Stata 15 help for spivregress

[SP] spivregress -- Spatial autoregressive models with endogenous covariates


spivregress depvar [varlist_1] (varlist_2 = varlist_iv) [if] [in] [, options]

varlist_1 is the list of included exogenous regressors.

varlist_2 is the list of endogenous regressors.

varlist_iv is the list of excluded exogenous regressors used with varlist_1 as instruments for varlist_2.

options Description ------------------------------------------------------------------------- Model dvarlag(spmatname) spatially lagged dependent variable; repeatable errorlag(spmatname) spatially lagged errors; repeatable ivarlag(spmatname : varlist) spatially lagged exogenous variables from varlist_1; repeatable noconstant suppress constant term heteroskedastic treat errors as heteroskedastic force allow estimation when estimation sample is a subset of the sample used to create the spatial weighting matrix impower(#) order of instrumental-variable approximation

Reporting level(#) set confidence level; default is level(95) display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Optimization optimization_options control the optimization process; seldom used

coeflegend display legend instead of statistics -------------------------------------------------------------------------

varlist_1, varlist_2, varlist_iv, and varlist specified in ivarlag() may contain factor variables; see fvvarlist. coeflegend does not appear in the dialog box. See [SP] spivregress postestimation for features available after estimation.


Statistics > Spatial autoregressive models


spivregress is the equivalent of ivregress for spatial data. spivregress fits spatial autoregressive (SAR) models, also known as simultaneous autoregressive models, where the models may contain additional endogenous variables as well as exogenous variables. These models can be used to account for possible dependence between the outcome variable and the unobserved errors.

For models without endogenous regressors, see [SP] spregress.

If you have not read [SP] intro 1 - [SP] intro 8, you should do so before using spivregress. Your data must be Sp data to use spivregress. See [SP] intro 3 for instructions on how to prepare your data.

To specify spatial lags, you will need to have one or more spatial weighting matrices. See [SP] intro 2 and [SP] spmatrix for an explanation of the types of weighting matrices and how to create them.


+-------+ ----+ Model +------------------------------------------------------------

dvarlag(spmatname) specifies a spatial weighting matrix that defines a spatial lag of the dependent variable. This option is repeatable to allow higher-order models. By default, no spatial lags of the dependent variable are included.

errorlag(spmatname) specifies a spatial weighting matrix that defines a spatially lagged error. This option is repeatable to allow higher-order models. By default, no spatially lagged errors are included.

ivarlag(spmatname : varlist) specifies a spatial weighting matrix and a list of exogenous variables that define spatial lags of the variables. The variables in varlist must be a subset of the exogenous variables in varlist_1. This option is repeatable to allow spatial lags created from different matrices. By default, no spatial lags of the exogenous variables are included.

noconstant; see [R] estimation options.

heteroskedastic specifies that the estimator treat the errors as heteroskedastic instead of homoskedastic, which is the default; see Methods and formulas in [SP] spregress.

force requests that estimation be done when the estimation sample is a proper subset of the sample used to create the spatial weighting matrices. The default is to refuse to fit the model. Weighting matrices potentially connect all the spatial units. When the estimation sample is a subset of this space, the spatial connections differ and spillover effects can be altered. In addition, the normalization of the weighting matrix differs from what it would have been had the matrix been normalized over the estimation sample. The better alternative to force is first to understand the spatial space of the estimation sample and, if it is sensible, then create new weighting matrices for it. See [SP] spmatrix and Missing values, dropped observations, and the W matrix in [SP] intro 2.

impower(#) specifies the order of an instrumental-variable approximation used in fitting the model. The derivation of the estimator involves a product of # matrices. Increasing # may improve the precision of the estimation and will not cause harm, but will require more computer time. The default is impower(2). See Methods and formulas for additional details on impower(#).

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+--------------+ ----+ Optimization +-----------------------------------------------------

optimization_options: iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), and nonrtolerance; see [M-5] optimize().

The following option is available with spivregress but is not shown in the dialog box:

coeflegend; see [R] estimation options.


Setup . copy . . copy . . use dui_southern . spset

Create a contiguity weighting matrix with the default spectral normalization . spmatrix create contiguity W

Fit a generalized spatial two-stage least-squares regression . spivregress dui nondui vehicles i.dry (police = elect), dvarlag(W) errorlag(W)

Same as above but add a spatial lag of the covariate dry . spivregress dui nondui vehicles i.dry (police = elect), dvarlag(W) errorlag(W) ivarlag(W: i.dry)

Stored results

spivregress stores the following in e():

Scalars e(N) number of observations e(k) number of parameters e(df_m) model degrees of freedom e(df_c) degrees of freedom for comparison test e(iterations) number of generalized method of moments iterations e(iterations_2sls) number of two-stage least-squares iterations e(rank) rank of e(V) e(r2_p) pseudo-R-squared e(chi2) chi-squared e(chi2_c) chi-squared for comparison test e(p) p-value for model test e(p_c) p-value for test of spatial terms e(converged) 1 if generalized method of moments converged, 0 otherwise e(converged_2sls) 1 if two-stage least-squares converged, 0 otherwise

Macros e(cmd) spivregress e(cmdline) command as typed e(depvar) name of dependent variable e(indeps) names of independent variables e(idvar) name of ID variable e(estimator) gs2sls e(title) title in estimation output e(constant) hasconstant or noconstant e(exogr) exogenous regressors e(dlmat) names of spatial weighting matrices applied to depvar e(elmat) names of spatial weighting matrices applied to errors e(het) heteroskedastic or homoskedastic e(chi2type) Wald; type of model chi-squared test e(properties) b V e(estat_cmd) program used to implement estat e(predict) program used to implement predict e(marginsok) predictions allowed by margins e(marginsnotok) predictions disallowed by margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(delta_2sls) two-stage least-squares estimates of coefficients in spatial lag equation e(rho_2sls) generalized method of moments estimates of coefficients in spatial error equation e(V) variance-covariance matrix of the estimators

Functions e(sample) marks estimation sample

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