Nonlinear Modal Regression for Dependent Data with Application for Predicting COVID-19

Aman Ullah, Tao Wang and Weixin Yao ()
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Weixin Yao: UC Riverside

No 202207, Working Papers from University of California at Riverside, Department of Economics

Abstract: In this paper, under the stationary alpha-mixing dependent samples, we develop a novel nonlinear modal regression for time series sequences and establish the consistency and asymptotic property of the proposed nonlinear modal estimator with a shrinking bandwidth under certain regularity conditions. The asymptotic distribution is shown to be identical to the one derived from the independent observations, whereas the convergence rate is slower than that in the nonlinear mean regression. We numerically estimate the proposed nonlinear modal regression model by the use of a modified modal-expectation-maximization (MEM) algorithm in conjunction with Taylor expansion. Monte Carlo simulations are presented to demonstrate the good fi nite sample (prediction) performance of the newly proposed model. We also construct a specified nonlinear modal regression to match the available daily new cases and new deaths data of the COVID-19 outbreak at the state/region level in the United States, and provide forward prediction up to 130 days ahead (from August 24, 2020 to December 31, 2020). In comparison to the traditional nonlinear regressions, the suggested model can fit the COVID-19 data better and produce more precise predictions. The prediction results indicate that there are systematic differences in spreading distributions among states/regions. For most western and eastern states, they have many serious COVID-19 burdens compared to Midwest. We hope that the built nonlinear modal regression can help policymakers to implement fast actions to curb the spread of the infection, avoid overburdening the health system, and understand the development of COVID-19 from some points.

Keywords: COVID-19; Dependent data; MEM algorithm; Modal regression; Nonlinear; Prediction (search for similar items in EconPapers)
JEL-codes: C01 C14 C22 C53 (search for similar items in EconPapers)
Pages: 51 Pages
Date: 2022-02
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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https://economics.ucr.edu/repec/ucr/wpaper/202207.pdf First version, 2022 (application/pdf)

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Journal Article: Nonlinear modal regression for dependent data with application for predicting COVID‐19 (2022)
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