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Windmeijer, Frank - Centre for Microdata Methods and Practice (CEMMAP) - 2006

This paper gives an account of the recent literature on estimating models for panel count data. Specifically, the …

Persistent link: https://www.econbiz.de/10005509534

The weak instrument problem of the system GMM estimator in dynamic panel data models

Bun, Maurice; Windmeijer, Frank - Centre for Microdata Methods and Practice (CEMMAP) - 2007

<p><p><p>The system GMM estimator for dynamic panel data models combines moment conditions for the model in first … differenced model in terms of bias and root mean squared error. However, we show in this paper that in the covariance stationary … panel data AR(1) model the expected values of the concentration parameters in the differenced and levels equations for the …

Persistent link: https://www.econbiz.de/10005509554

ExpEnd, A Gauss programme for non-linear GMM estimation of exponential models with endogenous regressors for cross section and panel data

Windmeijer, Frank - Centre for Microdata Methods and Practice (CEMMAP) - 2002

endogenous regressors for cross section and panel data. The estimators included in this package are simple Poisson pseudo ML; GMM … Poisson for panel data; GMM estimation using quasi-differenced moment conditions eliminating unobserved heterogeneity and …

Persistent link: https://www.econbiz.de/10005811450

Inverse probability weighted M-estimators for sample selection, attrition and stratification

Wooldridge, Jeffrey M. - Centre for Microdata Methods and Practice (CEMMAP) - 2002

I provide an overview of inverse probability weighted (IPW) M-estimators for cross section and two-period panel data …

Persistent link: https://www.econbiz.de/10005811461

GMM with many weak moment conditions

Newey, Whitney; Windmeijer, Frank - Centre for Microdata Methods and Practice (CEMMAP) - 2005

biased. Generalized empirical likelihood (GEL) has smaller bias but the usual standard errors are too small. In this paper we …

Persistent link: https://www.econbiz.de/10005727673

Inverse probability weighted estimation for general missing data problems

Wooldridge, Jeffrey M. - Centre for Microdata Methods and Practice (CEMMAP) - 2004

I study inverse probability weighted M-estimation under a general missing data scheme. The cases covered that do not previously appear in the literature include M-estimation with missing data due to a censored survival time, propensity score estimation of the average treatment effect for linear...

Persistent link: https://www.econbiz.de/10005727682

Projection estimators for autoregressive panel data models

Bond, Steve; Windmeijer, Frank - Centre for Microdata Methods and Practice (CEMMAP) - 2001

In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the …

Persistent link: https://www.econbiz.de/10005727705

Local identification of nonparametric and semiparametric models

Chen, Xiaohong; Chernozhukov, Victor; Sokbae 'Simon' Lee; … - Centre for Microdata Methods and Practice (CEMMAP) - 2012

In parametric models a sufficient condition for local idenfication is that the vector of moment is differentiable at the true parameter with full rank derivative matrix. This paper shows that additional conditions are often needed in nonlinear, nonparametric models to avoid nonlinearities...

Persistent link: https://www.econbiz.de/10010593707

What do instrumental variable models deliver with discrete dependent variables?

Chesher, Andrew; Rosen, Adam - Centre for Microdata Methods and Practice (CEMMAP) - 2013

We study models with discrete endogenous variables and compare the use of two stage least squares (2SLS) in a linear probability model with bounds analysis using a nonparametric instrumental variable model. 2SLS has the advantage of providing an easy to compute point estimator of a slope...

Persistent link: https://www.econbiz.de/10010827566

An instrumental variable random coefficients model for binary outcomes

Chesher, Andrew; Rosen, Adam - Centre for Microdata Methods and Practice (CEMMAP) - 2012

In this paper we study a random coefficient model for a binary outcome. We allow for the possibility that some or even all of the regressors are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are...

Persistent link: https://www.econbiz.de/10010593710