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Please cite this article as: Sarafidis, V., Yamagata, T., Robertson, D., A test of cross section dependence for a linear dynamic panel model with regressors. Journal of Econometrics (2008Econometrics ( ), doi:10.1016Econometrics ( /j.jeconom.2008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract This paper proposes a new testing procedure for detecting error cross section dependence after estimating a linear dynamic panel data model with regressors using the generalised method of moments (GMM). The test is valid when the crosssectional dimension of the panel is large relative to the time series dimension. Importantly, our approach allows one to examine whether any error cross section dependence remains after including time dummies (or after transforming the data in terms of deviations from time-speci…c averages), which will be the case under heterogeneous error cross section dependence. Finite sample simulation-based results suggest that our tests perform well, particularly the version based on the Blundell and Bond (1998) system GMM estimator. In addition, it is shown that the system GMM estimator, based only on partial instruments consisting of the regressors, can be a reliable alternative to the standard GMM estimators under heterogeneous error cross section dependence. The proposed tests are applied to employment equations using UK …rm data and the results show little evidence of heterogeneous error cross section dependence. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT

We study the biases that are likely to arise in practice with panel data when parameters vary across individuals, but this is not allowed for in estimation. We consider both stationary and non‐stationary regressors. We find that biases can be severe for relatively small parameter variation, and that this problem is hard to detect. We study in some detail by Monte‐Carlo the performance of the Anderson‐Hsiao estimator in the presence of this particular mis‐specification.

This paper explores the impact of error cross-sectional dependence (modelled as a factor structure) on a number of widely used IV and generalized method of moments (GMM) estimators in the context of a linear dynamic panel data model. It is shown that, under such circumstances, the standard moment conditions used by these estimators are invalid -- a result that holds for any lag length of the instruments used. Transforming the data in terms of deviations from time-specific averages helps to reduce the asymptotic bias of the estimators, unless the factor loadings have mean zero. The finite sample behaviour of IV and GMM estimators is investigated by means of Monte Carlo experiments. The results suggest that the bias of these estimators can be severe to the extent that the standard fixed effects estimator is not generally inferior anymore in terms of root median square error. Time-specific demeaning alleviates the problem, although the effectiveness of this transformation decreases when the variance of the factor loadings is large. Copyright The Author(s). Journal compilation Royal Economic Society 2008

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This paper proposes a new instrumental variables approach for consistent and asymptotically ecient estimation of panel data models with weakly exogenous or endogenous regressors and residuals generated by a multi-factor error structure. In this case, the standard dynamic panel estimators fail to provide consistent estimates of the parameters. The novelty of our approach is that we introduce new parameters to represent the unobserved covariances between the instruments and the factor component of the residual; these parameters are estimable when N is large. Some important estimation and identication issues are studied in detail. The nite sample performance of the proposed estimators is investigated using simulated data. The results show that the method produces reliable estimates of the parameters over several parametrisations.

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This paper estimates an educational production function. Educational attainment is a function of peer group, parental input and schooling. Conventional measures of school quality are not good predictors for academic attainment, once we control for peer group effects; parental qualities also have strong effects on academic attainment. This academic attainment is a then a key determinant of subsequent labour market success, as measured by earnings. The main methodological innovation in this paper is the nomination of a set of instruments, very broad regions of birth, which, as a whole, pass close scrutiny for validity and permit unbiased estimation of the production function.