Ingmar Prucha (University of Maryland) – Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential ExogeneitySave the date
This paper considers a class of GMM estimators for general dynamic panel models, allowing for weakly exogenous covariates and cross sectional dependence due to spatial lags, unspeciﬁed common shocks and time-varying interactive eﬀects. We signiﬁcantly expand the scope of the existing literature by allowing for endogenous time varying spatial weight matrices without imposing explicit structural assumptions on how the weights are formed. An important area of application is in social interaction and network models where our speciﬁcation can accommodate data dependent network formation. We consider an exemplary social interaction model and show how identiﬁcation of the interaction parameters is achieved through a combination of linear and quadratic moment conditions. For the general setup we develop an orthogonal forward diﬀerencing transformation to aid in the estimation of factor components while maintaining orthogonality of moment conditions. This is an important ingredient to a tractable asymptotic distribution of our estimators. In general, the asymptotic distribution of our estimators is found to be mixed normal due to random norming. However, the asymptotic distribution of our test statistics is still chi-square.
Friday, December 06, 2019, 11:00 a.m.
Please register until Tuesday, December 03, 2019.