Model averaging by jackknife criterion in models with dependent data

By Xinyu Zhang, Alan T. K. Wan, Guohua Zou. 2013

164 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e91baed160b79faf5f42813a409349203e71b55c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Xun Lu
2. Liangjun Su
3. Giuseppe De Luca
4. J. R. Magnus
5. Xinyu Zhang
6. Yan Gao
7. Guohua Zou
8. Shouyang Wang

In Finance

1. Xun Lu
2. Liangjun Su
3. J. R. Magnus
4. Giuseppe De Luca
5. Xinyu Zhang
6. Guohua Zou
7. Shouyang Wang
8. Halbert White

In Statistics and Probability

1. Giuseppe De Luca
2. J. R. Magnus
3. Guohua Zou
4. Xinyu Zhang
5. Xun Lu
6. Yan Gao
7. Shouyang Wang
8. Liangjun Su

Citations in the corpus

• Weighted‐Average Least Squares (WALS): A Survey (2016, methodology): “For linear models with nonsperical disturbances, Hansen and Racine (2012) and Zhang et al. (2013b) developed jackknife model-average estimators which are asymptotically optimal under heteroskedastic and serially correlated errors, while Liu and Okui (2013) and Liu et al. (2013) extended the MMA…”
• Jackknife model averaging for quantile regressions (2015, methodology): “In other words, we pretend that the in-sample and out-sample data are independent, following the same strategy taken by Hansen (2008) and Zhang et al. (2013). quantile restriction.”
• INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS (2018, unknown): “…(Hansen, 2007; Wan, Zhang, and Zou, 2010; Liu and Okui, 2013; Zhang, Zou, and Liang, 2014), optimal mean squared error averaging (Liang, Zou, Wan, and Zhang, 2011), jackknife model averaging (Hansen and Racine, 2012; Zhang, Wan, and Zou, 2013; Lu and Su, 2015), and plug-in averaging (Liu, 2015).”
• Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods (2019, unknown): “Instead of using simple average, JMA uses leave-one-out cross-validation to estimate optimal weights; see Hansen and Racine (2012) and Zhang, Wan, and Zou (2013).”
• Model averaging based on leave-subject-out cross-validation (2016, methodology): “Condition 6 is developed from condition (22) of Zhang et al. (2013).”
• Generalized Least Squares Model Averaging (2016, unknown): “Jackknife model averaging is extended to the case of dependent data by Zhang et al. (2013).”