Liudas Giraitis, George Kapetanios, Yufei Li, Alexia Ventouri
arXiv 10 Nov 2025 · Econometrics · publishedEconometric Theory (2026)
arXiv:2511.07183 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces and analyzes a framework that accommodates general heterogeneity in regression modeling. It demonstrates that regression models with fixed or time-varying parameters can be estimated using the OLS and time-varying OLS methods, respectively, across a broad class of regressors and noise processes not covered by existing theory. The proposed setting facilitates the development of asymptotic theory and the estimation of robust standard errors. The robust confidence interval estimators accommodate substantial heterogeneity in both regressors and noise. The resulting robust standard error estimates coincide with White's (1980) heteroskedasticity-consistent estimator but are applicable to a broader range of conditions, including models with missing data. They are computationally simple and perform well in Monte Carlo simulations. Their robustness, generality, and ease of implementation make them highly suitable for empirical applications. Finally, the paper provides a brief empirical illustration.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | White, H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity | 1.000 | 6 | 3 | 100% |
| 2 | Hall, A., S. Han, and O. Boldea (2012) Inference regarding multiple structural changes in linear models with endogenous regressors | 0.811 | 4 | 2 | 100% |
| 3 | Hu, Z., I. Kasparis, and Q. Wang (2024) Time-varying parameter regressions with stationary presistent data | 0.737 | 3 | 2 | 100% |
| 4 | Boldea, O., A. Cornea-Madeira, and A. Hall (2019) Bootstrapping structural change tests | 0.737 | 3 | 2 | 100% |
| 5 | Karmakar, S., S. Richter, and W. B. Wu (2022) Simultaneous inference for time-varying models | 0.737 | 3 | 2 | 100% |
| 6 | Vogt, M (2012) Nonparametric regression for locally stationary time series | 0.737 | 3 | 2 | 100% |
| 7 | Bardet, J.-M. and O. Wintenberger (2009) Asymptotic normality of the quasi-maximum likelihood estimator for multidimensional causal processes | 0.644 | 2 | 2 | 100% |
| 8 | Doukhan, P. and O. Wintenberger (2008) Weakly dependent chains with infinite memory | 0.644 | 2 | 2 | 100% |
| 9 | Georgiev, I., D. Harvey, S. Leybourn, and A. Robert Taylor (2018) Testing for parameter instability in predictive regression models | 0.644 | 2 | 2 | 100% |
| 10 | Giraitis, L., Y. Li, and P. C. B. Phillips (2024) Robust inference on correlation under general heterogeneity self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 62 scored citations.