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Full-Text Articles in Economics
Dynamic Panel Modeling Of Climate Change, Peter C.B. Phillips
Dynamic Panel Modeling Of Climate Change, Peter C.B. Phillips
Cowles Foundation Discussion Papers
We discuss some conceptual and practical issues that arise from the presence of global energy balance effects on station level adjustment mechanisms in dynamic panel regressions with climate data. The paper provides asymptotic analyses, observational data computations, and Monte Carlo simulations to assess the use of various estimation methodologies, including standard dynamic panel regression and cointegration techniques that have been used in earlier research. The findings reveal massive bias in system GMM estimation of the dynamic panel regression parameters, which arise from fixed effect heterogeneity across individual station level observations. Difference GMM and Within Group (WG) estimation have little bias …
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.
First Difference Mle And Dynamic Panel Estimation, Chirok Han, Peter C.B. Phillips
First Difference Mle And Dynamic Panel Estimation, Chirok Han, Peter C.B. Phillips
Cowles Foundation Discussion Papers
First difference maximum likelihood (FDML) seems an attractive estimation methodology in dynamic panel data modeling because differencing eliminates fixed effects and, in the case of a unit root, differencing transforms the data to stationarity, thereby addressing both incidental parameter problems and the possible effects of nonstationarity. This paper draws attention to certain pathologies that arise in the use of FDML that have gone unnoticed in the literature and that affect both finite sample peformance and asymptotics. FDML uses the Gaussian likelihood function for first differenced data and parameter estimation is based on the whole domain over which the log-likelihood is …
Indirect Inference For Dynamic Panel Models, Christian Gouriéroux, Peter C.B. Phillips, Jun Yu
Indirect Inference For Dynamic Panel Models, Christian Gouriéroux, Peter C.B. Phillips, Jun Yu
Cowles Foundation Discussion Papers
It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size ( T ) and large cross section sample size ( N ) asymptotics. The estimation bias is particularly relevant in practical applications when T is small and the autoregressive parameter is close to unity. The present paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference (Gouriéroux et al., 1993), shows unbiasedness and analyzes efficiency. The method is implemented in a simple linear dynamic panel …