Stata 15 help for spregress

[SP] spregress -- Spatial autoregressive models

Syntax

Generalized spatial two-stage least squares

spregress depvar [indepvars] [if] [in], gs2sls [gs2sls_options]

Maximum likelihood

spregress depvar [indepvars] [if] [in], ml [ml_options]

gs2sls_options Description ------------------------------------------------------------------------- Model * gs2sls use generalized spatial two-stage least-squares estimator dvarlag(spmatname) spatially lagged dependent variable; repeatable errorlag(spmatname) spatially lagged errors; repeatable ivarlag(spmatname : varlist) spatially lagged independent variables; 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 -------------------------------------------------------------------------

ml_options Description ------------------------------------------------------------------------- Model * ml use maximum likelihood estimator dvarlag(spmatname) spatially lagged dependent variable; not repeatable errorlag(spmatname) spatially lagged errors; not repeatable ivarlag(spmatname : varlist) spatially lagged independent variables; repeatable noconstant suppress constant term constraints(constraints) apply specified linear constraints force allow estimation when estimation sample is a subset of the sample used to create the spatial weighting matrix gridsearch(#) resolution of the initial-value search grid; seldom used

SE/Robust vce(vcetype) vcetype may be oim or robust

Reporting level(#) set confidence level; default is level(95) nocnsreport do not display constraints display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Maximization maximize_options control the maximization process; seldom used

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

* You must specify either gs2sls or ml. indepvars and varlist specified in ivarlag() may contain factor variables; see fvvarlist. coeflegend does not appear in the dialog box. See [SP] spregress postestimation for features available after estimation.

Menu

Statistics > Spatial autoregressive models

Description

spregress is the equivalent of regress for spatial data. spregress fits spatial autoregressive (SAR) models, also known as simultaneous autoregressive models. If you have not read [SP] intro 1 - [SP] intro 8, you should do so before using spregress.

To use spregress, your data must be Sp data. 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.

Options for spregress, gs2sls

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

gs2sls requests that the generalized spatial two-stage least-squares estimator be used.

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 independent variables that define spatial lags of the variables. This option is repeatable to allow spatial lags created from different matrices. By default, no spatial lags of the independent 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 spregress, gs2sls but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Options for spregress, ml

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

ml requests that the maximum likelihood estimator be used.

dvarlag(spmatname) specifies a spatial weighting matrix that defines a spatial lag of the dependent variable. Only one dvarlag() option may be specified. By default, no spatial lags of the dependent variable are included.

errorlag(spmatname) specifies a spatial weighting matrix that defines a spatially lagged error. Only one errorlag() option may be specified. By default, no spatially lagged errors are included.

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

noconstant; see [R] estimation options.

constraints(constraints); see [R] estimation options.

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. This is the same force option described for use with spregress, gs2sls.

gridsearch(#) specifies the resolution of the initial-value search grid. The default is gridsearch(0.1). You may specify any number between 0.001 and 0.1 inclusive.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim) and that are robust to nonnormal independent and identically distributed (i.i.d.) disturbance (robust). See [R] vce_option.

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

level(#), nocnsreport; 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.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), and nonrtolerance; see [R] maximize.

The following option is available with spregress, ml but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

Setup . copy http://www.stata-press.com/data/r15/homicide1990.dta . . copy http://www.stata-press.com/data/r15/homicide1990_shp.dta . . use homicide1990 . spset

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

Fit a generalized spatial two-stage least-squares regression . spregress hrate ln_population ln_pdensity gini, gs2sls dvarlag(W)

Same as above but add a spatial autoregressive error term . spregress hrate ln_population ln_pdensity gini, gs2sls dvarlag(W) errorlag(W)

Same as above but add terms representing spatial lags of the independent variables . spregress hrate ln_population ln_pdensity gini, gs2sls dvarlag(W) errorlag(W) ivarlag(W: ln_population ln_pdensity gini)

Stored results

spregress, gs2sls 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 test of spatial terms 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 test of spatial terms 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) spregress 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

spregress, ml 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 test of spatial terms e(ll) log likelihood e(iterations) number of maximum log-likelihood estimation iterations e(rank) rank of e(V) e(r2_p) pseudo-R-squared e(chi2) chi-squared e(chi2_c) chi-squared for test of spatial terms e(p) p-value for model test e(p_c) p-value for test of spatial terms e(converged) 1 if converged, 0 otherwise

Macros e(cmd) spregress 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) ml e(title) title in estimation output e(constant) hasconstant or noconstant e(dlmat) name of spatial weighting matrix applied to depvar e(elmat) name of spatial weighting matrix applied to errors e(chi2type) {cmd Wald}; type of model chi-squared test e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(ml_method) type of ml method e(technique) maximization technique 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(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(Hessian) Hessian matrix e(V) variance-covariance matrix of the estimators

Functions e(sample) marks estimation sample


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