Unit Root Quantile Autoregression Inference
By Roger W. Koenker, Zhijie Xiao. 2004
520 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/6e02b53bc7492dae3ad9a29eb5cd57e6b4905d11
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
- Muhammad Shahbaz
- Avik Sinha
- Arshian Sharif
- Ilhan Ozturk
- Gazi Salah Uddin
- Victor Troster
- G. Uzuner
- Mantu Kumar Mahalik
In Finance
- Ilhan Ozturk
- Arshian Sharif
- Muhammad Shahbaz
- Avik Sinha
- Victor Troster
- Jun Yang
- O. Baris-Tuzemen
- S. Leng
Citations in the corpus
- Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression (2016, methodology): “In this paper we adopt a novel statistical method proposed by Koenker and Xiao [5], the quantile autoregression (QAR)model, to examine the influence of lagged returns on the current return over the whole spectrum of return distribution.”
- Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from Quantile ARDL approach (2020, background): “) to eliminate possible biased results and obtain robust inference [58].”
- The asymmetric effect of public private partnership investment on transport CO2 emission in China: Evidence from quantile ARDL approach (2020, unknown): “Consequently, quantile unit root methods provide more robust inference and to avoid the biased results (Koenker and Xiao, 2004).”
- Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis (2018, methodology): “Then, we test whether the quantiles of the distribution follow a unit root process using the quantile autoregression unit root test proposed by Koenker and Xiao (2004) and Galvao (2009).”
- Quantile cointegration in the autoregressive distributed-lag modeling framework (2015, methodology): “…are either I(1), mutually cointegrated, or I(0).1 Recently, the literature on quantile time series regression has been rapidly growing, e.g., Koenker and Xiao (2004, 2006).2 Importantly, Xiao (2009) advances a quantile cointegration approach in a static regression and develops the…”