Institution: Peking University
Primary Field: Econometrics (weighted toward more recent publications)
Homepage: https://feng.li/
Scores use coauthorship adjustment: α/n credit per paper, where n = number of authors. α = 2.02: calibrated so average adjusted count equals average raw count (a zero-sum adjustment).
| Period | S (4x) | A (2x) | B (1x) | C (½x) | Total | Percentile |
|---|---|---|---|---|---|---|
| Last 5 Years | 0.00 | 0.67 | 3.39 | 0.00 | 4.06 | 78% |
| Last 10 Years | 0.00 | 0.67 | 4.40 | 0.00 | 5.07 | 72% |
| All Time | 0.00 | 0.67 | 4.40 | 0.00 | 5.07 | 82% |
| Year | Article | Journal | Tier | Authors |
|---|---|---|---|---|
| 2023 | Distributed ARIMA models for ultra-long time series | International Journal of Forecasting | B | 4 |
| 2023 | Bayesian forecast combination using time-varying features | International Journal of Forecasting | B | 3 |
| 2023 | Forecast combinations: An over 50-year review | International Journal of Forecasting | B | 4 |
| 2022 | Forecasting: theory and practice | International Journal of Forecasting | B | 80 |
| 2022 | FFORMPP: Feature-based forecast model performance prediction | International Journal of Forecasting | B | 3 |
| 2022 | Hierarchical forecasting with a top-down alignment of independent-level forecasts | International Journal of Forecasting | B | 2 |
| 2022 | A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating | Journal of Business & Economic Statistics | A | 6 |
| 2018 | Improving forecasting performance using covariate-dependent copula models | International Journal of Forecasting | B | 2 |