A Moment Ratio Estimator is proposed for the parameters of an Autoregressive (AR) model of the error in an Ordinary Least Squares (OLS) linear regression. Although it is computed from the conventional residual autocorrelation coefficients, it greatly reduces their bias, and provides corrected standard errors with far less bias than alternatives. The estimator is in the spirit of the Median Unbi...

This paper derives Lagrange multiplier tests based on artificial double length regressions (DLR) to jointly test for differenced linear or loglinear models with no heteroskedasticity or autocorrelation against a more general differenced Box–Cox model with heteroskedasticity or autocorrelation. These tests are easy to implement and are illustrated using an empirical example.  2000 Elsevier Scie...

Motivated by the first differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogeous regressors. The estimators utilize the additive structure of the first-differenced model, the fact that the two additive components have the same functional form, and the unknown function of inte...

We study an estimator of the mean function of a counting process based on panel count data which has been proposed by Sun and Kalb eisch The setting for panel count data is one in which n independent subjects each with a counting process with common mean function are observed at several possibly di erent times during a study Following a model proposed by Schick and Yu we allow the number of obs...

In this paper it is shown that using the maximum likelihood ML prin ciple for the estimation of multivariate probit models leads to consistent and normally distributed pseudo maximum likelihood regression parame ter estimators PML estimators even if the true correlation structure of the responses is misspeci ed As a consequence e g the PML estimator of the random e ects probit model may be used...

This paper develops an adaptive elastic-net GMM estimator with many possibly invalid moment conditions. We allow for the number of structural parameters (p0) as well as the number of moment conditions increasing with the sample size (n). The new estimator conducts simultaneous model and moment selection. We estimate the structural parameters along with parameters associated with the invalid mom...

Empirical researchers frequently use dynamic panel data models and employ Generalized Method of Moments (GMM) estimators (Hansen, 1982) to estimate model parameters. An important practical problem in the estimation of a dynamic panel data model is the choice of moments as it provides a large number of moment conditions. Even though adding moment conditions leads to efficiency gain according to ...

We are interested in making inference on the AR parameter in an AR(1) panel data model when the time-series dimension is …xed and the cross-section dimension is large. We consider a GMM estimator based on the second order moments of the observed variables. It turns out that when the AR parameter equals unity and certain restrictions apply to the other parameters in the model then local identi…c...

The system GMM estimator developed by Blundell and Bond (1998) for dynamic panel data models has been widely used in empirical work; however, it does not perform well with weak instruments. This paper proposes a variation on the system GMM estimator, based on a simple transformation of the dependent variable. Simulation results indicate that, in finite samples, this transformed system GMM estim...