dataset · Figshare · 2025

Unified Inference for Panel Autoregressive Models With Unobserved Grouped Heterogeneity*

Huang, Wenxin, Su, Liangjun, Wang, Yiru

Cited by 0Open access

Abstract

This paper considers unified estimation and inference in panel autoregressive (PAR) models. The PAR coefficients are assumed to contain a latent group structure that allows the degree of persistence for each time series to be heterogeneous and unknown. We propose a novel penalized weighted least squares approach to simultaneously identify the unknown group membership and consistently estimate the PAR coefficients, regardless of whether the underlying PAR process is stationary, unit-root, near-integrated, or even explosive. Theoretically, we establish the classification consistency, oracle properties, and unified asymptotic normal distributions for the proposed Lasso-type estimators. Empirically, we apply our data-driven method to uncover the existence of firm-level hidden bubbles in the U.S. stock market that have not been accounted for in previous studies.

Cite this paper

APA 7

Huang, W., Su, L., & Wang, Y. (2025). Unified Inference for Panel Autoregressive Models With Unobserved Grouped Heterogeneity. Figshare*. https://doi.org/10.6084/m9.figshare.29103509

MLA 9

Huang, Wenxin, et al. “Unified Inference for Panel Autoregressive Models With Unobserved Grouped Heterogeneity.” Figshare*, 2025, https://doi.org/10.6084/m9.figshare.29103509.

Chicago

Huang, Wenxin, Liangjun Su, and Yiru Wang. 2025. “Unified Inference for Panel Autoregressive Models With Unobserved Grouped Heterogeneity.” Figshare*. https://doi.org/10.6084/m9.figshare.29103509.

Harvard

Huang, W., Su, L. and Wang, Y. (2025) ‘Unified Inference for Panel Autoregressive Models With Unobserved Grouped Heterogeneity’, Figshare*. Available at: https://doi.org/10.6084/m9.figshare.29103509.

BibTeX

@misc{huang2025unified,
  title = {{Unified Inference for Panel Autoregressive Models With Unobserved Grouped Heterogeneity*}},
  author = {Huang, Wenxin and Su, Liangjun and Wang, Yiru},
  journal = {Figshare},
  year = {2025},
  doi = {10.6084/m9.figshare.29103509}
}

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