In the standard generalized method of moments estimation of dynamic panel data models, the constant term is usually omitted from instrument sets. As a result, adding a constant to the dependent variable affects the estimates for models without full period dummies. Omitting the constant term from...

This work considers the estimation of a network model with sampled networks. Chandrasekhar and Lewis (2011) show that the estimation with sampled networks could be biased due to measurement error induced by sampling and propose a bias correction by restricting the estimation to sampled nodes to...

This paper focuses on the effects of global factors on the saving–investment relationship. We prove that, if investments and savings are affected by idiosyncratic and global components, they must be cointegrated to obtain reliable estimates of the saving-retention coefficient. When global...

This note presents a new solution to the classic problem with using hedonic price functions to recover demand curves. Unexpected changes in the composition of a differentiated product can generate instruments that support a simple reduced-form approach to demand estimation.

This paper shows that instrumental variables estimators currently in use, require strong but neglected auxiliary assumptions to be consistent in situations with partially missing instruments. We introduce an alternative instrumental variables estimator that does not require auxiliary assumptions.

We propose an alternative bivariate zero-inflated negative binomial (BZINB) regression model based on a copula. The empirical result shows that the proposed model performs better than the existing BZINB models in terms of the maximum log-likelihood and the AIC.

We propose a HAC estimator for the covariance matrix of the fixed effects estimator in a panel data model with unobserved fixed effects and errors that are both serially and spatially correlated.

This paper discusses the copula-based approach of a bivariate binary choice model. We derive the marginal effects of explanatory variables on an outcome of interest (both direct and indirect) in the model. We also show that the signs of the marginal effects are determined by the signs of the...

This paper proposes an ℓ1 penalized quantile regression estimator which adapts the Hausman–Taylor instrumental variable approach in order to address the bias resulting from the shrinkage of the individual effects.

This note discusses partial identification in a nonparametric triangular system with discrete endogenous regressors and nonseparable errors. Recently, Jun et al. (2011, JPX) provide bounds on the structural function evaluated at particular values using exclusion, exogeneity and rank conditions....