Abstract
xsmle is a new command for spatial analysis using Stata. We consider the quasi-maximum likelihood estimation of a wide set of both fixed- and random-
effects spatial models for balanced panel data. Of special note is that xsmle allows to handle unbalanced panels thanks to its full compatibility with the mi suite
of commands, to use spatial weight matrices in the form of both Stata matrices and spmat objects, to compute direct, indirect and total marginal effects and
related standard errors for linear (in variables) specifications, and to exploit a wide range of postestimation features, extending to the panel data case the redictors proposed by Kelejian and Prucha (2007). Moreover, it also allows the use of margins to compute total marginal effects in presence of nonlinear specifications obtained using factor variables. This paper describes the command and all its functionalities using both simulated and real data.
effects spatial models for balanced panel data. Of special note is that xsmle allows to handle unbalanced panels thanks to its full compatibility with the mi suite
of commands, to use spatial weight matrices in the form of both Stata matrices and spmat objects, to compute direct, indirect and total marginal effects and
related standard errors for linear (in variables) specifications, and to exploit a wide range of postestimation features, extending to the panel data case the redictors proposed by Kelejian and Prucha (2007). Moreover, it also allows the use of margins to compute total marginal effects in presence of nonlinear specifications obtained using factor variables. This paper describes the command and all its functionalities using both simulated and real data.
| Original language | English |
|---|---|
| Pages (from-to) | 139-180 |
| Journal | The Stata Journal |
| Volume | 17 |
| Issue number | 1 |
| Publication status | Published - 22 Mar 2017 |
Keywords / Materials (for Non-textual outputs)
- xsml
- spatial analysis
- spatial autocorrelation model
- spatial autoregressive model
- spatial Durbin model
- spatial error model
- generalized spatial panel random-effects model
- panel data
- maximum likelihood estimation