Jackknife model averaging for quantile regressions

By Xun Lu, Liangjun Su. 2015

147 citations recorded by the source corpus.

In this paper we consider model averaging for quantile regressions (QR) when all models under investigation are potentially misspecified and the number of parameters is diverging with the sample size. To allow for the dependence between the error terms and regressors in the QR models, we propose a jackknife model averaging (JMA) estimator which selects the weights by minimizing a leave-one-out cross-validation criterion function and demonstrate its asymptotic optimality in terms of minimizing the out-of-sample final prediction error. We conduct simulations to demonstrate the finite-sample performance of our estimator and compare it with other model selection and averaging methods. We apply our JMA method to forecast quantiles of excess stock returns and wages.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4ac993d4508e23054038d06151b0755b1d3488c5

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. J. Qian
2. Liangjun Su
3. Yiguo Sun
4. Xun Lu
5. Xia Wang
6. Sainan Jin
7. Emir Malikov
8. Peter C. B. Phillips

In Finance

1. J. Qian
2. Liangjun Su
3. Halbert White
4. Tzee-Ming Huang
5. Xun Lu
6. Sessi Tokpavi
7. B. Candelon
8. László Györfi

In Management Science and Operations Research

1. J. Qian
2. Liangjun Su

Citations in the corpus

• Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects (2016, unknown): “Instead, we follow the literature on inferences with a diverging number of parameters (see, e.g., Fan and Peng (2004), Lam and Fan (2008), Lu and Su (2015)) and prove the asymptotic normality for any arbitrary linear combinations of elements of ̂(1) To understand the results in Theorem 3.3, we…”
• A Mallows-Type Model Averaging Estimator for the Varying-Coefficient Partially Linear Model (2018, unknown): “In the model averaging literature, the true model is commonly assumed to be infinite dimensional (Hansen 2007; Lu and Su 2015).”
• SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES (2015, background): “Instead, we follow the literature on inferences with a diverging number of parameters (see, e.g., Fan and Peng (2004), Lam and Fan (2008), Lu and Su (2014)) and prove the asymptotic normality for any arbitrary linear combinations of elements of α̂0 after adapting to different convergence rates for…”
• Time-varying model averaging (2020, unknown): “We employ Campbell and Thompson’s (2008) popular data set, which is used in Chen and Hong (2012), Jin et al. (2014) nd Lu and Su (2015), among many others.”
• 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).”