Abstracts » Economics

Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models

Article Abstract:

This paper develops consistent model and moment selection criteria for GMM estimation. The criteria select the correct model specification and all correct moment conditions asymptotically. The selection criteria resemble the widely used likelihood-based selection criteria BIC, HQIC, and AIC. (The latter is not consistent.) The GMM selection criteria are based on the J statistic for testing over-identifying restrictions. Bonus terms reward the use of fewer parameters for a given number of moment conditions and the use of more moment conditions for a given number of parameters. The paper also considers a consistent downward testing procedure. The paper applies the model and moment selection criteria to dynamic panel data models with unobserved individual effects. The paper shows how to apply the selection criteria to select the lag length for lagged dependent variables, to detect the number and locations of structural breaks, to determine the exogeneity of regressors, and/or to determine the existence of correlation between some regressors and the individual effect. To illustrate the finite sample performance of the selection criteria and the testing procedures and their impact

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GMM estimation of linear panel data models with time-varying individual effects

Article Abstract:

This paper considers models for panel data in which the individual effects vary over time. The temporal pattern of variation is arbitrary, but it is the same for all individuals. The model thus allows one to control for time-varying unobservables that are faced by all individuals (e.g., macro-economic events) and to which individuals may respond differently. A generalized within estimator is consistent under strong assumptions on the errors, but it is dominated by a generalized method of moments estimator. This is perhaps surprising, because the generalized within estimator is the MLE under normality. The efficiency gains from imposing second-moment error assumptions are evaluated; they are substantial when the regressors and effects are weakly correlated. [C] 2001 Elsevier Science S.A. All rights reserved. JEL classification: C23 Keywords: Panel data; Time-varying effects; Generalized method of moments; MLE

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Nested random effects estimation in unbalanced panel data

Article Abstract:

Panel data in many econometric applications exhibit a nested (hierarchical) structure. For example, data on firms may be grouped by industry, or data on air pollution may be grouped by observation station within a city, city within a country, and by country. In these cases, one can control for unobserved group and sub-group effects using a nested-error component model. A double-nested unbalanced panel is examined and a corresponding maximum likelihood estimator is derived. A generalization to even higher-order nesting is feasible. A practical example and a Monte-Carlo simulation compare the new estimator against the non-nested ML estimator. The style of presentation is intended to aid applied econometricians in implementing the new ML estimator. [C] 2001 Elsevier Science S.A. All rights reserved. JEL classification: C13; C23 Keywords: Panel data; Nested effects; Error component model; Econometrics

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