Two-stage least squares.

Carries out estimation using two-stage least squares.

Syntax

tsls(options) y x1 [x2 x3 ...] @ z1 [z2 z3 ...]

tsls(options) specification @ z1 [z2 z3 ...]

To use the tsls command, list the dependent variable first, followed by the regressors, then any AR or MA error specifications, then an “@”-sign, and finally, a list of exogenous instruments. You may estimate nonlinear equations or equations specified with formulas by first providing a specification, then listing the instrumental variables after an “@”-sign.

There must be at least as many instrumental variables as there are independent variables. All exogenous variables included in the regressor list should also be included in the instrument list. A constant is included in the list of instrumental variables even if not explicitly specified.

Options

Non-Panel TSLS Options

nocinst | Do not automatically include a constant as an instrument. |

w=arg | Weight series or expression. Note: we recommend that, absent a good reason, you employ the default settings Inverse std. dev. weights (“wtype=istdev”) with EViews default scaling (“wscale=eviews”) for backward compatibility with versions prior to EViews 7. |

wtype=arg (default=“istdev”) | Weight specification type: inverse standard deviation (“istdev”), inverse variance (“ivar”), standard deviation (“stdev”), variance (“var”). |

wscale=arg | Weight scaling: EViews default (“eviews”), average (“avg”), none (“none”). The default setting depends upon the weight type: “eviews” if “wtype=istdev”, “avg” for all others. |

z | Turn off backcasting in ARMA models. |

cov=keyword | Covariance type (optional): “white” (White diagonal matrix), “hac” (Newey-West HAC). |

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

covlag=arg (default=1) | Whitening lag specification: integer (user-specified lag value), “a” (automatic selection). |

covinfosel=arg (default=“aic”) | Information criterion for automatic selection: “aic” (Akaike), “sic” (Schwarz), “hqc” (Hannan-Quinn) (if “lag=a”). |

covmaxlag=integer | Maximum lag-length for automatic selection (optional) (if “lag=a”). The default is an observation-based maximum of . |

covkern=arg (default=“bart”) | Kernel shape: “none” (no kernel), “bart” (Bartlett, default), “bohman” (Bohman), “daniell” (Daniel), “parzen” (Parzen), “parzriesz” (Parzen-Riesz), “parzgeo” (Parzen-Geometric), “parzcauchy” (Parzen-Cauchy), “quadspec” (Quadratic Spectral), “trunc” (Truncated), “thamm” (Tukey-Hamming), “thann” (Tukey-Hanning), “tparz” (Tukey-Parzen). |

covbw=arg (default=“fixednw”) | Kernel Bandwidth: “fixednw” (Newey-West fixed), “andrews” (Andrews automatic), “neweywest” (Newey-West automatic), number (User-specified bandwidth). |

covnwlag=integer | Newey-West lag-selection parameter for use in nonparametric kernel bandwidth selection (if “covbw=neweywest”). |

covbwint | Use integer portion of bandwidth. |

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 estimator coefficient vector as starting values for equations specified by list 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 TSLS 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”. Does not apply to coefficients for AR and MA terms which are set to EViews determined default values. |

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. |

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

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

p | Print basic estimation results. |

Panel TSLS Options

cx=arg | Cross-section effects. For fixed effects estimation, use “cx=f”; for random effects estimation, use “cx=r”. |

per=arg | Period effects. For fixed effects estimation, use “cx=f”; for random effects estimation, use “cx=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 (“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. |

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 in unbalanced data using the subset of available observations for a cluster. |

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

s | Use the current coefficient values in estimator coefficient vector as starting values for equations specified by list with AR terms (see also param ). |

s=number | Determine starting values for equations specified by list with AR terms. Specify a number between zero and one representing the fraction of TSLS estimates computed without AR 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”. Does not apply to coefficients for AR terms which are instead set to EViews determined default values. |

m=integer | Set maximum number of iterations. |

c=number | 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. |

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. |

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

p | Print estimation results. |

Examples

tsls y_d c cpi inc ar(1) @ lw(-1 to -3)

estimates an UNTITLED equation using TSLS regression of Y_D on a constant, CPI, INC with AR(1) using a constant, LW(-1), LW(-2), and LW(-3) as instruments.

param c(1) .1 c(2) .1

tsls(s,m=500) y_d=c(1)+inc^c(2) @ cpi

estimates a nonlinear TSLS model using a constant and CPI as instruments. The first line sets the starting values for the nonlinear iteration algorithm.

Cross-references

See “Additional Regression Tools” and “Two-Stage Least Squares” for details on two-stage least squares estimation in single equations and systems, respectively. “Instrumental Variables” discusses estimation using pool objects, while “Instrumental Variables Estimation” discusses estimation in panel structured workfiles.