Sensitivity analysis of error-contaminated time series data under autoregressive models with the application of COVID-19 data

  title={Sensitivity analysis of error-contaminated time series data under autoregressive models with the application of COVID-19 data},
  author={Qihuang Zhang and Grace Y. Yi},
  journal={Journal of Applied Statistics},
Autoregressive (AR) models are useful tools in time series analysis. Inferences under such models are distorted in the presence of measurement error, which is very common in practice. In this article, we establish analytical results for quantifying the biases of the parameter estimation in AR models if the measurement error effects are neglected. We propose two measurement error models to describe different processes of data contamination. An estimating equation approach is proposed for the… 

Figures from this paper


Measurement Error in Linear Autoregressive Models
Time series data are often subject to measurement error, usually the result of needing to estimate the variable of interest. Although it is often reasonable to assume that the measurement error is
Regression and time series model selection in small samples
SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small,
Time Series Modelling and Interpretation
The possibility that a complicated looking model could have arisen from a simpler situation is explored and some suggestions made about the possible interpreta- tion of some models that appear to arise in practice.
The measurement error problem that we consider in this paper is concerned with the situation where time series data of various kinds—short memory, long memory, and random walk processes—are
Edgeworth Approximation of a Finite Sample Distribution for an AR(1) Model with Measurement Error
In this paper, we consider the finite sample property of the ordinary least squares (OLS) estimator for an AR(1) model with measurement error. We present the Edgeworth approximation for a finite
Asymptotic properties of estimators for autoregressive models with errors in variables
Let {X t , t ∈ Z} be an observable strictly stationary sequence of random variables and let X t = U t + e t , where {U t } is an AR (p) and {e t } is a strictly stationary sequence representing
Estimation in autoregressive model with measurement error
Consider an autoregressive model with measurement error: we observe $Z_i=X_i+\epsilon_i$, where $X_i$ is a stationary solution of the equation $X_i=f_{\theta^0}(X_{i-1})+\xi_i$. The regression
Introduction to time series and forecasting
A general approach to Time Series Modelling and ModeLLing with ARMA Processes, which describes the development of a Stationary Process in Terms of Infinitely Many Past Values and the Autocorrelation Function.
Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID‐19: Meta‐analysis and sensitivity analysis
The coronavirus disease‐2019 (COVID‐19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). However, comprehensive knowledge of COVID‐19 remains