In this note we revisit the use of exclusion restrictions in the semiparametric binary choice panel data model introduced in Honore and Lewbel (2002). We show that in a dynamic panel data setting (where one of the pre-determined explanatory variables is the lagged dependent variable), the exclusion restriction in Honore and Lewbel (2002) implicitly requires serial independence condition on an o...

This paper deals with asymptotically efficient estimation in exchangeable nonlinear dynamic panel models with common unobservable factor. These models are especially relevant for applications to large portfolios of credits, corporate bonds, or life insurance contracts, and are recommended in the current regulation in Finance (Basel II and Basel III) and Insurance (Solvency II). The specificatio...

Previous research on dynamic panel estimation has focused on panels that, unlike a typical panel of macroeconomic data, have small time dimensions and large individual dimensions. We use a Monte Carlo approach to investigate the performance of several different methods designed to reduce the bias of the estimated coefficients for the longer, narrower panels commonly found for macro data. We fin...

Identification of dynamic nonlinear panel data models is an important and delicate problem in econometrics. In this paper we provide insights that shed light on the identification of parameters of some commonly used models. Using this insight, we are able to show through simple calculations that point identification often fails in these models. On the other hand, these calculations also suggest...

This paper considers nonparametric estimation of autoregressive panel data models with fixed effects. A within-group type series estimator is developed and its convergence rate and asymptotic normality are derived. It is found that the series estimator is asymptotically biased and the bias could reduce the mean-square convergence rate compared with the cross section cases. A bias corrected nonp...

The two-step GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) for dynamic panel data models have been widely used in empirical work; however, neither of them performs well in small samples with weak instruments. The continuous-updating GMM estimator proposed by Hansen, Heaton, and Yaron (1996) is in principle able to reduce the small-sample bias, but it involves high-dime...

This paper considers bandwidth selection for spectral density estimators based on panel data sets. The spectral densities of greatest interest in this paper are the ones that appear in the bias expression for …xed e¤ects estimators in nonlinear dynamic panel models obtained by Hahn and Kuersteiner. The bias estimation problem is di¤erent from the usual HAC estimation problem because the need fo...

in this paper, we have calculated the environmental efficiency for india, turkey, egypt and iran. the results show that iran’s environmental efficiency has experienced a decreasing trend from 1991 to 2007and india, turkey, egypt and iran have the highest environmental efficiency, respectively. we also have investigated the environmental kuznets curve for this period using data envelopment analy...

We define a combined DEA score to evaluate efficiency in agricultural research. The production model we propose considers efficiency measurements under variable returns to scale for each year in the period 2012–2017. We postulate a first-order autoregressive process in the presence of covariates, to explain efficiency. Powers of the autocorrelation coefficient estimated assuming a dynamic panel...

Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent work. Dynamic panel data models have become increasingly popular in macroeconomics to study common relationships across countries or regions. This paper estimates dynamic panel data models with stochastic volatility by maximizing an approximate likelihood obtained via Rao-Blackw...