Chaohua Dong, Jiti Gao, Bin Peng, Yundong Tu
arXiv 3 Nov 2021 · Econometrics
arXiv:2111.02023 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a class of parametric multiple-index time series models that involve linear combinations of time trends, stationary variables and unit root processes as regressors. The inclusion of the three different types of time series, along with the use of a multiple-index structure for these variables to circumvent the curse of dimensionality, is due to both theoretical and practical considerations. The M-type estimators (including OLS, LAD, Huber's estimator, quantile and expectile estimators, etc.) for the index vectors are proposed, and their asymptotic properties are established, with the aid of the generalized function approach to accommodate a wide class of loss functions that may not be necessarily differentiable at every point. The proposed multiple-index model is then applied to study the stock return predictability, which reveals strong nonlinear predictability under various loss measures. Monte Carlo simulations are also included to evaluate the finite-sample performance of the proposed estimators.
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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 | Dong, C., Gao, J., and Tjstheim, D (2016) Estimation for single-index and partially linear single-index integrated models self | 0.961 | 9 | 4 | 89% |
| 2 | Park, J. Y. and Phillips, P. C. B (2001) Nonlinear regression with integreted time series | 0.956 | 8 | 3 | 88% |
| 3 | Phillips, P. C. B (1995) Robust nonstationary regression | 0.894 | 7 | 5 | 71% |
| 4 | Koenker, R. and Bassett, G (1978) Regression quantiles | 0.737 | 3 | 2 | 100% |
| 5 | Xiao, Z (2009) Quantile cointegrating regression | 0.737 | 3 | 2 | 100% |
| 6 | Kanwal, R. P (1983) Generalized Fuctions: Theory and Technique | 0.721 | 8 | 3 | 38% |
| 7 | Tu, Y., Liang, H. Y., and Wang, Q (2021) Nonparametric inference for quantile cointegrations with stationary covariates self | 0.644 | 4 | 1 | 100% |
| 8 | Phillips, P. C. B (1991) A shortcut to LAD estimator asymptotics | 0.644 | 2 | 2 | 100% |
| 9 | Kasparis, I., Andreou, E., and Phillips, P (2015) Nonparametric predictive regression | 0.585 | 3 | 1 | 100% |
| 10 | Lee, J. H (2016) Predictive quantile regression with persistent covariates: Ivx-qr approach | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 47 scored citations.