Stata 15 help for spxtregress

[SP] spxtregress -- Spatial autoregressive models for panel data

Syntax

Fixed-effects maximum likelihood

spxtregress depvar [indepvars] [if] [in], fe [fe_options]

Random-effects maximum likelihood

spxtregress depvar [indepvars] [if] [in], re [re_options]

fe_options Description ------------------------------------------------------------------------- Model * fe use fixed-effects estimator dvarlag(spmatname) spatially lagged dependent variable errorlag(spmatname) spatially lagged errors ivarlag(spmatname : varlist) spatially lagged independent variables; repeatable 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

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

Maximization maximize_options control the maximization process; seldom used

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

re_options Description ------------------------------------------------------------------------- Model * re use random-effects estimator dvarlag(spmatname) spatially lagged dependent variable errorlag(spmatname) spatially lagged errors ivarlag(spmatname : varlist) spatially lagged independent variables; repeatable sarpanel alternative formulation of the estimator in which the panel effects follow the same spatial process as the errors noconstant suppress constant term force allow estimation when estimation sample is a subset of the sample used to create the spatial weighting matrix

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

Maximization maximize_options control the maximization process; seldom used

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

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

Menu

Statistics > Spatial autoregressive models

Description

spxtregress fits spatial autoregressive (SAR) models, also known as simultaneous autoregressive models, for panel data. The commands spxtregress, fe and spxtregress, re are extensions of xtreg, fe and xtreg, re for spatial data; see [XT] xtreg.

If you have not read [SP] intro 1 - [SP] intro 8, you should do so before using spxtregress.

To use spxtregress, your data must be Sp data and xtset. 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 spxtregress, fe

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

fe requests the fixed-effects regression estimator.

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.

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.

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.

+-----------+ ----+ 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.

+--------------+ ----+ 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 spxtregress, fe but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Options for spxtregress, re

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

re requests the generalized least-squares random-effects estimator.

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.

sarpanel requests an alternative formulation of the estimator in which the panel effects follow the same spatial process as the errors. By default, the panel effects are included in the estimation equation as an additive term, just as they are in the standard nonspatial random-effects model. When sarpanel and errorlag(spmatname) are specified, the panel effects also have a spatial autoregressive form based on spmatname. If errorlag() is not specified with sarpanel, the estimator is identical to the estimator when sarpanel is not specified. The sarpanel estimator was originally developed by Kapoor, Kelejian, and Prucha (2007); see Methods and formulas.

noconstant; 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 spxtregress, fe.

+-----------+ ----+ 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.

+--------------+ ----+ 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 spxtregress, re but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

Setup . copy http://www.stata-press.com/data/r15/homicide_1960_1990.dta . . copy http://www.stata-press.com/data/r15/homicide_1960_1990_shp.dta . . use homicide_1960_1990 . xtset _ID year . spset

Create a contiguity weighting matrix with the default spectral normalization . spmatrix create contiguity W if year == 1990

Fit a spatial autoregressive random-effects model . spxtregress hrate ln_population ln_pdensity gini i.year, re dvarlag(W)

Create an inverse-distance weighting matrix with the default spectral normalization . spmatrix create idistance M if year == 1990

Same as above but use the alternative formulation of the estimator . spxtregress hrate ln_population ln_pdensity gini i.year, re sarpanel dvarlag(M) errorlag(M)

Fit a spatial autoregressive fixed-effects model . spxtregress hrate ln_population ln_pdensity gini i.year, fe dvarlag(M) errorlag(M)

Stored results

spxtregress, fe and spxtregress, re store the following in e():

Scalars e(N) number of observations e(N_g) number of groups (panels) e(g) group size 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) spxtregress e(cmdline) command as typed e(depvar) name of dependent variable e(indeps) names of independent variables e(idvar) name of ID variable e(model) fe, re, or re sarpanel e(title) title in estimation output e(constant) hasconstant or noconstant (re only) e(dlmat) name of spatial weighting matrix applied to depvar e(elmat) name of spatial weighting matrix applied to errors e(chi2type) Wald; type of model chi-squared test e(vce) oim 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(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector 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

Reference

Kapoor, M., H. H. Kelejian, and I. R. Prucha. 2007. Panel data models with spatially correlated error components. Journal of Econometrics 140: 97-130.


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