Feasible ML Estimator for Spatial (Dynamic) Panel Data Probit Models with Fixed Effects and Large Datasets

Relatore: (Anna Gloria Billé - Università di Bologna)

  • Data: 23 febbraio 2023 dalle 16:00 alle 17:00

  • Luogo: Aula 4, Piazza Scaravilli

In this paper we propose three different concentrated partial maximum likelihood estimators (CPMLE) for a new model specification, namely spatial dynamic panel data probit (SDPD-probit) model, which allows to deal with cross-sectional dependence, time dependence and individual (spatial) and/or time fixed effects in a nonlinear setting. The first ML-based estimator is a panel version of the bivariate PMLE proposed by Wang et al. (2013) and Billé and Leorato (2020), the second one is the same estimator based on univariate probabilites. We adjust the MLE and concentrated out the fixed effects following Carro (2007) and Fernandez-Val (2009).
Proper marginal effects for this new model specification are also defined. We provide extensive Monte Carlo simulations for the finite sample properties of those estimators, as well as their asymptotic behaviour relying on the increasing domain case and under the assumption of near-epoch dependence. Finally, the last one is a feasible version of PMLE which make use of the coding technique, see Besag (1974) and Arbia (2014), and a block-diagonal structure of the variance-covariance matrix which can be used when we deal with very large datasets.

Luca Trapin