Yundong Tu
Biographic Data
| ID | 8920872 |
|---|---|
| NAME | Yundong Tu |
| GIVEN NAMES | Yundong |
| FAMILY NAME | Tu |
| SIGNATURE | TU Y |
| AFFILIATIONS | Peking University |
| ORCID | 0000-0002-2556-3614 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Regime-Specific Return Predictability in Quantiles
This paper captures the underlying regime switching mechanism in the prediction of stock returns via a predictive quantile regression with multiple thresholds. The predictability of each predictor is allowed to switch from one regime to another according to the value of a threshold variable and could vary across quantiles, and the predictors could possess heterogeneous degrees of persistence. Statistical learning techniques, including a sequentia…
Threshold Expectile Regressions With an Unknown Threshold for Dependent Data
This article introduces a threshold expectile regression model with an unknown threshold for dependent data, which enables simple characterization of nonlinearity and heteroscedasticity in economic and financial applications. Profile estimation is proposed for the unknown parameters, and a sup‐Wald test is developed to test the existence of the threshold effect at a fixed expectile level. Inference issues across multiple expectile levels are furt…
Variable Screening and Model Averaging for Expectile Regressions
Expectile regression is a useful tool in modelling data with heterogeneous conditional distributions. This paper introduces two new concepts, i.e. the expectile correlation and expectile partial correlation, which can measure the contribution from each regressor to the response in expectile regression. In ultra‐high dimensional setting, the expectile partial correlation, which provides an importance ranking of the predictors, is found useful for …
Estimating Spatial Autocorrelation With Sampled Network Data
Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation requires the whole network to be observed. This is practically infeasible if network size is huge (e.g., Facebook, Twitter, Weibo, WeChat, etc.). In that case, one has to rely on sampled network data to infer about spatial autocorrelation. By doing so,…
Forecasting Equity Premium
The equity premium, return on equity minus return on risk-free asset, is expected to be positive. We consider imposing such positivity constraint in local historical average (LHA) in nonparametric kernel regression framework. It is also extended to the semiparametric single index model when multiple predictors are used. We construct the constrained LHA estimator via an indicator function which operates as “model-selection” between the unconstrain…
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Forecasting Equity Premium
The equity premium, return on equity minus return on risk-free asset, is expected to be positive. We consider imposing such positivity constraint in local historical average (LHA) in nonparametric kernel regression framework. It is also extended to the semiparametric single index model when multiple predictors are used. We construct the constrained LHA estimator via an indicator function which operates as “model-selection” between the unconstrain…
Estimating Spatial Autocorrelation With Sampled Network Data
Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation requires the whole network to be observed. This is practically infeasible if network size is huge (e.g., Facebook, Twitter, Weibo, WeChat, etc.). In that case, one has to rely on sampled network data to infer about spatial autocorrelation. By doing so,…
Variable Screening and Model Averaging for Expectile Regressions
Expectile regression is a useful tool in modelling data with heterogeneous conditional distributions. This paper introduces two new concepts, i.e. the expectile correlation and expectile partial correlation, which can measure the contribution from each regressor to the response in expectile regression. In ultra‐high dimensional setting, the expectile partial correlation, which provides an importance ranking of the predictors, is found useful for …
Threshold Expectile Regressions With an Unknown Threshold for Dependent Data
This article introduces a threshold expectile regression model with an unknown threshold for dependent data, which enables simple characterization of nonlinearity and heteroscedasticity in economic and financial applications. Profile estimation is proposed for the unknown parameters, and a sup‐Wald test is developed to test the existence of the threshold effect at a fixed expectile level. Inference issues across multiple expectile levels are furt…
Regime-Specific Return Predictability in Quantiles
This paper captures the underlying regime switching mechanism in the prediction of stock returns via a predictive quantile regression with multiple thresholds. The predictability of each predictor is allowed to switch from one regime to another according to the value of a threshold variable and could vary across quantiles, and the predictors could possess heterogeneous degrees of persistence. Statistical learning techniques, including a sequentia…
Mathematics (4 works) · Statistics (4 works) · Econometrics (3 works) · Estimator (3 works) · Financial Risk and Volatility Modeling (3 works) · Statistical Methods and Inference (3 works) · Computer Science (2 works) · Economics (2 works) · Regression (2 works) · Advanced Statistical Methods and Models (1 work)