scholarly journals Comparison of estimators of linear time trend in Weibull-distributed low flows

2003 ◽  
Vol 39 (7) ◽  
Author(s):  
Robin T. Clarke
Keyword(s):  
Time Trend ◽  
Linear Time ◽  
Low Flows ◽  
Linear Time Trend

scholarly journals Subsampling Intervals in Autoregressive Models with Linear Time Trend

Econometrica ◽  
2001 ◽  
Vol 69 (5) ◽  
pp. 1283-1314 ◽  
Author(s):  
Joseph P. Romano ◽  
Michael Wolf
Keyword(s):  
Time Trend ◽  
Linear Time ◽  
Autoregressive Models ◽  
Linear Time Trend

scholarly journals Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm

2020 ◽  
Author(s):  
Varun Agiwal ◽  
Jitendra Kumar ◽  
Yau Chun Yip
Keyword(s):  
Time Series ◽  
Time Trend ◽  
Spline Function ◽  
Linear Time ◽  
Linear Trend ◽  
Model Parameters ◽  
Linear Pattern ◽  
Linear Time Trend ◽  
Proposed Model ◽  
Non Linear

A vast majority of the countries is under the economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, which is an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We have considered an autoregressive model with a non-linear time trend component that approximately converted into the linear trend using the spline function. The spline function split the COVID-19 series into different piecewise segments between respective knots and fitted the linear time trend. First, we obtain the number of knots with its locations in the COVID-19 series and then the estimation of the best-fitted model parameters are determined under Bayesian setup. The results advocate that the proposed model/methodology is a useful procedure to convert the non-linear time trend into a linear pattern of newly coronavirus case for various countries in the pandemic situation of COVID-19.

scholarly journals Bayesian Unit Root Test for AR(1) Model with Trend Approximated

2020 ◽  
Vol 8 (2) ◽  
pp. 425-461
Author(s):  
Jitendra Kumar ◽  
Varun Varun ◽  
Dhirendra Kumar ◽  
Anoop Chaturvedi
Keyword(s):  
Unit Root ◽  
Time Trend ◽  
Spline Function ◽  
Linear Time ◽  
Linear Trend ◽  
Unit Root Test ◽  
Posterior Odds ◽  
Linear Time Trend ◽  
Proposed Model ◽  
Non Linear

The objective of present study is to develop a time series model for handling the non-linear trend process using a spline function. Spline function is a piecewise polynomial segment concerning the time component. The main advantage of spline function is the approximation, non linear time trend, but linear time trend between the consecutive join points. A unit root hypothesis is projected to test the non stationarity due to presence of unit root in the proposed model. In the autoregressive model with linear trend, the time trend vanishes under the unit root case. However, when non-linear trend is present and approximated by the linear spline function, through the trend component is absent under the unit root case, but the intercept term makes a shift with r knots. For decision making under the Bayesian perspective, the posterior odds ratio is used for hypothesis testing problems. We have derived the posterior probability for the assumed hypotheses under appropriate prior information. A simulation study and an empirical application are presented to examine the performance of theoretical outcomes.

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