Functional quantile autoregression

Chaohua Dong, Rong Chen, Zhijie Xiao and Weiyi Liu

Journal of Econometrics, 2024, vol. 244, issue 2

Abstract: This paper proposes a new class of time series models, the functional quantile autoregression (FQAR) models, in which the conditional distribution of the observation at the current time point is affected by its past distributional information, and is expressed as a functional of the past conditional quantile functions. Different from the conventional functional time series models which are based on functionally observed data, the proposed FQAR method studies functional dynamics in traditional time series data. We propose a sieve estimator for the model. Asymptotic properties of the estimators are derived. Numerical investigations are conducted to highlight the proposed method.

Keywords: Distributional dynamics; Functional dependence; GARCH; Quantile autoregression; Sieve estimation (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:244:y:2024:i:2:s0304407624001118

DOI: 10.1016/j.jeconom.2024.105765

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