used.

    The sup-score-based tests reported by ivlasso come in three versions:  (a) using
    lasso-orthogonalized variables, where the variables have first been orthogonalized with respect to
    the high-dimensional controls using the lasso; (b) using post-lasso-orthogonalized variables; (c)
    using the variables without any orthogonalization.  The orthogonalizations use the same lasso
    settings as in the main estimation.  After orthgonalization, e~ = y~ - b0*d~ is constructed (where
    a tilde indicates an orthogonalized variable), and then the sup-score test is conducted using e~
    and the instruments.  Versions (a) and (b) are not reported if there are no high-dimensional
    controls.  Version (c) is available if there are high-dimensional controls but only if the
    method(select) option is used.  The sup-score-based tests are not available if the specification
    also includes either exogenous causal regressors or unpenalized instruments.

    For large datasets, obtaining the p-value for the sup-score test by simulation (multiplier
    bootstrap, ssmethod(simulate) option) can be time-consuming.  In such cases, using the default
    method of a conservative (asymptotic bound) critical value (ssmethod(abound) option) will be much

    The grid search to construct the sup-score confidence set can be controlled by the ssgridmin,
    ssgridmax and ssgridpoints options.  If these options are not specified by the user, a 100-point
    grid centered on the OLS estimator is used.

    The fe fixed-effects option is equivalent to (but computationally faster and more accurate than)
    specifying unpenalized panel-specific dummies.  The fixed-effects ("within") transformation also
    removes the constant as well as the fixed effects.  The panel variable used by the fe option is the
    panel variable set by xtset.

    rlasso, like the lasso in general, accommodates possibly perfectly-collinear sets of regressors.
    Stata's factor variables are supported by rlasso.  Users therefore have the option of specifying as
    high-dimensional controls or instruments one or more complete sets of factor variables or
    interactions with no base levels using the ibn prefix.  This can be interpreted as allowing the
    lasso to choose the members of the base category.

    For a detailed discussion of an R implementation of this methodology, see Spindler et al. (2016).

Examples using data from Acemoglu-Johnson-Robinson (2001)

    Load and reorder AJR data for Table 6 and Table 8 (datasets need to be in current directory).
        . clear
        . (click to download from
        . unzipfile maketable6
        . (click to download from
        . unzipfile maketable8
        . use maketable6
        . merge 1:1 shortnam using maketable8
        . keep if baseco==1
        . order shortnam logpgp95 avexpr lat_abst logem4 edes1975 avelf, first
        . order indtime euro1900 democ1 cons1 democ00a cons00a, last

    Alternatively, load AJR data from our website (no manual download required):
        . clear
        . use

    Examples with exogenous regressors:

    Replicate OLS results in Panel C, col. 9.
        . reg logpgp95 avexpr lat_abst edes1975 avelf temp* humid* steplow-oilres

    Basic usage: select from high-dim controls.
        . pdslasso logpgp95 avexpr (lat_abst edes1975 avelf temp* humid* steplow-oilres)

    As above, hetoroskedastic-robust.
        . pdslasso logpgp95 avexpr (lat_abst edes1975 avelf temp* humid* steplow-oilres), rob

    Specify that latitude is an unpenalized control to be partialled out.
        . pdslasso logpgp95 avexpr (lat_abst edes1975 avelf temp* humid* steplow-oilres),

    Specify that latitude is an unpenalized control using the notpen option (equivalent).
        . pdslasso logpgp95 avexpr (lat_abst edes1975 avelf temp* humid* steplow-oilres),

    Specify that latitude is in the amelioration set.
        . pdslasso logpgp95 avexpr (lat_abst edes1975 avelf temp* humid* steplow-oilres),

    Example with endogenous regressor, high-dimensional controls and low-dimensional instrument:

    Replicate IV results in Panels A & B, col. 9.
        . ivreg logpgp95 (avexpr=logem4) lat_abst edes1975 avelf temp* humid* steplow-oilres, first

    Select controls; specify that logem4 is an unpenalized instrument to be partialled out.
        . ivlasso logpgp95 (avexpr=logem4) (lat_abst edes1975 avelf temp* humid* steplow-oilres),

    Example with endogenous regressor and high-dimensional instruments and controls:

    Select controls and instruments; specify that logem4 is an unpenalized instrument and lat_abst is
    an unpenalized control; request weak identification stats and first-stage results.
        . ivlasso logpgp95 (lat_abst edes1975 avelf temp* humid* steplow-oilres) (avexpr=logem4
            euro1900-cons00a), partial(logem4 lat_abst) idstats first

    Replay first-stage estimation. (Can also use est restore to make this the current estimation
        . est replay _ivlasso_avexpr

    Select controls and instruments; specify that lat_abst is an unpenalized control; request weak
    identification stats and sup-score confidence sets.
        . ivlasso logpgp95 (lat_abst edes1975 avelf temp* humid* steplow-oilres) (avexpr=logem4
            euro1900-cons00a), partial(lat_abst) idstats sscset

    As above but heteroskedastic-robust and use grid options to control grid search and test level;
    also set seed in rlasso options to make multiplier-bootstrap p-values replicable.
        . ivlasso logpgp95 (lat_abst edes1975 avelf temp* humid* steplow-oilres) (avexpr=logem4
            euro1900-cons00a), partial(lat_abst) rob idstats sscset ssgridmin(0) ssgridmax(2)
            ssgamma(0.1) lopt(seed(1))

Examples using data from Angrist-Krueger (1991)

    Load AK data and rename variables (dataset needs to be in current directory).  NB: this is a large
    dataset (330k observations) and estimations may take some time to run on some installations.
        . clear
        . (click to download from
        . unzipfile
        . infix lnwage 1-9 edu 10-20 yob 21-31 qob 32-42 pob 43-53 using asciiqob.txt

    Alternative source (no unzipping needed):
        . use

    xtset data by place of birth (state):
        . xtset pob

    Table VII (1930-39) col 2. Year and state of birth = yob & pob.
        . ivregress 2sls lnwage i.pob i.yob (edu=i.qob i.yob#i.qob i.pob#i.qob)

    Fixed effects; select year controls and IVs; IVs are QOB and QOBxYOB.
        . ivlasso lnwage (i.yob) (edu=i.qob i.yob#i.qob), fe

    Fixed effects; select year controls and IVs; IVs are QOB, QOBxYOB, QOBxSOB.
        . ivlasso lnwage (i.yob) (edu=i.qob i.yob#i.qob i.pob#i.qob), fe

    All dummies & interactions incl. base levels.
        . ivlasso lnwage (i.yob) (edu=ibn.qob ibn.yob#ibn.qob ibn.pob#ibn.qob), fe

Example using data from Belloni et al. (2015)

    Load dataset on eminent domain (available at journal website).
        . clear
        . import excel using, first

    Settings used in Belloni et al. (2015) - results as in journal replication file (not text)
    (Includes use of undocumented rlasso option c0(real) to control initial penalty loadings.)
    Store rlasso intermediate results for replay later.
        . ivlasso CSIndex (NumProCase = Z*), nocons robust rlasso lopt(lalt corrnum(0) maxpsiiter(100)
        . estimates replay _ivlasso_step5_NumProCase

Saved results

    ivlasso saves the following in e():

      e(N)               sample size
      e(xhighdim_ct)     number of all high-dimensional controls
      e(zhighdim_ct)     number of all high-dimensional instruments
      e(N_clust)         number of clusters in cluster-robust estimation
      e(N_g)             number of groups in fixed-effects model
      e(ss_gamma)        significance level in sup-score tests and CIs
      e(ss_level)        test level in % in sup-score tests and CIs (=100*(1-gamma))
      e(ss_gridmin)      min grid point in sup-score CI
      e(ss_gridmax)      max grid point in sup-score CI
      e(ss_gridpoints)   number of grid points in sup-score CI

      e(cmd)             pdslasso or ivlasso
      e(depvar)          name of dependent variable
      e(dexog)           name(s) of exogenous structural variable(s)
      e(dendog)          name(s) endogenous structural variable(s)
      e(xhighdim)        names of high-dimensional control variables
      e(zhighdim)        names of high-dimensional instruments
      e(method)          lasso or sqrt-lasso
      e(ss_null)         result of sup-score test (reject/fail to reject)
      e(ss_null_l)       result of lasso-orthogonalized sup-score test (reject/fail to reject)
      e(ss_null_pl)      result of post-lasso-orthogonalized sup-score test (reject/fail to reject)
      e(ss_cset)         confidence interval for sup-score test
      e(ss_cset_l)       confidence interval for lasso-orthogonalized sup-score test
      e(ss_cset_pl)      confidence interval for post-lasso-orthogonalized sup-score test
      e(ss_method)       simulate, abound or select

      e(b)               posted coefficient vector
      e(V)               posted variance-covariance matrix
      e(beta_pds)        PDS coefficient vector
      e(V_pds)           PDS variance-covariance matrix
      e(beta_lasso)      CHS lasso-based coefficient vector
      e(V_lasso)         CHS lasso-based variance-covariance matrix
      e(beta_plasso)     CHS post-lasso-based coefficient vector
      e(V_plasso)        CHS post-lasso-based variance-covariance matrix
      e(ss_citable)      sup-score test results used to construct confidence sets
      e(ss_gridmat)      sup-score test results using user-specified grid



    Anderson, T. W. and Rubin, H. 1949.  Estimation of the Parameters of Single Equation in a Complete
        System of Stochastic Equations.  Annals of Mathematical Statistics 20:46-63. 

    Angrist, J. and Kruger, A. 1991.  Does compulsory school attendance affect schooling and earnings?
        Quarterly Journal of Economics 106(4):979-1014.

    Belloni, A., Chernozhukov, V. and Wang, L. 2011.  Square-root lasso: Pivotal recovery of sparse
        signals via conic programming.  Biometrika 98:791-806.

    Belloni, A., Chen, D., Chernozhukov, V. and Hansen, C. 2012.  Sparse models and methods for optimal
        instruments with an application to eminent domain.  Econometrica 80(6):2369-2429. 

    Belloni, A., Chernozhukov, V. and Hansen, C. 2013.  Inference for high-dimensional sparse
        econometric models.  In Advances in Economics and Econometrics: 10th World Congress, Vol. 3:
        Econometrics, Cambridge University Press: Cambridge, 245-295.

    Belloni, A., Chernozhukov, V. and Hansen, C. 2014.  Inference on treatment effects after selection
        among high-dimensional controls.  Review of Economic Studies 81:608-650. 

    Belloni, A., Chernozhukov, V. and Hansen, C. 2015.  High-dimensional methods and inference on
        structural and treatment effects.  Journal of Economic Perspectives 28(2):29-50. 

    Belloni, A., Chernozhukov, V., Hansen, C. and Kozbur, D. 2016.  Inference in High Dimensional Panel
        Models with an Application to Gun Control.  Journal of Business and Economic Statistics

    Belloni, A., Chernozhukov, V. and Wang, L. 2014.  Pivotal estimation via square-root-lasso in
        nonparametric regression.  Annals of Statistics 42(2):757-788. 

    Chernozhukov, V., Chetverikov, D. and Kato, K. 2013.  Gaussian approximations and multiplier
        bootstrap for maxima of sums of high-dimensional random vectors.  Annals of Statistics

    Chernozhukov, V. Hansen, C., and Spindler, M. 2015.  Post-selection and post-regularization
        inference in linear models with many controls and instruments.  American Economic Review:
        Papers & Proceedings 105(5):486-490. 

    Correia, S. 2016.  FTOOLS: Stata module to provide alternatives to common Stata commands optimized
        for large datasets.

    Spindler, M., Chernozhukov, V. and Hansen, C. 2016.  High-dimensional metrics.

    Tibshirani, R. 1996.  Regression Shrinkage and Selection via the Lasso.  Journal of the Royal
        Statistical Society. Series B (Methodological) 58(1):267-288.

    Yamada, H. 2017.  The Frisch-Waugh-Lovell Theorem for the lasso and the ridge regression.
        Communications in Statistics - Theory and Methods 46(21):10897-10902. 


    Please check our website for more information.


    To get the latest stable version of lassopack and pdslasso from our website, check the installation
    instructions at  We update the website versions more
    frequently than the SSC version.

    To verify that pdslasso is correctly installed, click on or type whichpkg pdslasso (which requires 
    whichpkg to be installed; ssc install whichpkg).


    Thanks to Sergio Correia for advice on the use of the FTOOLS package.

Citation of pdslasso and ivlasso

    pdslasso and ivlasso are not official Stata commands.  They are free contributions to the research
    community, like a paper.  Please cite it as such:

    Ahrens, A., Hansen, C.B., Schaffer, M.E. 2018.  pdslasso and ivlasso: Progams for post-selection
        and post-regularization OLS or IV estimation and inference. 


        Achim Ahrens, Economic and Social Research Institute, Ireland
        Christian B. Hansen, University of Chicago, USA

        Mark E Schaffer, Heriot-Watt University, UK

Also see

       Help:  rlasso, lasso2, cvlasso (if installed)