dynamic panel estimation (gmm)

This paper addresses the problem of efficient parameter estimation in high-dimensional panel data models featuring both interactive fixed effects and dynamic terms. Confronted with the efficiency loss of conventional fixed-effect estimators due to the incidental parameters problem, we reformulate the model as a system of simultaneous equations and propose a quasi-maximum likelihood estimator (QMLE). Our key theoretical contribution is the first derivation of a Cramér–Rao-type asymptotic efficiency bound under the joint presence of interactive effects and dynamics; we rigorously establish that the QMLE achieves this bound, thereby attaining semiparametric efficiency. Monte Carlo simulations demonstrate that the QMLE significantly outperforms standard fixed-effect estimators in finite samples. By overcoming the efficiency bottleneck inherent in dynamic panel models with complex high-dimensional structures, our approach provides a new paradigm for interactive-effect modeling—one that combines theoretical optimality with computational feasibility.

This study addresses the challenge of an unknown spatial weight matrix in spatial dynamic panel models when only economic distance measures are available and no prior structural information exists. The authors develop a semi-nonparametric framework that treats the spatial weights—entering the outcome variable, its lags, and the disturbance term—as unknown functions of economic distance. A unified operator-based approach accommodates both spatial autoregressive and matrix exponential specifications while incorporating two-way fixed effects. Innovatively fully nonparametrizing the spatial weights, the paper proposes a sieve GMM estimator that stacks linear and quadratic moment conditions and employs heteroskedasticity-robust inference. Theoretical results establish √{n(T−1)} convergence and asymptotic normality for the parameter estimates, and Monte Carlo simulations confirm strong finite-sample performance. Empirically, the analysis reveals that economic-geographic proximity significantly intensifies the spatial dependence of witchcraft-related murders.

This paper addresses the identification and estimation of dynamic random-coefficient linear models with individual heterogeneity in short panel data. Due to predetermined regressors—such as lagged dependent variables—point identification is infeasible under conventional approaches. We therefore propose a semiparametric identification framework grounded in moment inequalities and distributional constraints, which—novelty—systematically characterizes the non-point-identified sets for the mean, variance, and cumulative distribution function of the random coefficients, accommodating discrete, continuous, and unbounded outcomes. We further develop a computationally tractable estimation and inference procedure, applying it to PSID data. Empirically, we find substantial unobserved heterogeneity in U.S. household income persistence; this heterogeneity constitutes a key structural driver of divergent consumption and saving behaviors across households.

This paper addresses the dual heterogeneity in panel data—cross-sectional latent group structures (homogeneous within groups, heterogeneous across groups) and smoothly evolving time-varying coefficients. We propose a time-varying latent group panel model that jointly captures both features. Methodologically, we innovatively integrate adaptive pairwise grouping fusion Lasso—enabling automatic group identification—with polynomial or B-spline bases to flexibly model coefficient trajectories over time, thereby unifying latent grouping and smooth temporal variation for the first time. Theoretically, we establish asymptotic normality and oracle efficiency for both the penalized and post-selection estimators. Simulation studies demonstrate high grouping accuracy and low estimation bias. Empirical application to global GDP carbon intensity reveals significant cross-country latent grouping and time-varying convergence patterns, confirming the method’s statistical robustness and substantive interpretability.

This paper addresses the challenge of dynamic forecasting and steady-state distribution inference in panel data with cross-sectional heterogeneity in unit-specific coefficients. We propose a dynamic heterogeneous distribution regression framework that jointly estimates individual-level heterogeneous coefficients and their functional targets—including one-step-ahead forecasts, steady-state cross-sectional distributions, and quantile treatment effects. To enable uniform asymptotically valid inference on functional parameters under unknown heterogeneity, we develop a novel cross-sectional bootstrap procedure—the first of its kind for such settings. The method integrates fixed-effects estimation, distribution regression, and quantile treatment effect modeling. Empirical application to PSID data reveals that negative income shocks significantly increase right-skewness in labor income distributions and raise poverty persistence rates, while higher education mitigates these effects; moreover, income mobility exhibits systematic heterogeneity across individuals. Simulation studies confirm the method’s robustness and reliability.

This study addresses the incidental parameter problem arising from unit-specific fixed effects in nonlinear panel data models, particularly when the number of time periods per unit is small and conventional estimators break down. The authors propose a novel projection-based approach that eliminates these incidental parameters without imposing assumptions on the joint distribution of fixed effects and covariates. By constructing an identified set through an implementable correspondence between observables and unobserved heterogeneity, and leveraging random set theory together with moment inequalities, they develop a distribution-free partial identification framework. This framework accommodates both static and dynamic models as well as discrete and continuous outcomes, enabling robust inference even in short panels.

This study addresses efficient estimation and robust inference for semiparametric and nonparametric models with fixed effects in panel data. The authors propose a unified framework based on penalized splines, handling fixed effects via unit indicators, first-differencing, or penalized unit-specific effects, and leverage mgcv::bam for scalable fitting. A novel penalty-adjusted cluster-robust covariance estimator is developed, which remains valid under unknown smoothness and substantially improves the accuracy of finite-dimensional parameter tests and the coverage performance of function-wise confidence bands. Monte Carlo simulations demonstrate that the proposed method excels in function estimation, maintains correct test size, and achieves reliable interval coverage across various scenarios.

This paper addresses the challenge of modeling unobserved heterogeneity in dynamic panel data. We propose a Bayesian structural framework based on the Mixture of Finite Mixtures (MFM) model, which permits an unknown and estimable number of latent groups. To overcome the clustering inconsistency inherent in Dirichlet process priors, our approach employs an MFM prior coupled with an extended telescoping sampler, enabling efficient full Bayesian inference for dynamic panels with covariates. Theoretically, we establish joint parametric-rate convergence of both clustering and structural parameters, supporting posterior contraction and valid credible set construction. Empirically, we uncover previously hidden heterogeneity in the income–democracy relationship—evident only after controlling for additional covariates—thereby demonstrating the method’s small-sample robustness and substantive interpretability.

This study addresses the challenge of conducting valid statistical inference on unit-specific coefficients in panel data exhibiting latent group structure. The authors propose a novel inference framework that first clusters units into a small number of latent groups and then explicitly accounts for uncertainty in group membership. Their approach involves two key components: constructing test statistics based on the minimal value over confidence sets for group assignments, and correcting for bias induced by potential group misclassification while developing standard errors robust to such misclassification. Theoretical analysis and simulation results demonstrate that, compared to conventional unit-by-unit time series methods, the proposed procedure yields substantially narrower confidence sets—particularly for units with high error variance—while maintaining proper size control and coverage accuracy, thereby avoiding inferential distortions caused by ignoring group assignment uncertainty.

Bayesian inference for high-dimensional nonlinear, non-Gaussian panel data remains challenging; conventional Monte Carlo methods—such as particle filters—suffer from the curse of dimensionality and severe particle degeneracy in both model selection and parameter estimation. Method: We propose Marginalized Iterated Filtering (MIF), a novel algorithm integrating sequential Monte Carlo, iterated filtering, and analytical marginalization to explicitly accommodate individual heterogeneity and dynamic coupling structures. MIF avoids high-dimensional particle degeneracy while enabling approximate likelihood gradient computation. Contribution/Results: This work constitutes the first systematic extension of iterated filtering to maximum likelihood and Bayesian inference for non-Gaussian panel data. Empirical results demonstrate that MIF substantially improves parameter estimation accuracy and model comparison reliability, enabling computationally infeasible complex dynamic mechanism modeling. The method advances scalability and practicality of high-dimensional panel inference.