In this paper, we study the spatial dynamic panel data models with high-order time-varying endogenous weights matrices. To estimate the model, we propose a generalized method of moments (GMM) estimator. Compared to the traditional quasi-maximum likelihood (QML) estimator, GMM estimators can...

A unified approach to regularize maximum likelihood models for linear, multivariate normal, logistic (binomial and polytomous), Poisson, and Cox proportional hazards based on between-predictor correlation is presented. Regularization is accomplished via penalization based on the strength of...

This note looks at estimation of spatial autoregressive models for non-negative and count outcomes with multiplicative fixed effects. We show that in presence of significant proportion of zeros in the count variable, control function and Instrumental Variable estimation to model such spatial...

Covariates in regressions may be linked to each other on a network. Knowledge of the network structure can be incorporated into regularized regression settings via a network penalty term. However, when it is unknown whether the connection signs in the network are positive (connected covariates...

This paper introduces a novel simulation-based filtering method for general state space models. It allows for the computation of time-varying conditional means, quantiles, and modes, but also for the prediction of latent variables in general. The method relies on generating artificial samples of...

Covariates selection is essential when faced with many variables in modern causal inference in a data-rich environment. Particularly, the efficiency of the average causal effect (ACE) can be improved by including covariates only related to the outcome and reduced by including covariates related...

This note looks at estimation of count models with multiplicative fixed effects and spatial dependence in the form of spatial lag autocorrelation. We show that in presence of significant proportion of zeros in the count variable, control function estimation to model such spatial dependence is...

This paper uses administrative tax data to estimate top wealth in the United States. We assemble new data that links people to their sources of capital income and develop new methods to estimate the degree of return heterogeneity within asset classes. Disaggregated fixed income data reveal that...

We revisit the relationship between pre-primary education and achievement test scores in the primary school, by employing a Bayesian Model Averaging (BMA) approach combined with Gini regression estimates. The data are taken from the OECD Programme for International Student Assessment (PISA) for...

This paper extends the multivariate index autoregressive model by Reinsel (1983) to the case of cointegrated time series of order (1, 1). In this new modelling, namely the Vector Error-Correction Index Model (VECIM), the first differences of series are driven by some linear combinations of the...