We propose parametric and semiparametric IV estimators for spatial autoregressive models with network data where the network structure is endogenous. We embed a dyadic network formation process in the control function approach as in Heckman and Robb (1985). In the semiparametric case, we use power series to approximate the correction terms. We establish the consistency and asymptotic normality for both parametric and semiparametric cases. We also investigate their finite sample properties via Monte Carlo simulation
Arduini, T., Patacchini Eleonora, Rainone, E. (2015). Parametric and Semiparametric IV Estimation of Network Models with Selectivity. Roma : EIEF.
Parametric and Semiparametric IV Estimation of Network Models with Selectivity
ARDUINI, TIZIANO;
2015
Abstract
We propose parametric and semiparametric IV estimators for spatial autoregressive models with network data where the network structure is endogenous. We embed a dyadic network formation process in the control function approach as in Heckman and Robb (1985). In the semiparametric case, we use power series to approximate the correction terms. We establish the consistency and asymptotic normality for both parametric and semiparametric cases. We also investigate their finite sample properties via Monte Carlo simulationI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
