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Full-Text Articles in Economics
Dynamic Panel Gmm With Near Unity, Peter C.B. Phillips
Dynamic Panel Gmm With Near Unity, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Limit theory is developed for the dynamic panel GMM estimator in the presence of an autoregressive root near unity. In the unit root case, Anderson-Hsiao lagged variable instruments satisfy orthogonality conditions but are well-known to be irrelevant. For a fixed time series sample size (T) GMM is inconsistent and approaches a shifted Cauchy-distributed random variate as the cross section sample size n → ∞. But when T → ∞, either for fixed n or as n → ∞, GMM is √ T consistent and its limit distribution is a ratio of random variables that converges to twice a standard Cauchy …
Identifying Latent Structures In Panel Data, Liangjun Su, Zhentao Shi, Peter C.B. Phillips
Identifying Latent Structures In Panel Data, Liangjun Su, Zhentao Shi, Peter C.B. Phillips
Cowles Foundation Discussion Papers
This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized regression techniques. We focus on linear models where the slope parameters are heterogeneous across groups but homogenous within a group and the group membership is unknown. Two approaches are considered — penalized least squares (PLS) for models without endogenous regressors, and penalized GMM (PGMM) for models with endogeneity. In both cases we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. C-Lasso achieves simultaneous classification and consistent estimation in a single …
True Limit Distributions Of The Anderson-Hsiao Iv Estimators In Panel Autoregression, Peter C.B. Phillips, Chirok Han
True Limit Distributions Of The Anderson-Hsiao Iv Estimators In Panel Autoregression, Peter C.B. Phillips, Chirok Han
Cowles Foundation Discussion Papers
This note derives the correct limit distributions of the Anderson Hsiao (1981) levels and differences instrumental variable estimators, provides comparisons showing that the levels IV estimator has uniformly smaller variance asymptotically as the cross section ( n ) and time series ( T ) sample sizes tend to infinity, and compares these results with those of the first difference least squares (FDLS) estimator.