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Adaptive estimation of heteroskedastic functional-coefficient regressions with an application to fiscal policy evaluation on asset markets

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  • Yundong Tu
  • Ying Wang

Abstract

This article studies the adaptive estimation of the heteroskedastic functional-coefficient regressions. The motivation for such a theoretical study originates from the empirical analysis of Jansen et al., where the role of fiscal policy on the U.S. asset markets (treasury bonds) is evaluated via the functional-coefficient model. It is found that this model is subject to time-varying heteroskedasticity. As a result, the local least square (LLS) estimator suffers from efficiency loss. To overcome this problem, we propose an adaptive LLS (ALLS) estimator, which can adapt to heteroskedasticity of unknown form asymptotically. Simulation studies confirm that the ALLS estimator can achieve significant efficiency gain in finite samples, compared to the LLS estimator. Real data analysis reveals that the heteroskedastic functional-coefficient model provides adequate fit and better out-of-sample forecasting.

Suggested Citation

  • Yundong Tu & Ying Wang, 2020. "Adaptive estimation of heteroskedastic functional-coefficient regressions with an application to fiscal policy evaluation on asset markets," Econometric Reviews, Taylor & Francis Journals, vol. 39(3), pages 299-318, March.
    • Handle: RePEc:taf:emetrv:v:39:y:2020:i:3:p:299-318
    DOI: 10.1080/07474938.2019.1624402
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    Cited by:

    1. Ying Wang & Peter C. B. Phillips & Yundong Tu, 2024. "Limit Theory and Inference in Non-cointegrated Functional Coefficient Regression," Cowles Foundation Discussion Papers 2399, Cowles Foundation for Research in Economics, Yale University.
    2. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," Journal of Econometrics, Elsevier, vol. 216(1), pages 175-191.
    3. Michael Anthony Adams, 2020. "Fiscal Policy and Stock Market Efficiency in the USA: An ARDL Bounds Testing Approach," Journal of Accounting, Business and Finance Research, Scientific Publishing Institute, vol. 9(2), pages 73-81.
    4. Lin, Yingqian & Tu, Yundong & Yao, Qiwei, 2020. "Estimation for double-nonlinear cointegration," LSE Research Online Documents on Economics 103830, London School of Economics and Political Science, LSE Library.
    5. Qiying Wang & Peter C. B. Phillips & Ying Wang, 2023. "New asymptotics applied to functional coefficient regression and climate sensitivity analysis," Cowles Foundation Discussion Papers 2365, Cowles Foundation for Research in Economics, Yale University.
    6. Yuying Sun & Shaoxin Hong & Zongwu Cai, 2023. "Optimal Local Model Averaging for Divergent-Dimensional Functional-Coefficient Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202309, University of Kansas, Department of Economics, revised Sep 2023.
    7. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.

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