Estimation by linear or nonlinear least squares regression.

ls estimates cross-section weighed least squares, feasible GLS, and fixed and random effects models.

Syntax

pool_name.ls(options) y [x1 x2 x3...] [@cxreg z1 z2 ...] [@perreg z3 z4 ...]

ls carries out pooled data estimation. Type the name of the dependent variable followed by one or more lists of regressors. The first list should contain ordinary and pool series that are restricted to have the same coefficient across all members of the pool. The second list, if provided, should contain pool variables that have different coefficients for each cross-section member of the pool. If there is a cross-section specific regressor list, the two lists must be separated by “@CXREG”. The third list, if provided, should contain pool variables that have different coefficients for each period. The list should be separated from the previous lists by “@PERREG”.

You may include AR terms as regressors in either the common or cross-section specific lists. AR terms are, however, not allowed for some estimation methods. MA terms are not supported.

Options

m=integer | Set maximum number of iterations. |

c=scalar | Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coefficients. The criterion will be set to the nearest value between 1e-24 and 0.2. |

s | Use the current coefficient values in “C” as starting values for equations with AR or MA terms (see also param). |

s=number | Determine starting values for equations specified by list with AR or MA terms. Specify a number between zero and one representing the fraction of preliminary least squares estimates computed without AR or MA terms to be used. Note that out of range values are set to “s=1”. Specifying “s=0” initializes coefficients to zero. By default EViews uses “s=1”. |

numericderiv / ‑numericderiv | [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. |

fastderiv / ‑fastderiv | [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default. |

showopts / ‑showopts | [Do / do not] display the starting coefficient values and estimation options in the estimation output. |

cx=arg | Cross-section effects: (default) none, fixed effects (“cx=f”), random effects (“cx=r”). |

per=arg | Period effects: (default) none, fixed effects (“per=f”), random effects (“per=r”). |

wgt=arg | GLS weighting: (default) none, cross-section system weights (“wgt=cxsur”), period system weights (“wgt=persur”), cross-section diagonal weighs (“wgt=cxdiag”), period diagonal weights (“wgt=perdiag”). |

cov=arg | Coefficient covariance method: (default) ordinary, White cross-section system robust (“cov=cxwhite”), White period system robust (“cov=perwhite”), White heteroskedasticity robust (“cov=stackedwhite”), Cross-section system robust/PCSE (“cov=cxsur”), Period system robust/PCSE (“cov=persur”), Cross-section heteroskedasticity robust/PCSE (“cov=cxdiag”), Period heteroskedasticity robust/PCSE (“cov=perdiag”). |

keepwgts | Keep full set of GLS weights used in estimation with object, if applicable (by default, only small memory weights are saved). |

rancalc=arg (default=“sa”) | Random component method: Swamy-Arora (“rancalc=sa”), Wansbeek-Kapteyn (“rancalc=wk”), Wallace-Hussain (“rancalc=wh”). |

nodf | Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. |

b | Estimate using a balanced sample (pool estimation only). |

coef=arg | Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector. |

iter=arg (default= “onec”) | Iteration control for GLS specifications: perform one weight iteration, then iterate coefficients to convergence (“iter=onec”), iterate weights and coefficients simultaneously to convergence (“iter=sim”), iterate weights and coefficients sequentially to convergence (“iter=seq”), perform one weight iteration, then one coefficient step (“iter=oneb”). Note that random effects models currently do not permit weight iteration to convergence. |

unbalsur | Compute SUR factorization for unbalanced data using the subset of available observations in a cluster. |

prompt | Force the dialog to appear from within a program. |

p | Print basic estimation results. |

Examples

pool1.ls dy? c inv? edu? year

estimates pooled OLS of DY? on a constant, INV?, EDU? and YEAR.

pool1.ls(cx=f) dy? @cxreg inv? edu? year ar(1)

estimates a fixed effects model without restricting any of the coefficients to be the same across pool members.

Cross-references

“Basic Regression Analysis” and “Additional Regression Tools” discuss the various regression methods in greater depth.

See “Pooled Time Series, Cross-Section Data” for a discussion of pool estimation, and “Panel Estimation” for a discussion of panel equation estimation.

See “Special Expression Reference” for special terms that may be used in ls specifications.

See also Pool::tsls for instrumental variables estimation.